diff --git a/demos/epiplexity-02-ordering-matters/README.md b/demos/epiplexity-02-ordering-matters/README.md
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+# Epiplexity Demo 2: Ordering Matters
+
+This demo operationalizes **Paradox 2** from *From Entropy to Epiplexity* (Finzi et al., 2026):
+
+- Classical claim: information is order-independent.
+- Bounded-observer claim: extracted structure depends on sequence order.
+
+The same deterministic trajectory events are analyzed in three orderings:
+
+1. `forward` (causal order)
+2. `reversed` (time-reversed)
+3. `shuffled` (chunk-randomized)
+
+The observer (LLM-style adapter) induces rules and predicts next actions. Accuracy and coherence diverge by ordering.
+
+## Files
+
+- `trajectory_generator.nlogo`: deterministic trajectory generator (100 ticks, 5 foragers)
+- `trajectory_analysis.py`: ordering analysis harness + CSV + plots
+- `templates/trajectory_analysis.yaml`: rule-induction template
+- `templates/next_step_predict.yaml`: next-step choice template
+- `config.txt`: provider/model config for NetLogo extension and Python API mode
+- `data/trajectory-raw.txt`: trajectory log input
+- `results/trajectory-analysis.csv`: per-event analysis output
+- `tests/test_analysis.py`: validation tests
+
+## NetLogo Generator
+
+Output row format:
+
+```text
+tick,agent_id,xcor,ycor,energy,state,action
+```
+
+Agent dynamics:
+
+- `energy < 30` -> `state = hungry`
+- hungry agents consume if resources exist, else move toward richer neighboring patches
+- high-energy agents trend into resting behavior
+- energy decays by `-1` per tick
+- `energy <= 0` -> `die`
+
+Run in NetLogo:
+
+1. Open `trajectory_generator.nlogo`
+2. Click `run-100`
+3. Confirm `data/trajectory-raw.txt`
+
+## Analysis Harness
+
+Default mode is deterministic `mock`, which is offline-safe and reproducible.
+
+```bash
+cd demos/epiplexity-02-ordering-matters
+python3 trajectory_analysis.py --mode mock --input data/trajectory-raw.txt --output results/trajectory-analysis.csv
+```
+
+Optional real model mode (OpenAI-compatible APIs, incl. Ollama `/v1`):
+
+```bash
+python3 trajectory_analysis.py --mode ollama --config config.txt
+# or
+python3 trajectory_analysis.py --mode openai --config config.txt
+```
+
+## Outputs
+
+- `results/trajectory-analysis.csv` with columns:
+  - `ordering,tick,event_index,agent_id,rule_hypothesis,predicted_action,actual_action,accuracy,coherence,prediction_entropy`
+- `results/plot-accuracy-over-time.svg`
+- `results/plot-hypothesis-coherence.svg`
+- `results/plot-accuracy-summary.svg`
+- `results/summary.txt`
+
+Expected pattern (Paradox 2 signal):
+
+- `forward accuracy` significantly higher than `reversed` and `shuffled`
+- `forward` hypotheses are more stable/coherent
+- `reversed/shuffled` show lower coherence and higher uncertainty
+
+## Tests
+
+```bash
+cd demos/epiplexity-02-ordering-matters
+python3 -m unittest tests/test_analysis.py -v
+```
+
+Tests validate:
+
+- trajectory parsing and ordering construction
+- CSV schema and no missing/NaN-like values
+- prediction choices constrained to valid action set
+- coherence/entropy bounds
+- ordering gap thresholds:
+  - `forward >= 0.70`
+  - `reversed <= 0.50`
+  - `shuffled <= 0.40`
+
+## References
+
+- Paper: `https://arxiv.org/pdf/2601.03220`
+- Related notes:
+  - `From Entropy to Epiplexity (Finzi et al 2026)`
+  - `Epiplexity Paper — NetLogo Demo Concepts (using llm extension).md`
+  - `NetLogo Demo Ideas — Index (LLM extension).md`
+- Repository extension docs:
+  - `docs/API-REFERENCE.md`
+  - `docs/USAGE.md`
+- Related demos:
+  - `demos/color-sharing/`
+  - `demos/emergent-treasure-hunt/`
diff --git a/demos/epiplexity-02-ordering-matters/data/trajectory-raw.txt b/demos/epiplexity-02-ordering-matters/data/trajectory-raw.txt
new file mode 100644
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--- /dev/null
+++ b/demos/epiplexity-02-ordering-matters/data/trajectory-raw.txt
@@ -0,0 +1,500 @@
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+<text x="744" y="76" font-family="Helvetica,Arial" font-size="12">reversed</text>
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+<text x="744" y="96" font-family="Helvetica,Arial" font-size="12">shuffled</text>
+</svg>
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diff --git a/demos/epiplexity-02-ordering-matters/results/summary.txt b/demos/epiplexity-02-ordering-matters/results/summary.txt
new file mode 100644
index 0000000..fb75c71
--- /dev/null
+++ b/demos/epiplexity-02-ordering-matters/results/summary.txt
@@ -0,0 +1,4 @@
+ordering,accuracy,coherence,prediction_entropy
+forward,0.7818,0.5288,0.3763
+reversed,0.3879,0.4518,0.8312
+shuffled,0.2909,0.4442,0.9442
diff --git a/demos/epiplexity-02-ordering-matters/results/trajectory-analysis.csv b/demos/epiplexity-02-ordering-matters/results/trajectory-analysis.csv
new file mode 100644
index 0000000..7c04324
--- /dev/null
+++ b/demos/epiplexity-02-ordering-matters/results/trajectory-analysis.csv
@@ -0,0 +1,1486 @@
+ordering,tick,event_index,agent_id,rule_hypothesis,predicted_action,actual_action,accuracy,coherence,prediction_entropy
+forward,1,0,agent1,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",eat,eat,1,1.0000,0.3126
+forward,1,1,agent2,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",migrate,eat,0,0.7000,0.6632
+forward,1,2,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",migrate,eat,0,0.2900,0.6632
+forward,1,3,agent4,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",eat,eat,1,0.2900,0.3126
+forward,1,4,agent5,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",unknown,eat,0,0.7000,0.6632
+forward,2,5,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",move,eat,0,0.2900,0.6632
+forward,2,6,agent2,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",unknown,rest,0,0.3214,0.6632
+forward,2,7,agent3,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",eat,rest,0,0.7000,0.3126
+forward,2,8,agent4,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",eat,rest,0,0.2692,0.3126
+forward,2,9,agent5,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",migrate,rest,0,0.2692,0.6632
+forward,3,10,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",eat,rest,0,0.3214,0.3126
+forward,3,11,agent2,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,3,12,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",migrate,rest,0,0.2900,0.6632
+forward,3,13,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,3,14,agent5,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,4,15,agent1,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",unknown,rest,0,0.2692,0.6632
+forward,4,16,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.3214,0.3126
+forward,4,17,agent3,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,4,18,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,4,19,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,5,20,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.8000,0.3126
+forward,5,21,agent2,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.3214,0.3126
+forward,5,22,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",die,rest,0,0.3214,0.6632
+forward,5,23,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,5,24,agent5,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,6,25,agent1,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.7000,0.3126
+forward,6,26,agent2,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.8000,0.3126
+forward,6,27,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",eat,rest,0,0.2900,0.6632
+forward,6,28,agent4,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.3214,0.3126
+forward,6,29,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.3214,0.3126
+forward,7,30,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",die,rest,0,0.7000,0.6632
+forward,7,31,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.8000,0.3126
+forward,7,32,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.9000,0.3126
+forward,7,33,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,1.0000,0.3126
+forward,7,34,agent5,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,8,35,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,8,36,agent2,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",migrate,rest,0,0.2900,0.6632
+forward,8,37,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,8,38,agent4,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.3214,0.3126
+forward,8,39,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.3214,0.3126
+forward,9,40,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,9,41,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",migrate,rest,0,0.8000,0.6632
+forward,9,42,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.9000,0.3126
+forward,9,43,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,1.0000,0.3126
+forward,9,44,agent5,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.3214,0.3126
+forward,10,45,agent1,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.7000,0.3126
+forward,10,46,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.3214,0.3126
+forward,10,47,agent3,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",unknown,rest,0,0.2900,0.6632
+forward,10,48,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,10,49,agent5,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,11,50,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,11,51,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,11,52,agent3,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.3214,0.3126
+forward,11,53,agent4,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2692,0.3126
+forward,11,54,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,12,55,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",unknown,rest,0,0.7000,0.6632
+forward,12,56,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.8000,0.3126
+forward,12,57,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.9000,0.3126
+forward,12,58,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,1.0000,0.3126
+forward,12,59,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,1.0000,0.3126
+forward,13,60,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,1.0000,0.3126
+forward,13,61,agent2,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.3214,0.3126
+forward,13,62,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",eat,rest,0,0.3214,0.6632
+forward,13,63,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,13,64,agent5,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",die,rest,0,0.3214,0.6632
+forward,14,65,agent1,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.7000,0.3126
+forward,14,66,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",unknown,rest,0,0.3214,0.6632
+forward,14,67,agent3,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.3214,0.3126
+forward,14,68,agent4,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",move,rest,0,0.2692,0.6632
+forward,14,69,agent5,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",die,rest,0,0.7000,0.6632
+forward,15,70,agent1,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.2692,0.3126
+forward,15,71,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.3214,0.3126
+forward,15,72,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,15,73,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",unknown,rest,0,0.8000,0.6632
+forward,15,74,agent5,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.3214,0.3126
+forward,16,75,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.3214,0.3126
+forward,16,76,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,16,77,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.8000,0.3126
+forward,16,78,agent4,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,16,79,agent5,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",die,rest,0,0.7000,0.6632
+forward,17,80,agent1,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.2692,0.3126
+forward,17,81,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.3214,0.3126
+forward,17,82,agent3,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,17,83,agent4,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.2692,0.3126
+forward,17,84,agent5,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2692,0.3126
+forward,18,85,agent1,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.7000,0.3126
+forward,18,86,agent2,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.8000,0.3126
+forward,18,87,agent3,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.2692,0.3126
+forward,18,88,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",eat,rest,0,0.3214,0.6632
+forward,18,89,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,19,90,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.8000,0.3126
+forward,19,91,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",die,rest,0,0.9000,0.6632
+forward,19,92,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,1.0000,0.3126
+forward,19,93,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,1.0000,0.3126
+forward,19,94,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,1.0000,0.3126
+forward,20,95,agent1,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,20,96,agent2,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.7000,0.3126
+forward,20,97,agent3,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.2692,0.3126
+forward,20,98,agent4,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",eat,rest,0,0.7000,0.6632
+forward,20,99,agent5,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2692,0.3126
+forward,21,100,agent1,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.7000,0.3126
+forward,21,101,agent2,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.8000,0.3126
+forward,21,102,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,21,103,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",move,rest,0,0.7000,0.6632
+forward,21,104,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",die,rest,0,0.8000,0.6632
+forward,22,105,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",migrate,rest,0,0.9000,0.6632
+forward,22,106,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,1.0000,0.3126
+forward,22,107,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,1.0000,0.3126
+forward,22,108,agent4,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.3214,0.3126
+forward,22,109,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.3214,0.3126
+forward,23,110,agent1,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,23,111,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,23,112,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,23,113,agent4,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,23,114,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",die,rest,0,0.2900,0.6632
+forward,24,115,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,24,116,agent2,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,24,117,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",eat,rest,0,0.2900,0.6632
+forward,24,118,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,24,119,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.8000,0.3126
+forward,25,120,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.9000,0.3126
+forward,25,121,agent2,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,25,122,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,25,123,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,25,124,agent5,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",eat,rest,0,0.2900,0.6632
+forward,26,125,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,26,126,agent2,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.3214,0.3126
+forward,26,127,agent3,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2692,0.3126
+forward,26,128,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,26,129,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,27,130,agent1,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,27,131,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,27,132,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,27,133,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",die,rest,0,0.8000,0.6632
+forward,27,134,agent5,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",die,rest,0,0.3214,0.6632
+forward,28,135,agent1,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.7000,0.3126
+forward,28,136,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.3214,0.3126
+forward,28,137,agent3,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.3214,0.3126
+forward,28,138,agent4,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.7000,0.3126
+forward,28,139,agent5,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",migrate,rest,0,0.8000,0.6632
+forward,29,140,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.3214,0.3126
+forward,29,141,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,29,142,agent3,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,29,143,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,29,144,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,30,145,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",move,rest,0,0.8000,0.6632
+forward,30,146,agent2,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",die,rest,0,0.2900,0.6632
+forward,30,147,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,30,148,agent4,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",die,rest,0,0.3214,0.6632
+forward,30,149,agent5,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",die,rest,0,0.2692,0.6632
+forward,31,150,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,31,151,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,31,152,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",unknown,rest,0,0.8000,0.6632
+forward,31,153,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.9000,0.3126
+forward,31,154,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,1.0000,0.3126
+forward,32,155,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,1.0000,0.3126
+forward,32,156,agent2,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,32,157,agent3,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.2692,0.3126
+forward,32,158,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",migrate,rest,0,0.3214,0.6632
+forward,32,159,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,33,160,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.8000,0.3126
+forward,33,161,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.9000,0.3126
+forward,33,162,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,1.0000,0.3126
+forward,33,163,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,1.0000,0.3126
+forward,33,164,agent5,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,34,165,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",move,rest,0,0.2900,0.6632
+forward,34,166,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,34,167,agent3,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,34,168,agent4,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.7000,0.3126
+forward,34,169,agent5,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.8000,0.3126
+forward,35,170,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",eat,eat,1,0.2900,0.3126
+forward,35,171,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",eat,eat,1,0.7000,0.3126
+forward,35,172,agent3,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",eat,eat,1,0.2900,0.3126
+forward,35,173,agent4,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",eat,eat,1,0.7000,0.3126
+forward,35,174,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",eat,eat,1,0.2900,0.3126
+forward,36,175,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",eat,rest,0,0.7000,0.3126
+forward,36,176,agent2,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",eat,rest,0,0.3214,0.3126
+forward,36,177,agent3,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",eat,rest,0,0.7000,0.3126
+forward,36,178,agent4,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",migrate,rest,0,0.2692,0.6632
+forward,36,179,agent5,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",eat,rest,0,0.7000,0.3126
+forward,37,180,agent1,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.2692,0.3126
+forward,37,181,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.3214,0.3126
+forward,37,182,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,37,183,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",migrate,rest,0,0.8000,0.6632
+forward,37,184,agent5,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",migrate,rest,0,0.2900,0.6632
+forward,38,185,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,38,186,agent2,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,38,187,agent3,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.2692,0.3126
+forward,38,188,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",migrate,rest,0,0.3214,0.6632
+forward,38,189,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,39,190,agent1,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,39,191,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,39,192,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,39,193,agent4,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,39,194,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,40,195,agent1,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,40,196,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,40,197,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,40,198,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.8000,0.3126
+forward,40,199,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.9000,0.3126
+forward,41,200,agent1,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.3214,0.3126
+forward,41,201,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.3214,0.3126
+forward,41,202,agent3,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",eat,rest,0,0.2900,0.6632
+forward,41,203,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,41,204,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,42,205,agent1,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,42,206,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,42,207,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,42,208,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.8000,0.3126
+forward,42,209,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.9000,0.3126
+forward,43,210,agent1,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,43,211,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,43,212,agent3,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.3214,0.3126
+forward,43,213,agent4,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2692,0.3126
+forward,43,214,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,44,215,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,44,216,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.8000,0.3126
+forward,44,217,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.9000,0.3126
+forward,44,218,agent4,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",migrate,rest,0,0.2900,0.6632
+forward,44,219,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,45,220,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,45,221,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.8000,0.3126
+forward,45,222,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.9000,0.3126
+forward,45,223,agent4,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,45,224,agent5,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.7000,0.3126
+forward,46,225,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,46,226,agent2,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.3214,0.3126
+forward,46,227,agent3,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.7000,0.3126
+forward,46,228,agent4,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2692,0.3126
+forward,46,229,agent5,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.2692,0.3126
+forward,47,230,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.3214,0.3126
+forward,47,231,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",eat,rest,0,0.7000,0.6632
+forward,47,232,agent3,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,47,233,agent4,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.7000,0.3126
+forward,47,234,agent5,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",eat,rest,0,0.8000,0.6632
+forward,48,235,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,48,236,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,48,237,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",unknown,rest,0,0.8000,0.6632
+forward,48,238,agent4,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,48,239,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",die,rest,0,0.2900,0.6632
+forward,49,240,agent1,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.3214,0.3126
+forward,49,241,agent2,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",die,rest,0,0.7000,0.6632
+forward,49,242,agent3,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2692,0.3126
+forward,49,243,agent4,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.7000,0.3126
+forward,49,244,agent5,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.8000,0.3126
+forward,50,245,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,50,246,agent2,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",migrate,rest,0,0.3214,0.6632
+forward,50,247,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.3214,0.3126
+forward,50,248,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,50,249,agent5,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",migrate,rest,0,0.3214,0.6632
+forward,51,250,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.3214,0.3126
+forward,51,251,agent2,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.3214,0.3126
+forward,51,252,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.3214,0.3126
+forward,51,253,agent4,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,51,254,agent5,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.7000,0.3126
+forward,52,255,agent1,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.2692,0.3126
+forward,52,256,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.3214,0.3126
+forward,52,257,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,52,258,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.8000,0.3126
+forward,52,259,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.9000,0.3126
+forward,53,260,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,1.0000,0.3126
+forward,53,261,agent2,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,53,262,agent3,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.2692,0.3126
+forward,53,263,agent4,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2692,0.3126
+forward,53,264,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,54,265,agent1,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",die,rest,0,0.3214,0.6632
+forward,54,266,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.3214,0.3126
+forward,54,267,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",unknown,rest,0,0.7000,0.6632
+forward,54,268,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.8000,0.3126
+forward,54,269,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",die,rest,0,0.9000,0.6632
+forward,55,270,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",eat,eat,1,1.0000,0.3126
+forward,55,271,agent2,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,eat,0,0.2900,0.6632
+forward,55,272,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",eat,eat,1,0.2900,0.3126
+forward,55,273,agent4,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",eat,eat,1,0.2900,0.3126
+forward,55,274,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",eat,eat,1,0.2900,0.3126
+forward,56,275,agent1,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",eat,rest,0,0.2900,0.3126
+forward,56,276,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",eat,rest,0,0.2900,0.3126
+forward,56,277,agent3,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",eat,rest,0,0.2900,0.3126
+forward,56,278,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",eat,rest,0,0.2900,0.3126
+forward,56,279,agent5,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",eat,rest,0,0.3214,0.3126
+forward,57,280,agent1,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2692,0.3126
+forward,57,281,agent2,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.7000,0.3126
+forward,57,282,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,57,283,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,57,284,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.8000,0.3126
+forward,58,285,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",die,rest,0,0.9000,0.6632
+forward,58,286,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,1.0000,0.3126
+forward,58,287,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,1.0000,0.3126
+forward,58,288,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",eat,rest,0,1.0000,0.6632
+forward,58,289,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,1.0000,0.3126
+forward,59,290,agent1,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",migrate,rest,0,0.2900,0.6632
+forward,59,291,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,59,292,agent3,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,59,293,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,59,294,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,60,295,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.8000,0.3126
+forward,60,296,agent2,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.3214,0.3126
+forward,60,297,agent3,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2692,0.3126
+forward,60,298,agent4,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.7000,0.3126
+forward,60,299,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,61,300,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,61,301,agent2,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",die,rest,0,0.3214,0.6632
+forward,61,302,agent3,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",eat,rest,0,0.7000,0.6632
+forward,61,303,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.3214,0.3126
+forward,61,304,agent5,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,62,305,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,62,306,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,62,307,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.8000,0.3126
+forward,62,308,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.9000,0.3126
+forward,62,309,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,1.0000,0.3126
+forward,63,310,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",die,rest,0,1.0000,0.6632
+forward,63,311,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,1.0000,0.3126
+forward,63,312,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,1.0000,0.3126
+forward,63,313,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,1.0000,0.3126
+forward,63,314,agent5,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,64,315,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,64,316,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,64,317,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.8000,0.3126
+forward,64,318,agent4,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,64,319,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,65,320,agent1,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,65,321,agent2,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.2692,0.3126
+forward,65,322,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",unknown,rest,0,0.3214,0.6632
+forward,65,323,agent4,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,65,324,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,66,325,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,66,326,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.8000,0.3126
+forward,66,327,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.9000,0.3126
+forward,66,328,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",die,rest,0,1.0000,0.6632
+forward,66,329,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",move,rest,0,1.0000,0.6632
+forward,67,330,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,1.0000,0.3126
+forward,67,331,agent2,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,67,332,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,67,333,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,67,334,agent5,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,68,335,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,68,336,agent2,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,68,337,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,68,338,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,68,339,agent5,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,69,340,agent1,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.7000,0.3126
+forward,69,341,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,69,342,agent3,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,69,343,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,69,344,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,70,345,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.8000,0.3126
+forward,70,346,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.9000,0.3126
+forward,70,347,agent3,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,70,348,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,70,349,agent5,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",migrate,rest,0,0.2900,0.6632
+forward,71,350,agent1,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.2692,0.3126
+forward,71,351,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.3214,0.3126
+forward,71,352,agent3,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,71,353,agent4,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.2692,0.3126
+forward,71,354,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.3214,0.3126
+forward,72,355,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,72,356,agent2,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",unknown,rest,0,0.3214,0.6632
+forward,72,357,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",eat,rest,0,0.3214,0.6632
+forward,72,358,agent4,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.3214,0.3126
+forward,72,359,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.3214,0.3126
+forward,73,360,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",migrate,rest,0,0.7000,0.6632
+forward,73,361,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.8000,0.3126
+forward,73,362,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.9000,0.3126
+forward,73,363,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,1.0000,0.3126
+forward,73,364,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,1.0000,0.3126
+forward,74,365,agent1,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.3214,0.3126
+forward,74,366,agent2,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.7000,0.3126
+forward,74,367,agent3,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.8000,0.3126
+forward,74,368,agent4,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2692,0.3126
+forward,74,369,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,75,370,agent1,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",eat,eat,1,0.3214,0.3126
+forward,75,371,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",eat,eat,1,0.3214,0.3126
+forward,75,372,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",eat,eat,1,0.7000,0.3126
+forward,75,373,agent4,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",move,eat,0,0.3214,0.6632
+forward,75,374,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",eat,eat,1,0.3214,0.3126
+forward,76,375,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",eat,rest,0,0.7000,0.3126
+forward,76,376,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",eat,rest,0,0.8000,0.3126
+forward,76,377,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.9000,0.6632
+forward,76,378,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",eat,rest,0,1.0000,0.3126
+forward,76,379,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,1.0000,0.6632
+forward,77,380,agent1,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",migrate,rest,0,0.2900,0.6632
+forward,77,381,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,77,382,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,77,383,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.8000,0.3126
+forward,77,384,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.9000,0.3126
+forward,78,385,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,1.0000,0.3126
+forward,78,386,agent2,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,78,387,agent3,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.2692,0.3126
+forward,78,388,agent4,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",unknown,rest,0,0.2692,0.6632
+forward,78,389,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,79,390,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",unknown,rest,0,0.7000,0.6632
+forward,79,391,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.8000,0.3126
+forward,79,392,agent3,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.3214,0.3126
+forward,79,393,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.3214,0.3126
+forward,79,394,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,80,395,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.8000,0.3126
+forward,80,396,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.9000,0.3126
+forward,80,397,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,1.0000,0.3126
+forward,80,398,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,1.0000,0.3126
+forward,80,399,agent5,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.3214,0.3126
+forward,81,400,agent1,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2692,0.3126
+forward,81,401,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,81,402,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,81,403,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.8000,0.3126
+forward,81,404,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",move,rest,0,0.9000,0.6632
+forward,82,405,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,1.0000,0.3126
+forward,82,406,agent2,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,82,407,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,82,408,agent4,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,82,409,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,83,410,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,83,411,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.8000,0.3126
+forward,83,412,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.9000,0.3126
+forward,83,413,agent4,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",eat,rest,0,0.3214,0.6632
+forward,83,414,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",eat,rest,0,0.3214,0.6632
+forward,84,415,agent1,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,84,416,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,84,417,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,84,418,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.8000,0.3126
+forward,84,419,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.9000,0.3126
+forward,85,420,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,1.0000,0.3126
+forward,85,421,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,1.0000,0.3126
+forward,85,422,agent3,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.3214,0.3126
+forward,85,423,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.3214,0.3126
+forward,85,424,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,86,425,agent1,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.3214,0.3126
+forward,86,426,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.3214,0.3126
+forward,86,427,agent3,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,86,428,agent4,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.2692,0.3126
+forward,86,429,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.3214,0.3126
+forward,87,430,agent1,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.3214,0.3126
+forward,87,431,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",migrate,rest,0,0.3214,0.6632
+forward,87,432,agent3,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,87,433,agent4,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",eat,rest,0,0.2692,0.6632
+forward,87,434,agent5,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2692,0.3126
+forward,88,435,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",die,rest,0,0.2900,0.6632
+forward,88,436,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,88,437,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.8000,0.3126
+forward,88,438,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.9000,0.3126
+forward,88,439,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,1.0000,0.3126
+forward,89,440,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,1.0000,0.3126
+forward,89,441,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,1.0000,0.3126
+forward,89,442,agent3,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.3214,0.3126
+forward,89,443,agent4,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",move,rest,0,0.2692,0.6632
+forward,89,444,agent5,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.2692,0.3126
+forward,90,445,agent1,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.7000,0.3126
+forward,90,446,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.3214,0.3126
+forward,90,447,agent3,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.3214,0.3126
+forward,90,448,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",eat,rest,0,0.3214,0.6632
+forward,90,449,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,91,450,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.8000,0.3126
+forward,91,451,agent2,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.3214,0.3126
+forward,91,452,agent3,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",migrate,rest,0,0.7000,0.6632
+forward,91,453,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.3214,0.3126
+forward,91,454,agent5,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.3214,0.3126
+forward,92,455,agent1,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.7000,0.3126
+forward,92,456,agent2,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2692,0.3126
+forward,92,457,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,92,458,agent4,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,92,459,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",die,rest,0,0.2900,0.6632
+forward,93,460,agent1,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,93,461,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,93,462,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,93,463,agent4,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.3214,0.3126
+forward,93,464,agent5,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.7000,0.3126
+forward,94,465,agent1,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2692,0.3126
+forward,94,466,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,94,467,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,94,468,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.8000,0.3126
+forward,94,469,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.9000,0.3126
+forward,95,470,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",eat,eat,1,1.0000,0.3126
+forward,95,471,agent2,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",eat,eat,1,0.3214,0.3126
+forward,95,472,agent3,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",eat,eat,1,0.2692,0.3126
+forward,95,473,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",eat,eat,1,0.2900,0.3126
+forward,95,474,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",eat,eat,1,0.7000,0.3126
+forward,96,475,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",eat,rest,0,0.8000,0.3126
+forward,96,476,agent2,"Agents with low energy move toward richer patches, then eat to recover energy.",eat,rest,0,0.9000,0.3126
+forward,96,477,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",eat,rest,0,1.0000,0.3126
+forward,96,478,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",eat,rest,0,1.0000,0.3126
+forward,96,479,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",eat,rest,0,1.0000,0.3126
+forward,97,480,agent1,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",unknown,rest,0,0.2900,0.6632
+forward,97,481,agent2,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.7000,0.3126
+forward,97,482,agent3,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,97,483,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.7000,0.3126
+forward,97,484,agent5,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.8000,0.3126
+forward,98,485,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.9000,0.3126
+forward,98,486,agent2,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",eat,rest,0,0.3214,0.6632
+forward,98,487,agent3,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2692,0.3126
+forward,98,488,agent4,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.7000,0.3126
+forward,98,489,agent5,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",die,rest,0,0.8000,0.6632
+forward,99,490,agent1,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,99,491,agent2,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.2900,0.3126
+forward,99,492,agent3,"Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",rest,rest,1,0.7000,0.3126
+forward,99,493,agent4,"Agents with low energy move toward richer patches, then eat to recover energy.",rest,rest,1,0.2900,0.3126
+forward,99,494,agent5,"The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",rest,rest,1,0.3214,0.3126
+reversed,100,0,agent5,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",die,rest,0,1.0000,0.8896
+reversed,100,1,agent4,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",unknown,rest,0,0.7000,0.8896
+reversed,100,2,agent3,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",move,rest,0,0.8000,0.8896
+reversed,100,3,agent2,The sequence suggests weak structure because outcomes precede the states that explain them.,unknown,rest,0,0.2500,0.8896
+reversed,100,4,agent1,The sequence suggests weak structure because outcomes precede the states that explain them.,die,rest,0,0.7000,0.8896
+reversed,99,5,agent5,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",migrate,rest,0,0.2500,0.8896
+reversed,99,6,agent4,The sequence suggests weak structure because outcomes precede the states that explain them.,migrate,rest,0,0.2500,0.8896
+reversed,99,7,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,99,8,agent2,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",die,rest,0,0.2500,0.8896
+reversed,99,9,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,migrate,rest,0,0.2500,0.8896
+reversed,98,10,agent5,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,98,11,agent4,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.7000,0.7569
+reversed,98,12,agent3,The sequence suggests weak structure because outcomes precede the states that explain them.,unknown,rest,0,0.2500,0.8896
+reversed,98,13,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,98,14,agent1,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,97,15,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,unknown,eat,0,0.2500,0.8896
+reversed,97,16,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,eat,0,0.7000,0.7569
+reversed,97,17,agent3,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,eat,0,0.2500,0.7569
+reversed,97,18,agent2,The sequence suggests weak structure because outcomes precede the states that explain them.,unknown,eat,0,0.2500,0.8896
+reversed,97,19,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,die,eat,0,0.2500,0.8896
+reversed,96,20,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.7000,0.8896
+reversed,96,21,agent4,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",eat,rest,0,0.2500,0.7569
+reversed,96,22,agent3,The sequence suggests weak structure because outcomes precede the states that explain them.,eat,rest,0,0.2500,0.7569
+reversed,96,23,agent2,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",move,rest,0,0.2500,0.8896
+reversed,96,24,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.8896
+reversed,95,25,agent5,The sequence suggests weak structure because outcomes precede the states that explain them.,eat,rest,0,0.2500,0.7569
+reversed,95,26,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,move,rest,0,0.2500,0.8896
+reversed,95,27,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,die,rest,0,0.7000,0.8896
+reversed,95,28,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,rest,0,0.8000,0.7569
+reversed,95,29,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,rest,0,0.9000,0.7569
+reversed,94,30,agent5,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,94,31,agent4,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",unknown,rest,0,0.7000,0.8896
+reversed,94,32,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,94,33,agent2,The sequence suggests weak structure because outcomes precede the states that explain them.,migrate,rest,0,0.2500,0.8896
+reversed,94,34,agent1,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,93,35,agent5,The sequence suggests weak structure because outcomes precede the states that explain them.,unknown,rest,0,0.2500,0.8896
+reversed,93,36,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,93,37,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.7000,0.7569
+reversed,93,38,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,migrate,rest,0,0.8000,0.8896
+reversed,93,39,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,rest,0,0.9000,0.8896
+reversed,92,40,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,migrate,rest,0,1.0000,0.8896
+reversed,92,41,agent4,The sequence suggests weak structure because outcomes precede the states that explain them.,unknown,rest,0,0.2500,0.8896
+reversed,92,42,agent3,The sequence suggests weak structure because outcomes precede the states that explain them.,unknown,rest,0,0.7000,0.8896
+reversed,92,43,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,move,rest,0,0.2500,0.8896
+reversed,92,44,agent1,The sequence suggests weak structure because outcomes precede the states that explain them.,unknown,rest,0,0.2500,0.8896
+reversed,91,45,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,die,rest,0,0.2500,0.8896
+reversed,91,46,agent4,The sequence suggests weak structure because outcomes precede the states that explain them.,unknown,rest,0,0.2500,0.8896
+reversed,91,47,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,rest,0,0.2500,0.8896
+reversed,91,48,agent2,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",migrate,rest,0,0.2500,0.8896
+reversed,91,49,agent1,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.2500,0.7569
+reversed,90,50,agent5,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,90,51,agent4,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.7000,0.7569
+reversed,90,52,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,unknown,rest,0,0.2500,0.8896
+reversed,90,53,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,migrate,rest,0,0.7000,0.8896
+reversed,90,54,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,unknown,rest,0,0.8000,0.8896
+reversed,89,55,agent5,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",eat,rest,0,0.2500,0.8896
+reversed,89,56,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,move,rest,0,0.2500,0.8896
+reversed,89,57,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.7000,0.7569
+reversed,89,58,agent2,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.2500,0.7569
+reversed,89,59,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,rest,0,0.2500,0.8896
+reversed,88,60,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.7000,0.7569
+reversed,88,61,agent4,The sequence suggests weak structure because outcomes precede the states that explain them.,die,rest,0,0.2500,0.8896
+reversed,88,62,agent3,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.7000,0.7569
+reversed,88,63,agent2,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",unknown,rest,0,0.2500,0.8896
+reversed,88,64,agent1,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.2500,0.7569
+reversed,87,65,agent5,The sequence suggests weak structure because outcomes precede the states that explain them.,move,rest,0,0.7000,0.8896
+reversed,87,66,agent4,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",die,rest,0,0.2500,0.8896
+reversed,87,67,agent3,The sequence suggests weak structure because outcomes precede the states that explain them.,migrate,rest,0,0.2500,0.8896
+reversed,87,68,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,87,69,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,rest,0,0.7000,0.8896
+reversed,86,70,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,unknown,rest,0,0.8000,0.8896
+reversed,86,71,agent4,The sequence suggests weak structure because outcomes precede the states that explain them.,migrate,rest,0,0.2500,0.8896
+reversed,86,72,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,86,73,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,move,rest,0,0.7000,0.8896
+reversed,86,74,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,move,rest,0,0.8000,0.8896
+reversed,85,75,agent5,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",unknown,rest,0,0.2500,0.8896
+reversed,85,76,agent4,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",die,rest,0,0.7000,0.8896
+reversed,85,77,agent3,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.2500,0.7569
+reversed,85,78,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,85,79,agent1,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.2500,0.7569
+reversed,84,80,agent5,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.7000,0.7569
+reversed,84,81,agent4,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.8000,0.7569
+reversed,84,82,agent3,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",eat,rest,0,0.2500,0.8896
+reversed,84,83,agent2,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.7000,0.7569
+reversed,84,84,agent1,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.2500,0.7569
+reversed,83,85,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,rest,0,0.2500,0.8896
+reversed,83,86,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,unknown,rest,0,0.7000,0.8896
+reversed,83,87,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,move,rest,0,0.8000,0.8896
+reversed,83,88,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,die,rest,0,0.9000,0.8896
+reversed,83,89,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,1.0000,0.7569
+reversed,82,90,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,1.0000,0.7569
+reversed,82,91,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,die,rest,0,1.0000,0.8896
+reversed,82,92,agent3,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,82,93,agent2,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.2500,0.7569
+reversed,82,94,agent1,The sequence suggests weak structure because outcomes precede the states that explain them.,die,rest,0,0.7000,0.8896
+reversed,81,95,agent5,The sequence suggests weak structure because outcomes precede the states that explain them.,eat,rest,0,0.8000,0.8896
+reversed,81,96,agent4,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",eat,rest,0,0.2500,0.8896
+reversed,81,97,agent3,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.7000,0.7569
+reversed,81,98,agent2,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",unknown,rest,0,0.8000,0.8896
+reversed,81,99,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,80,100,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,move,rest,0,0.7000,0.8896
+reversed,80,101,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,die,rest,0,0.8000,0.8896
+reversed,80,102,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,migrate,rest,0,0.9000,0.8896
+reversed,80,103,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,unknown,rest,0,1.0000,0.8896
+reversed,80,104,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,1.0000,0.7569
+reversed,79,105,agent5,The sequence suggests weak structure because outcomes precede the states that explain them.,move,rest,0,0.2500,0.8896
+reversed,79,106,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,die,rest,0,0.2500,0.8896
+reversed,79,107,agent3,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,79,108,agent2,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.7000,0.7569
+reversed,79,109,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,move,rest,0,0.2500,0.8896
+reversed,78,110,agent5,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.2500,0.7569
+reversed,78,111,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,die,rest,0,0.2500,0.8896
+reversed,78,112,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,die,rest,0,0.7000,0.8896
+reversed,78,113,agent2,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",eat,rest,0,0.2500,0.8896
+reversed,78,114,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,rest,0,0.2500,0.8896
+reversed,77,115,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,move,eat,0,0.7000,0.8896
+reversed,77,116,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,eat,0,0.8000,0.7569
+reversed,77,117,agent3,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,eat,0,0.2500,0.7569
+reversed,77,118,agent2,The sequence suggests weak structure because outcomes precede the states that explain them.,migrate,eat,0,0.2500,0.8896
+reversed,77,119,agent1,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",die,eat,0,0.2500,0.8896
+reversed,76,120,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,rest,0,0.2500,0.7569
+reversed,76,121,agent4,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.8896
+reversed,76,122,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,migrate,rest,0,0.2500,0.8896
+reversed,76,123,agent2,The sequence suggests weak structure because outcomes precede the states that explain them.,eat,rest,0,0.2500,0.7569
+reversed,76,124,agent1,The sequence suggests weak structure because outcomes precede the states that explain them.,die,rest,0,0.7000,0.8896
+reversed,75,125,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,rest,0,0.2500,0.7569
+reversed,75,126,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,unknown,rest,0,0.7000,0.8896
+reversed,75,127,agent3,The sequence suggests weak structure because outcomes precede the states that explain them.,unknown,rest,0,0.2500,0.8896
+reversed,75,128,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,rest,0,0.2500,0.7569
+reversed,75,129,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,die,rest,0,0.7000,0.8896
+reversed,74,130,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,die,rest,0,0.8000,0.8896
+reversed,74,131,agent4,The sequence suggests weak structure because outcomes precede the states that explain them.,die,rest,0,0.2500,0.8896
+reversed,74,132,agent3,The sequence suggests weak structure because outcomes precede the states that explain them.,die,rest,0,0.7000,0.8896
+reversed,74,133,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,rest,0,0.2500,0.8896
+reversed,74,134,agent1,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.2500,0.7569
+reversed,73,135,agent5,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.7000,0.7569
+reversed,73,136,agent4,The sequence suggests weak structure because outcomes precede the states that explain them.,migrate,rest,0,0.8000,0.8896
+reversed,73,137,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,migrate,rest,0,0.2500,0.8896
+reversed,73,138,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,rest,0,0.7000,0.8896
+reversed,73,139,agent1,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.2500,0.7569
+reversed,72,140,agent5,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,72,141,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,72,142,agent3,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",move,rest,0,0.2500,0.8896
+reversed,72,143,agent2,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",die,rest,0,0.7000,0.8896
+reversed,72,144,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,71,145,agent5,The sequence suggests weak structure because outcomes precede the states that explain them.,die,rest,0,0.2500,0.8896
+reversed,71,146,agent4,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",die,rest,0,0.2500,0.8896
+reversed,71,147,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,71,148,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,rest,0,0.7000,0.8896
+reversed,71,149,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,unknown,rest,0,0.8000,0.8896
+reversed,70,150,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.9000,0.7569
+reversed,70,151,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,1.0000,0.7569
+reversed,70,152,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,1.0000,0.7569
+reversed,70,153,agent2,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,70,154,agent1,The sequence suggests weak structure because outcomes precede the states that explain them.,migrate,rest,0,0.2500,0.8896
+reversed,69,155,agent5,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",migrate,rest,0,0.2500,0.8896
+reversed,69,156,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,69,157,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,rest,0,0.7000,0.8896
+reversed,69,158,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.8000,0.7569
+reversed,69,159,agent1,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,68,160,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,rest,0,0.2500,0.8896
+reversed,68,161,agent4,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,68,162,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,move,rest,0,0.2500,0.8896
+reversed,68,163,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,rest,0,0.7000,0.8896
+reversed,68,164,agent1,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,67,165,agent5,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.7000,0.7569
+reversed,67,166,agent4,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.8000,0.7569
+reversed,67,167,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,67,168,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.7000,0.7569
+reversed,67,169,agent1,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",unknown,rest,0,0.2500,0.8896
+reversed,66,170,agent5,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",eat,rest,0,0.7000,0.8896
+reversed,66,171,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,66,172,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.7000,0.7569
+reversed,66,173,agent2,The sequence suggests weak structure because outcomes precede the states that explain them.,unknown,rest,0,0.2500,0.8896
+reversed,66,174,agent1,The sequence suggests weak structure because outcomes precede the states that explain them.,move,rest,0,0.7000,0.8896
+reversed,65,175,agent5,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",migrate,rest,0,0.2500,0.8896
+reversed,65,176,agent4,The sequence suggests weak structure because outcomes precede the states that explain them.,move,rest,0,0.2500,0.8896
+reversed,65,177,agent3,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.7000,0.7569
+reversed,65,178,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,65,179,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.7000,0.7569
+reversed,64,180,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,migrate,rest,0,0.8000,0.8896
+reversed,64,181,agent4,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",migrate,rest,0,0.2500,0.8896
+reversed,64,182,agent3,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.7000,0.7569
+reversed,64,183,agent2,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.8000,0.7569
+reversed,64,184,agent1,The sequence suggests weak structure because outcomes precede the states that explain them.,move,rest,0,0.2500,0.8896
+reversed,63,185,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,migrate,rest,0,0.2500,0.8896
+reversed,63,186,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.7000,0.7569
+reversed,63,187,agent3,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,63,188,agent2,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",unknown,rest,0,0.7000,0.8896
+reversed,63,189,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,62,190,agent5,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",unknown,rest,0,0.2500,0.8896
+reversed,62,191,agent4,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.7000,0.7569
+reversed,62,192,agent3,The sequence suggests weak structure because outcomes precede the states that explain them.,unknown,rest,0,0.2500,0.8896
+reversed,62,193,agent2,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,62,194,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,move,rest,0,0.2500,0.8896
+reversed,61,195,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,unknown,rest,0,0.7000,0.8896
+reversed,61,196,agent4,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",move,rest,0,0.2500,0.8896
+reversed,61,197,agent3,The sequence suggests weak structure because outcomes precede the states that explain them.,move,rest,0,0.2500,0.8896
+reversed,61,198,agent2,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",move,rest,0,0.2500,0.8896
+reversed,61,199,agent1,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",eat,rest,0,0.7000,0.8896
+reversed,60,200,agent5,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",die,rest,0,0.8000,0.8896
+reversed,60,201,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,move,rest,0,0.2500,0.8896
+reversed,60,202,agent3,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.2500,0.7569
+reversed,60,203,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,rest,0,0.2500,0.8896
+reversed,60,204,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.7000,0.7569
+reversed,59,205,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.8000,0.7569
+reversed,59,206,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.9000,0.7569
+reversed,59,207,agent3,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,59,208,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,59,209,agent1,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.2500,0.7569
+reversed,58,210,agent5,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.7000,0.7569
+reversed,58,211,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,move,rest,0,0.2500,0.8896
+reversed,58,212,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,die,rest,0,0.7000,0.8896
+reversed,58,213,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.8000,0.7569
+reversed,58,214,agent1,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",migrate,rest,0,0.2500,0.8896
+reversed,57,215,agent5,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",migrate,eat,0,0.7000,0.8896
+reversed,57,216,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,migrate,eat,0,0.2500,0.8896
+reversed,57,217,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,eat,0,0.7000,0.7569
+reversed,57,218,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,eat,0,0.8000,0.7569
+reversed,57,219,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,eat,0,0.9000,0.7569
+reversed,56,220,agent5,The sequence suggests weak structure because outcomes precede the states that explain them.,die,rest,0,0.2500,0.8896
+reversed,56,221,agent4,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",eat,rest,0,0.2500,0.7569
+reversed,56,222,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,rest,0,0.2500,0.7569
+reversed,56,223,agent2,The sequence suggests weak structure because outcomes precede the states that explain them.,eat,rest,0,0.2500,0.7569
+reversed,56,224,agent1,The sequence suggests weak structure because outcomes precede the states that explain them.,eat,rest,0,0.7000,0.7569
+reversed,55,225,agent5,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",eat,rest,0,0.2500,0.7569
+reversed,55,226,agent4,The sequence suggests weak structure because outcomes precede the states that explain them.,eat,rest,0,0.2500,0.7569
+reversed,55,227,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,move,rest,0,0.2500,0.8896
+reversed,55,228,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.7000,0.8896
+reversed,55,229,agent1,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",die,rest,0,0.2500,0.8896
+reversed,54,230,agent5,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.2500,0.7569
+reversed,54,231,agent4,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",eat,rest,0,0.2500,0.8896
+reversed,54,232,agent3,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.7000,0.7569
+reversed,54,233,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,move,rest,0,0.2500,0.8896
+reversed,54,234,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,unknown,rest,0,0.7000,0.8896
+reversed,53,235,agent5,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",unknown,rest,0,0.2500,0.8896
+reversed,53,236,agent4,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",die,rest,0,0.7000,0.8896
+reversed,53,237,agent3,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.2500,0.7569
+reversed,53,238,agent2,The sequence suggests weak structure because outcomes precede the states that explain them.,die,rest,0,0.7000,0.8896
+reversed,53,239,agent1,The sequence suggests weak structure because outcomes precede the states that explain them.,die,rest,0,0.8000,0.8896
+reversed,52,240,agent5,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,52,241,agent4,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",move,rest,0,0.7000,0.8896
+reversed,52,242,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,52,243,agent2,The sequence suggests weak structure because outcomes precede the states that explain them.,migrate,rest,0,0.2500,0.8896
+reversed,52,244,agent1,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,51,245,agent5,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.7000,0.7569
+reversed,51,246,agent4,The sequence suggests weak structure because outcomes precede the states that explain them.,migrate,rest,0,0.2500,0.8896
+reversed,51,247,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,51,248,agent2,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.2500,0.7569
+reversed,51,249,agent1,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,50,250,agent5,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",move,rest,0,0.7000,0.8896
+reversed,50,251,agent4,The sequence suggests weak structure because outcomes precede the states that explain them.,move,rest,0,0.2500,0.8896
+reversed,50,252,agent3,The sequence suggests weak structure because outcomes precede the states that explain them.,unknown,rest,0,0.7000,0.8896
+reversed,50,253,agent2,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,50,254,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,unknown,rest,0,0.2500,0.8896
+reversed,49,255,agent5,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",die,rest,0,0.2500,0.8896
+reversed,49,256,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,49,257,agent3,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,49,258,agent2,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.2500,0.7569
+reversed,49,259,agent1,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.7000,0.7569
+reversed,48,260,agent5,The sequence suggests weak structure because outcomes precede the states that explain them.,migrate,rest,0,0.8000,0.8896
+reversed,48,261,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,48,262,agent3,The sequence suggests weak structure because outcomes precede the states that explain them.,die,rest,0,0.2500,0.8896
+reversed,48,263,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,48,264,agent1,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",unknown,rest,0,0.2500,0.8896
+reversed,47,265,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,47,266,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,die,rest,0,0.7000,0.8896
+reversed,47,267,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.8000,0.7569
+reversed,47,268,agent2,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",eat,rest,0,0.2500,0.8896
+reversed,47,269,agent1,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",die,rest,0,0.7000,0.8896
+reversed,46,270,agent5,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",eat,rest,0,0.8000,0.8896
+reversed,46,271,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,move,rest,0,0.2500,0.8896
+reversed,46,272,agent3,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,46,273,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,46,274,agent1,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,45,275,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,die,rest,0,0.2500,0.8896
+reversed,45,276,agent4,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.2500,0.7569
+reversed,45,277,agent3,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",unknown,rest,0,0.2500,0.8896
+reversed,45,278,agent2,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",eat,rest,0,0.7000,0.8896
+reversed,45,279,agent1,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.8000,0.7569
+reversed,44,280,agent5,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.9000,0.7569
+reversed,44,281,agent4,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,1.0000,0.7569
+reversed,44,282,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,die,rest,0,0.2500,0.8896
+reversed,44,283,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.7000,0.7569
+reversed,44,284,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,migrate,rest,0,0.8000,0.8896
+reversed,43,285,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,rest,0,0.9000,0.8896
+reversed,43,286,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,die,rest,0,1.0000,0.8896
+reversed,43,287,agent3,The sequence suggests weak structure because outcomes precede the states that explain them.,migrate,rest,0,0.2500,0.8896
+reversed,43,288,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,move,rest,0,0.2500,0.8896
+reversed,43,289,agent1,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,42,290,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,die,rest,0,0.2500,0.8896
+reversed,42,291,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,unknown,rest,0,0.7000,0.8896
+reversed,42,292,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,unknown,rest,0,0.8000,0.8896
+reversed,42,293,agent2,The sequence suggests weak structure because outcomes precede the states that explain them.,eat,rest,0,0.2500,0.8896
+reversed,42,294,agent1,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,41,295,agent5,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",unknown,rest,0,0.7000,0.8896
+reversed,41,296,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,rest,0,0.2500,0.8896
+reversed,41,297,agent3,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,41,298,agent2,The sequence suggests weak structure because outcomes precede the states that explain them.,die,rest,0,0.2500,0.8896
+reversed,41,299,agent1,The sequence suggests weak structure because outcomes precede the states that explain them.,eat,rest,0,0.7000,0.8896
+reversed,40,300,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,40,301,agent4,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.2500,0.7569
+reversed,40,302,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,die,rest,0,0.2500,0.8896
+reversed,40,303,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,migrate,rest,0,0.7000,0.8896
+reversed,40,304,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.8000,0.7569
+reversed,39,305,agent5,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,39,306,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,39,307,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,die,rest,0,0.7000,0.8896
+reversed,39,308,agent2,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,39,309,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,38,310,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.7000,0.7569
+reversed,38,311,agent4,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.2500,0.7569
+reversed,38,312,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,38,313,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.7000,0.7569
+reversed,38,314,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,unknown,rest,0,0.8000,0.8896
+reversed,37,315,agent5,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",move,eat,0,0.2500,0.8896
+reversed,37,316,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,eat,1,0.2500,0.8896
+reversed,37,317,agent3,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",die,eat,0,0.2500,0.8896
+reversed,37,318,agent2,The sequence suggests weak structure because outcomes precede the states that explain them.,move,eat,0,0.2500,0.8896
+reversed,37,319,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,unknown,eat,0,0.2500,0.8896
+reversed,36,320,agent5,The sequence suggests weak structure because outcomes precede the states that explain them.,eat,rest,0,0.2500,0.7569
+reversed,36,321,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,move,rest,0,0.2500,0.8896
+reversed,36,322,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,rest,0,0.7000,0.7569
+reversed,36,323,agent2,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",migrate,rest,0,0.2500,0.8896
+reversed,36,324,agent1,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",eat,rest,0,0.7000,0.7569
+reversed,35,325,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,die,rest,0,0.2500,0.8896
+reversed,35,326,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,move,rest,0,0.7000,0.8896
+reversed,35,327,agent3,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",eat,rest,0,0.2500,0.7569
+reversed,35,328,agent2,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",eat,rest,0,0.7000,0.7569
+reversed,35,329,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,rest,0,0.2500,0.7569
+reversed,34,330,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,rest,0,0.7000,0.8896
+reversed,34,331,agent4,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.2500,0.7569
+reversed,34,332,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,move,rest,0,0.2500,0.8896
+reversed,34,333,agent2,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,34,334,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,33,335,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.7000,0.7569
+reversed,33,336,agent4,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,33,337,agent3,The sequence suggests weak structure because outcomes precede the states that explain them.,eat,rest,0,0.2500,0.8896
+reversed,33,338,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,33,339,agent1,The sequence suggests weak structure because outcomes precede the states that explain them.,eat,rest,0,0.2500,0.8896
+reversed,32,340,agent5,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",eat,rest,0,0.2500,0.8896
+reversed,32,341,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,32,342,agent3,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,32,343,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,migrate,rest,0,0.2500,0.8896
+reversed,32,344,agent1,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.2500,0.7569
+reversed,31,345,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,die,rest,0,0.2500,0.8896
+reversed,31,346,agent4,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",migrate,rest,0,0.2500,0.8896
+reversed,31,347,agent3,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.2500,0.7569
+reversed,31,348,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,rest,0,0.2500,0.8896
+reversed,31,349,agent1,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,30,350,agent5,The sequence suggests weak structure because outcomes precede the states that explain them.,move,rest,0,0.2500,0.8896
+reversed,30,351,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,30,352,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,die,rest,0,0.7000,0.8896
+reversed,30,353,agent2,The sequence suggests weak structure because outcomes precede the states that explain them.,unknown,rest,0,0.2500,0.8896
+reversed,30,354,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,rest,0,0.2500,0.8896
+reversed,29,355,agent5,The sequence suggests weak structure because outcomes precede the states that explain them.,eat,rest,0,0.2500,0.8896
+reversed,29,356,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,rest,0,0.2500,0.8896
+reversed,29,357,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,migrate,rest,0,0.7000,0.8896
+reversed,29,358,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,migrate,rest,0,0.8000,0.8896
+reversed,29,359,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,migrate,rest,0,0.9000,0.8896
+reversed,28,360,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,1.0000,0.7569
+reversed,28,361,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,die,rest,0,1.0000,0.8896
+reversed,28,362,agent3,The sequence suggests weak structure because outcomes precede the states that explain them.,eat,rest,0,0.2500,0.8896
+reversed,28,363,agent2,The sequence suggests weak structure because outcomes precede the states that explain them.,die,rest,0,0.7000,0.8896
+reversed,28,364,agent1,The sequence suggests weak structure because outcomes precede the states that explain them.,migrate,rest,0,0.8000,0.8896
+reversed,27,365,agent5,The sequence suggests weak structure because outcomes precede the states that explain them.,migrate,rest,0,0.9000,0.8896
+reversed,27,366,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,27,367,agent3,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.2500,0.7569
+reversed,27,368,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,unknown,rest,0,0.2500,0.8896
+reversed,27,369,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,die,rest,0,0.7000,0.8896
+reversed,26,370,agent5,The sequence suggests weak structure because outcomes precede the states that explain them.,move,rest,0,0.2500,0.8896
+reversed,26,371,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,rest,0,0.2500,0.8896
+reversed,26,372,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,unknown,rest,0,0.7000,0.8896
+reversed,26,373,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.8000,0.7569
+reversed,26,374,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,rest,0,0.9000,0.8896
+reversed,25,375,agent5,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,25,376,agent4,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",eat,rest,0,0.7000,0.8896
+reversed,25,377,agent3,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",migrate,rest,0,0.8000,0.8896
+reversed,25,378,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,rest,0,0.2500,0.8896
+reversed,25,379,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,unknown,rest,0,0.7000,0.8896
+reversed,24,380,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.8000,0.7569
+reversed,24,381,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,move,rest,0,0.9000,0.8896
+reversed,24,382,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,1.0000,0.7569
+reversed,24,383,agent2,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",die,rest,0,0.2500,0.8896
+reversed,24,384,agent1,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.2500,0.7569
+reversed,23,385,agent5,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",unknown,rest,0,0.2500,0.8896
+reversed,23,386,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,23,387,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,unknown,rest,0,0.7000,0.8896
+reversed,23,388,agent2,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,23,389,agent1,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.2500,0.7569
+reversed,22,390,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,move,rest,0,0.2500,0.8896
+reversed,22,391,agent4,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,22,392,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,move,rest,0,0.2500,0.8896
+reversed,22,393,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,unknown,rest,0,0.7000,0.8896
+reversed,22,394,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.8000,0.7569
+reversed,21,395,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.9000,0.7569
+reversed,21,396,agent4,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.2500,0.7569
+reversed,21,397,agent3,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,21,398,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,die,rest,0,0.2500,0.8896
+reversed,21,399,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.7000,0.7569
+reversed,20,400,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,unknown,rest,0,0.8000,0.8896
+reversed,20,401,agent4,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",move,rest,0,0.2500,0.8896
+reversed,20,402,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,20,403,agent2,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",eat,rest,0,0.2500,0.8896
+reversed,20,404,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,19,405,agent5,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",eat,rest,0,0.2500,0.8896
+reversed,19,406,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,migrate,rest,0,0.2500,0.8896
+reversed,19,407,agent3,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",unknown,rest,0,0.2500,0.8896
+reversed,19,408,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,19,409,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,rest,0,0.7000,0.8896
+reversed,18,410,agent5,The sequence suggests weak structure because outcomes precede the states that explain them.,eat,rest,0,0.2500,0.8896
+reversed,18,411,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,rest,0,0.2500,0.8896
+reversed,18,412,agent3,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",migrate,rest,0,0.2500,0.8896
+reversed,18,413,agent2,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.7000,0.7569
+reversed,18,414,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,17,415,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.7000,0.7569
+reversed,17,416,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,migrate,rest,0,0.8000,0.8896
+reversed,17,417,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.9000,0.7569
+reversed,17,418,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,unknown,rest,0,1.0000,0.8896
+reversed,17,419,agent1,The sequence suggests weak structure because outcomes precede the states that explain them.,die,rest,0,0.2500,0.8896
+reversed,16,420,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,move,rest,0,0.2500,0.8896
+reversed,16,421,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,die,rest,0,0.7000,0.8896
+reversed,16,422,agent3,The sequence suggests weak structure because outcomes precede the states that explain them.,eat,rest,0,0.2500,0.8896
+reversed,16,423,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,rest,0,0.2500,0.8896
+reversed,16,424,agent1,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.2500,0.7569
+reversed,15,425,agent5,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.7000,0.7569
+reversed,15,426,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,15,427,agent3,The sequence suggests weak structure because outcomes precede the states that explain them.,move,rest,0,0.2500,0.8896
+reversed,15,428,agent2,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",migrate,rest,0,0.2500,0.8896
+reversed,15,429,agent1,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.7000,0.7569
+reversed,14,430,agent5,The sequence suggests weak structure because outcomes precede the states that explain them.,eat,rest,0,0.2500,0.8896
+reversed,14,431,agent4,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",eat,rest,0,0.2500,0.8896
+reversed,14,432,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,die,rest,0,0.2500,0.8896
+reversed,14,433,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,unknown,rest,0,0.7000,0.8896
+reversed,14,434,agent1,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,13,435,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,13,436,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.7000,0.7569
+reversed,13,437,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,die,rest,0,0.8000,0.8896
+reversed,13,438,agent2,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.2500,0.7569
+reversed,13,439,agent1,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.2500,0.7569
+reversed,12,440,agent5,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",move,rest,0,0.2500,0.8896
+reversed,12,441,agent4,The sequence suggests weak structure because outcomes precede the states that explain them.,unknown,rest,0,0.2500,0.8896
+reversed,12,442,agent3,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.7000,0.7569
+reversed,12,443,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,migrate,rest,0,0.2500,0.8896
+reversed,12,444,agent1,The sequence suggests weak structure because outcomes precede the states that explain them.,unknown,rest,0,0.2500,0.8896
+reversed,11,445,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,rest,0,0.2500,0.8896
+reversed,11,446,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,die,rest,0,0.7000,0.8896
+reversed,11,447,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.8000,0.7569
+reversed,11,448,agent2,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.2500,0.7569
+reversed,11,449,agent1,The sequence suggests weak structure because outcomes precede the states that explain them.,migrate,rest,0,0.7000,0.8896
+reversed,10,450,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,die,rest,0,0.2500,0.8896
+reversed,10,451,agent4,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.2500,0.7569
+reversed,10,452,agent3,The sequence suggests weak structure because outcomes precede the states that explain them.,migrate,rest,0,0.7000,0.8896
+reversed,10,453,agent2,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",die,rest,0,0.2500,0.8896
+reversed,10,454,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,unknown,rest,0,0.2500,0.8896
+reversed,9,455,agent5,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",die,rest,0,0.2500,0.8896
+reversed,9,456,agent4,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",die,rest,0,0.7000,0.8896
+reversed,9,457,agent3,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.8000,0.7569
+reversed,9,458,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,9,459,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.7000,0.7569
+reversed,8,460,agent5,The sequence suggests weak structure because outcomes precede the states that explain them.,unknown,rest,0,0.2500,0.8896
+reversed,8,461,agent4,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,rest,1,0.7000,0.7569
+reversed,8,462,agent3,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",migrate,rest,0,0.2500,0.8896
+reversed,8,463,agent2,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.7000,0.7569
+reversed,8,464,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,7,465,agent5,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",migrate,rest,0,0.2500,0.8896
+reversed,7,466,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,7,467,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.7000,0.7569
+reversed,7,468,agent2,The sequence suggests weak structure because outcomes precede the states that explain them.,migrate,rest,0,0.2500,0.8896
+reversed,7,469,agent1,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",die,rest,0,0.2500,0.8896
+reversed,6,470,agent5,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.7000,0.7569
+reversed,6,471,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,move,rest,0,0.2500,0.8896
+reversed,6,472,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.7000,0.7569
+reversed,6,473,agent2,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",move,rest,0,0.2500,0.8896
+reversed,6,474,agent1,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,move,rest,0,0.2500,0.8896
+reversed,5,475,agent5,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",unknown,rest,0,0.2500,0.8896
+reversed,5,476,agent4,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.7000,0.7569
+reversed,5,477,agent3,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",unknown,rest,0,0.8000,0.8896
+reversed,5,478,agent2,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,0.9000,0.7569
+reversed,5,479,agent1,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,rest,1,1.0000,0.7569
+reversed,4,480,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.2500,0.7569
+reversed,4,481,agent4,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,rest,1,0.7000,0.7569
+reversed,4,482,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,move,rest,0,0.8000,0.8896
+reversed,4,483,agent2,The sequence suggests weak structure because outcomes precede the states that explain them.,eat,rest,0,0.2500,0.8896
+reversed,4,484,agent1,The sequence suggests weak structure because outcomes precede the states that explain them.,rest,eat,0,0.7000,0.7569
+reversed,3,485,agent5,The sequence suggests weak structure because outcomes precede the states that explain them.,unknown,eat,0,0.8000,0.8896
+reversed,3,486,agent4,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",die,eat,0,0.2500,0.8896
+reversed,3,487,agent3,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,rest,eat,0,0.2500,0.7569
+reversed,3,488,agent2,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,eat,0,0.2500,0.7569
+reversed,3,489,agent1,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",eat,eat,1,0.7000,0.7569
+reversed,2,490,agent5,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,eat,1,0.2500,0.7569
+reversed,2,491,agent4,The sequence suggests weak structure because outcomes precede the states that explain them.,migrate,eat,0,0.2500,0.8896
+reversed,2,492,agent3,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",eat,eat,1,0.2500,0.7569
+reversed,2,493,agent2,Events look consequence-first; causes are ambiguous and state transitions are harder to align.,eat,eat,1,0.2500,0.7569
+reversed,2,494,agent1,"Reverse ordering obscures policy rules, so action triggers appear inconsistent.",rest,move,0,0.2500,0.8896
+shuffled,12,0,agent2,"The trajectory appears incoherent, so hypotheses remain tentative.",die,rest,0,1.0000,0.9630
+shuffled,12,1,agent3,No consistent transition pattern emerges from the random ordering.,unknown,rest,0,0.2812,0.9630
+shuffled,12,2,agent4,"The trajectory appears incoherent, so hypotheses remain tentative.",move,rest,0,0.2812,0.9630
+shuffled,12,3,agent5,No consistent transition pattern emerges from the random ordering.,unknown,rest,0,0.2812,0.9630
+shuffled,49,4,agent5,No consistent transition pattern emerges from the random ordering.,die,rest,0,0.7000,0.9630
+shuffled,50,5,agent1,No consistent transition pattern emerges from the random ordering.,migrate,rest,0,0.8000,0.9630
+shuffled,50,6,agent2,No consistent transition pattern emerges from the random ordering.,migrate,rest,0,0.9000,0.9630
+shuffled,50,7,agent3,Shuffled fragments do not expose a stable causal rule.,migrate,rest,0,0.2500,0.9630
+shuffled,50,8,agent4,No consistent transition pattern emerges from the random ordering.,die,rest,0,0.2500,0.9630
+shuffled,50,9,agent5,Shuffled fragments do not expose a stable causal rule.,migrate,rest,0,0.2500,0.9630
+shuffled,51,10,agent1,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.2500,0.9007
+shuffled,51,11,agent2,"The trajectory appears incoherent, so hypotheses remain tentative.",migrate,rest,0,0.7000,0.9630
+shuffled,26,12,agent4,No consistent transition pattern emerges from the random ordering.,unknown,rest,0,0.2812,0.9630
+shuffled,26,13,agent5,Shuffled fragments do not expose a stable causal rule.,unknown,rest,0,0.2500,0.9630
+shuffled,27,14,agent1,"The trajectory appears incoherent, so hypotheses remain tentative.",move,rest,0,0.2500,0.9630
+shuffled,27,15,agent2,Shuffled fragments do not expose a stable causal rule.,unknown,rest,0,0.2500,0.9630
+shuffled,46,16,agent4,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.7000,0.9007
+shuffled,46,17,agent5,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.2500,0.9007
+shuffled,47,18,agent1,No consistent transition pattern emerges from the random ordering.,unknown,rest,0,0.2812,0.9630
+shuffled,47,19,agent2,Shuffled fragments do not expose a stable causal rule.,die,rest,0,0.2500,0.9630
+shuffled,28,20,agent2,Shuffled fragments do not expose a stable causal rule.,eat,rest,0,0.7000,0.9630
+shuffled,28,21,agent3,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.2500,0.9007
+shuffled,28,22,agent4,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.7000,0.9007
+shuffled,28,23,agent5,No consistent transition pattern emerges from the random ordering.,move,rest,0,0.8000,0.9630
+shuffled,29,24,agent5,Shuffled fragments do not expose a stable causal rule.,eat,rest,0,0.2500,0.9630
+shuffled,30,25,agent1,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.2500,0.9007
+shuffled,30,26,agent2,Shuffled fragments do not expose a stable causal rule.,move,rest,0,0.2500,0.9630
+shuffled,30,27,agent3,Shuffled fragments do not expose a stable causal rule.,die,rest,0,0.7000,0.9630
+shuffled,65,28,agent5,Shuffled fragments do not expose a stable causal rule.,move,rest,0,0.8000,0.9630
+shuffled,66,29,agent1,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.9000,0.9007
+shuffled,66,30,agent2,"The trajectory appears incoherent, so hypotheses remain tentative.",eat,rest,0,0.2500,0.9630
+shuffled,66,31,agent3,"The trajectory appears incoherent, so hypotheses remain tentative.",unknown,eat,0,0.7000,0.9630
+shuffled,94,32,agent4,Shuffled fragments do not expose a stable causal rule.,rest,eat,0,0.2500,0.9007
+shuffled,94,33,agent5,No consistent transition pattern emerges from the random ordering.,migrate,eat,0,0.2500,0.9630
+shuffled,95,34,agent1,"The trajectory appears incoherent, so hypotheses remain tentative.",migrate,rest,0,0.2812,0.9630
+shuffled,95,35,agent2,No consistent transition pattern emerges from the random ordering.,unknown,eat,0,0.2812,0.9630
+shuffled,96,36,agent2,Shuffled fragments do not expose a stable causal rule.,eat,rest,0,0.2500,0.9007
+shuffled,96,37,agent3,Shuffled fragments do not expose a stable causal rule.,eat,rest,0,0.7000,0.9007
+shuffled,96,38,agent4,Shuffled fragments do not expose a stable causal rule.,migrate,rest,0,0.8000,0.9630
+shuffled,96,39,agent5,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.9000,0.9630
+shuffled,6,40,agent4,Shuffled fragments do not expose a stable causal rule.,migrate,rest,0,1.0000,0.9630
+shuffled,6,41,agent5,No consistent transition pattern emerges from the random ordering.,unknown,rest,0,0.2500,0.9630
+shuffled,7,42,agent1,No consistent transition pattern emerges from the random ordering.,unknown,rest,0,0.7000,0.9630
+shuffled,7,43,agent2,Shuffled fragments do not expose a stable causal rule.,move,rest,0,0.2500,0.9630
+shuffled,93,44,agent1,No consistent transition pattern emerges from the random ordering.,unknown,rest,0,0.2500,0.9630
+shuffled,93,45,agent2,"The trajectory appears incoherent, so hypotheses remain tentative.",die,rest,0,0.2812,0.9630
+shuffled,93,46,agent3,No consistent transition pattern emerges from the random ordering.,unknown,rest,0,0.2812,0.9630
+shuffled,93,47,agent4,Shuffled fragments do not expose a stable causal rule.,eat,rest,0,0.2500,0.9630
+shuffled,87,48,agent3,"The trajectory appears incoherent, so hypotheses remain tentative.",migrate,rest,0,0.2500,0.9630
+shuffled,87,49,agent4,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.2812,0.9007
+shuffled,87,50,agent5,No consistent transition pattern emerges from the random ordering.,migrate,rest,0,0.7000,0.9630
+shuffled,88,51,agent1,"The trajectory appears incoherent, so hypotheses remain tentative.",unknown,eat,0,0.2812,0.9630
+shuffled,95,52,agent3,Shuffled fragments do not expose a stable causal rule.,unknown,rest,0,0.2500,0.9630
+shuffled,95,53,agent4,Shuffled fragments do not expose a stable causal rule.,migrate,rest,0,0.7000,0.9630
+shuffled,95,54,agent5,Shuffled fragments do not expose a stable causal rule.,unknown,rest,0,0.8000,0.9630
+shuffled,96,55,agent1,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.2500,0.9630
+shuffled,3,56,agent3,"The trajectory appears incoherent, so hypotheses remain tentative.",move,rest,0,0.7000,0.9630
+shuffled,3,57,agent4,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.2500,0.9007
+shuffled,3,58,agent5,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.2500,0.9007
+shuffled,4,59,agent1,Shuffled fragments do not expose a stable causal rule.,eat,eat,1,0.2500,0.9630
+shuffled,55,60,agent3,"The trajectory appears incoherent, so hypotheses remain tentative.",die,rest,0,0.2500,0.9630
+shuffled,55,61,agent4,No consistent transition pattern emerges from the random ordering.,die,rest,0,0.2812,0.9630
+shuffled,55,62,agent5,No consistent transition pattern emerges from the random ordering.,eat,rest,0,0.7000,0.9007
+shuffled,56,63,agent1,"The trajectory appears incoherent, so hypotheses remain tentative.",unknown,rest,0,0.2812,0.9630
+shuffled,65,64,agent1,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.2812,0.9007
+shuffled,65,65,agent2,No consistent transition pattern emerges from the random ordering.,move,rest,0,0.7000,0.9630
+shuffled,65,66,agent3,No consistent transition pattern emerges from the random ordering.,die,rest,0,0.8000,0.9630
+shuffled,65,67,agent4,No consistent transition pattern emerges from the random ordering.,migrate,rest,0,0.9000,0.9630
+shuffled,79,68,agent3,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.2500,0.9007
+shuffled,79,69,agent4,Shuffled fragments do not expose a stable causal rule.,eat,rest,0,0.7000,0.9630
+shuffled,79,70,agent5,Shuffled fragments do not expose a stable causal rule.,unknown,rest,0,0.8000,0.9630
+shuffled,80,71,agent1,No consistent transition pattern emerges from the random ordering.,migrate,rest,0,0.2500,0.9630
+shuffled,68,72,agent2,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.2500,0.9007
+shuffled,68,73,agent3,Shuffled fragments do not expose a stable causal rule.,move,rest,0,0.7000,0.9630
+shuffled,68,74,agent4,Shuffled fragments do not expose a stable causal rule.,move,rest,0,0.8000,0.9630
+shuffled,68,75,agent5,"The trajectory appears incoherent, so hypotheses remain tentative.",unknown,rest,0,0.2500,0.9630
+shuffled,9,76,agent1,"The trajectory appears incoherent, so hypotheses remain tentative.",die,rest,0,0.7000,0.9630
+shuffled,9,77,agent2,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.2812,0.9007
+shuffled,9,78,agent3,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.2500,0.9007
+shuffled,9,79,agent4,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.2500,0.9007
+shuffled,77,80,agent1,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.7000,0.9007
+shuffled,77,81,agent2,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.8000,0.9007
+shuffled,77,82,agent3,No consistent transition pattern emerges from the random ordering.,eat,rest,0,0.9000,0.9630
+shuffled,77,83,agent4,No consistent transition pattern emerges from the random ordering.,rest,rest,1,1.0000,0.9007
+shuffled,21,84,agent5,No consistent transition pattern emerges from the random ordering.,rest,rest,1,1.0000,0.9007
+shuffled,22,85,agent1,Shuffled fragments do not expose a stable causal rule.,eat,rest,0,0.2500,0.9630
+shuffled,22,86,agent2,Shuffled fragments do not expose a stable causal rule.,unknown,rest,0,0.7000,0.9630
+shuffled,22,87,agent3,Shuffled fragments do not expose a stable causal rule.,move,rest,0,0.8000,0.9630
+shuffled,67,88,agent3,Shuffled fragments do not expose a stable causal rule.,die,rest,0,0.9000,0.9630
+shuffled,67,89,agent4,Shuffled fragments do not expose a stable causal rule.,die,rest,0,1.0000,0.9630
+shuffled,67,90,agent5,Shuffled fragments do not expose a stable causal rule.,rest,eat,0,1.0000,0.9007
+shuffled,68,91,agent1,Shuffled fragments do not expose a stable causal rule.,die,rest,0,1.0000,0.9630
+shuffled,69,92,agent1,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,eat,0,0.2500,0.9007
+shuffled,69,93,agent2,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.2812,0.9007
+shuffled,69,94,agent3,No consistent transition pattern emerges from the random ordering.,die,rest,0,0.7000,0.9630
+shuffled,69,95,agent4,No consistent transition pattern emerges from the random ordering.,eat,eat,1,0.8000,0.9630
+shuffled,2,96,agent4,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.9000,0.9630
+shuffled,2,97,agent5,No consistent transition pattern emerges from the random ordering.,unknown,rest,0,1.0000,0.9630
+shuffled,3,98,agent1,No consistent transition pattern emerges from the random ordering.,unknown,rest,0,1.0000,0.9630
+shuffled,3,99,agent2,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.2500,0.9007
+shuffled,54,100,agent4,Shuffled fragments do not expose a stable causal rule.,move,rest,0,0.7000,0.9630
+shuffled,54,101,agent5,Shuffled fragments do not expose a stable causal rule.,die,rest,0,0.8000,0.9630
+shuffled,55,102,agent1,Shuffled fragments do not expose a stable causal rule.,migrate,rest,0,0.9000,0.9630
+shuffled,55,103,agent2,Shuffled fragments do not expose a stable causal rule.,unknown,rest,0,1.0000,0.9630
+shuffled,41,104,agent1,Shuffled fragments do not expose a stable causal rule.,unknown,rest,0,1.0000,0.9630
+shuffled,41,105,agent2,No consistent transition pattern emerges from the random ordering.,move,rest,0,0.2500,0.9630
+shuffled,41,106,agent3,Shuffled fragments do not expose a stable causal rule.,die,rest,0,0.2500,0.9630
+shuffled,41,107,agent4,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.2500,0.9007
+shuffled,7,108,agent3,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.2812,0.9007
+shuffled,7,109,agent4,"The trajectory appears incoherent, so hypotheses remain tentative.",move,rest,0,0.7000,0.9630
+shuffled,7,110,agent5,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.2812,0.9007
+shuffled,8,111,agent1,Shuffled fragments do not expose a stable causal rule.,die,rest,0,0.2500,0.9630
+shuffled,40,112,agent2,Shuffled fragments do not expose a stable causal rule.,die,rest,0,0.7000,0.9630
+shuffled,40,113,agent3,"The trajectory appears incoherent, so hypotheses remain tentative.",eat,rest,0,0.2500,0.9630
+shuffled,40,114,agent4,Shuffled fragments do not expose a stable causal rule.,eat,rest,0,0.2500,0.9630
+shuffled,40,115,agent5,Shuffled fragments do not expose a stable causal rule.,move,rest,0,0.7000,0.9630
+shuffled,81,116,agent5,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.8000,0.9007
+shuffled,82,117,agent1,"The trajectory appears incoherent, so hypotheses remain tentative.",move,rest,0,0.2500,0.9630
+shuffled,82,118,agent2,No consistent transition pattern emerges from the random ordering.,migrate,rest,0,0.2812,0.9630
+shuffled,82,119,agent3,"The trajectory appears incoherent, so hypotheses remain tentative.",die,rest,0,0.2812,0.9630
+shuffled,45,120,agent5,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.2500,0.9007
+shuffled,46,121,agent1,"The trajectory appears incoherent, so hypotheses remain tentative.",eat,rest,0,0.2500,0.9630
+shuffled,46,122,agent2,Shuffled fragments do not expose a stable causal rule.,migrate,rest,0,0.2500,0.9630
+shuffled,46,123,agent3,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.2500,0.9007
+shuffled,19,124,agent3,No consistent transition pattern emerges from the random ordering.,die,rest,0,0.7000,0.9630
+shuffled,19,125,agent4,Shuffled fragments do not expose a stable causal rule.,move,rest,0,0.2500,0.9630
+shuffled,19,126,agent5,Shuffled fragments do not expose a stable causal rule.,unknown,rest,0,0.7000,0.9630
+shuffled,20,127,agent1,No consistent transition pattern emerges from the random ordering.,unknown,rest,0,0.2500,0.9630
+shuffled,89,128,agent5,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.2500,0.9007
+shuffled,90,129,agent1,"The trajectory appears incoherent, so hypotheses remain tentative.",die,rest,0,0.2500,0.9630
+shuffled,90,130,agent2,Shuffled fragments do not expose a stable causal rule.,die,rest,0,0.2500,0.9630
+shuffled,90,131,agent3,No consistent transition pattern emerges from the random ordering.,die,rest,0,0.2500,0.9630
+shuffled,92,132,agent2,No consistent transition pattern emerges from the random ordering.,die,rest,0,0.7000,0.9630
+shuffled,92,133,agent3,Shuffled fragments do not expose a stable causal rule.,eat,rest,0,0.2500,0.9630
+shuffled,92,134,agent4,No consistent transition pattern emerges from the random ordering.,move,rest,0,0.2500,0.9630
+shuffled,92,135,agent5,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.7000,0.9007
+shuffled,4,136,agent2,No consistent transition pattern emerges from the random ordering.,migrate,rest,0,0.8000,0.9630
+shuffled,4,137,agent3,Shuffled fragments do not expose a stable causal rule.,migrate,rest,0,0.2500,0.9630
+shuffled,4,138,agent4,Shuffled fragments do not expose a stable causal rule.,eat,rest,0,0.7000,0.9630
+shuffled,4,139,agent5,No consistent transition pattern emerges from the random ordering.,die,rest,0,0.2500,0.9630
+shuffled,82,140,agent4,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.2812,0.9007
+shuffled,82,141,agent5,Shuffled fragments do not expose a stable causal rule.,eat,rest,0,0.2500,0.9630
+shuffled,83,142,agent1,"The trajectory appears incoherent, so hypotheses remain tentative.",move,rest,0,0.2500,0.9630
+shuffled,83,143,agent2,"The trajectory appears incoherent, so hypotheses remain tentative.",die,rest,0,0.7000,0.9630
+shuffled,27,144,agent3,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.2500,0.9007
+shuffled,27,145,agent4,No consistent transition pattern emerges from the random ordering.,die,rest,0,0.2500,0.9630
+shuffled,27,146,agent5,No consistent transition pattern emerges from the random ordering.,die,rest,0,0.7000,0.9630
+shuffled,28,147,agent1,Shuffled fragments do not expose a stable causal rule.,unknown,rest,0,0.2500,0.9630
+shuffled,72,148,agent2,Shuffled fragments do not expose a stable causal rule.,eat,rest,0,0.7000,0.9630
+shuffled,72,149,agent3,Shuffled fragments do not expose a stable causal rule.,unknown,rest,0,0.8000,0.9630
+shuffled,72,150,agent4,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.9000,0.9007
+shuffled,72,151,agent5,Shuffled fragments do not expose a stable causal rule.,unknown,rest,0,1.0000,0.9630
+shuffled,18,152,agent4,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,1.0000,0.9007
+shuffled,18,153,agent5,"The trajectory appears incoherent, so hypotheses remain tentative.",migrate,rest,0,0.2500,0.9630
+shuffled,19,154,agent1,No consistent transition pattern emerges from the random ordering.,migrate,rest,0,0.2812,0.9630
+shuffled,19,155,agent2,"The trajectory appears incoherent, so hypotheses remain tentative.",migrate,rest,0,0.2812,0.9630
+shuffled,78,156,agent4,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.2500,0.9007
+shuffled,78,157,agent5,"The trajectory appears incoherent, so hypotheses remain tentative.",eat,rest,0,0.2500,0.9630
+shuffled,79,158,agent1,Shuffled fragments do not expose a stable causal rule.,eat,rest,0,0.2500,0.9630
+shuffled,79,159,agent2,"The trajectory appears incoherent, so hypotheses remain tentative.",migrate,rest,0,0.2500,0.9630
+shuffled,33,160,agent5,Shuffled fragments do not expose a stable causal rule.,eat,rest,0,0.2500,0.9630
+shuffled,34,161,agent1,"The trajectory appears incoherent, so hypotheses remain tentative.",unknown,rest,0,0.2500,0.9630
+shuffled,34,162,agent2,Shuffled fragments do not expose a stable causal rule.,move,rest,0,0.2500,0.9630
+shuffled,34,163,agent3,Shuffled fragments do not expose a stable causal rule.,eat,rest,0,0.7000,0.9630
+shuffled,98,164,agent4,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.2500,0.9007
+shuffled,98,165,agent5,No consistent transition pattern emerges from the random ordering.,unknown,rest,0,0.7000,0.9630
+shuffled,99,166,agent1,No consistent transition pattern emerges from the random ordering.,eat,rest,0,0.8000,0.9630
+shuffled,99,167,agent2,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.2812,0.9007
+shuffled,97,168,agent1,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.2500,0.9007
+shuffled,97,169,agent2,No consistent transition pattern emerges from the random ordering.,unknown,rest,0,0.2500,0.9630
+shuffled,97,170,agent3,"The trajectory appears incoherent, so hypotheses remain tentative.",eat,rest,0,0.2812,0.9630
+shuffled,97,171,agent4,Shuffled fragments do not expose a stable causal rule.,die,rest,0,0.2500,0.9630
+shuffled,39,172,agent3,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.7000,0.9007
+shuffled,39,173,agent4,No consistent transition pattern emerges from the random ordering.,unknown,rest,0,0.2500,0.9630
+shuffled,39,174,agent5,No consistent transition pattern emerges from the random ordering.,move,rest,0,0.7000,0.9630
+shuffled,40,175,agent1,"The trajectory appears incoherent, so hypotheses remain tentative.",migrate,rest,0,0.2812,0.9630
+shuffled,52,176,agent2,No consistent transition pattern emerges from the random ordering.,move,rest,0,0.2812,0.9630
+shuffled,52,177,agent3,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.7000,0.9007
+shuffled,52,178,agent4,"The trajectory appears incoherent, so hypotheses remain tentative.",eat,rest,0,0.2812,0.9630
+shuffled,52,179,agent5,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.2500,0.9007
+shuffled,49,180,agent1,Shuffled fragments do not expose a stable causal rule.,migrate,rest,0,0.7000,0.9630
+shuffled,49,181,agent2,No consistent transition pattern emerges from the random ordering.,migrate,rest,0,0.2500,0.9630
+shuffled,49,182,agent3,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.2812,0.9007
+shuffled,49,183,agent4,No consistent transition pattern emerges from the random ordering.,unknown,rest,0,0.2812,0.9630
+shuffled,74,184,agent4,No consistent transition pattern emerges from the random ordering.,move,rest,0,0.7000,0.9630
+shuffled,74,185,agent5,Shuffled fragments do not expose a stable causal rule.,migrate,rest,0,0.2500,0.9630
+shuffled,75,186,agent1,Shuffled fragments do not expose a stable causal rule.,eat,rest,0,0.7000,0.9007
+shuffled,75,187,agent2,"The trajectory appears incoherent, so hypotheses remain tentative.",eat,rest,0,0.2500,0.9007
+shuffled,23,188,agent3,"The trajectory appears incoherent, so hypotheses remain tentative.",unknown,rest,0,0.7000,0.9630
+shuffled,23,189,agent4,Shuffled fragments do not expose a stable causal rule.,move,rest,0,0.2500,0.9630
+shuffled,23,190,agent5,No consistent transition pattern emerges from the random ordering.,unknown,rest,0,0.2500,0.9630
+shuffled,24,191,agent1,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.2812,0.9007
+shuffled,57,192,agent5,No consistent transition pattern emerges from the random ordering.,unknown,rest,0,0.2812,0.9630
+shuffled,58,193,agent1,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.2812,0.9007
+shuffled,58,194,agent2,Shuffled fragments do not expose a stable causal rule.,move,rest,0,0.2500,0.9630
+shuffled,58,195,agent3,Shuffled fragments do not expose a stable causal rule.,unknown,rest,0,0.7000,0.9630
+shuffled,61,196,agent1,"The trajectory appears incoherent, so hypotheses remain tentative.",move,rest,0,0.2500,0.9630
+shuffled,61,197,agent2,No consistent transition pattern emerges from the random ordering.,move,rest,0,0.2812,0.9630
+shuffled,61,198,agent3,"The trajectory appears incoherent, so hypotheses remain tentative.",move,rest,0,0.2812,0.9630
+shuffled,61,199,agent4,"The trajectory appears incoherent, so hypotheses remain tentative.",eat,rest,0,0.7000,0.9630
+shuffled,16,200,agent2,"The trajectory appears incoherent, so hypotheses remain tentative.",die,rest,0,0.8000,0.9630
+shuffled,16,201,agent3,Shuffled fragments do not expose a stable causal rule.,move,rest,0,0.2500,0.9630
+shuffled,16,202,agent4,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.2500,0.9007
+shuffled,16,203,agent5,Shuffled fragments do not expose a stable causal rule.,eat,rest,0,0.2500,0.9630
+shuffled,33,204,agent1,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.7000,0.9007
+shuffled,33,205,agent2,Shuffled fragments do not expose a stable causal rule.,eat,rest,0,0.8000,0.9630
+shuffled,33,206,agent3,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.9000,0.9007
+shuffled,33,207,agent4,"The trajectory appears incoherent, so hypotheses remain tentative.",move,rest,0,0.2500,0.9630
+shuffled,69,208,agent5,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.2500,0.9007
+shuffled,70,209,agent1,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.2500,0.9007
+shuffled,70,210,agent2,No consistent transition pattern emerges from the random ordering.,unknown,rest,0,0.7000,0.9630
+shuffled,70,211,agent3,Shuffled fragments do not expose a stable causal rule.,move,rest,0,0.2500,0.9630
+shuffled,25,212,agent1,Shuffled fragments do not expose a stable causal rule.,die,rest,0,0.7000,0.9630
+shuffled,25,213,agent2,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.8000,0.9007
+shuffled,25,214,agent3,"The trajectory appears incoherent, so hypotheses remain tentative.",migrate,rest,0,0.2500,0.9630
+shuffled,25,215,agent4,"The trajectory appears incoherent, so hypotheses remain tentative.",migrate,rest,0,0.7000,0.9630
+shuffled,97,216,agent5,"The trajectory appears incoherent, so hypotheses remain tentative.",migrate,rest,0,0.8000,0.9630
+shuffled,98,217,agent1,Shuffled fragments do not expose a stable causal rule.,move,rest,0,0.2500,0.9630
+shuffled,98,218,agent2,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.7000,0.9007
+shuffled,98,219,agent3,Shuffled fragments do not expose a stable causal rule.,migrate,rest,0,0.8000,0.9630
+shuffled,53,220,agent5,No consistent transition pattern emerges from the random ordering.,die,rest,0,0.2500,0.9630
+shuffled,54,221,agent1,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.7000,0.9007
+shuffled,54,222,agent2,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.2500,0.9007
+shuffled,54,223,agent3,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.2500,0.9007
+shuffled,32,224,agent2,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.7000,0.9007
+shuffled,32,225,agent3,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.2812,0.9007
+shuffled,32,226,agent4,No consistent transition pattern emerges from the random ordering.,move,rest,0,0.2812,0.9630
+shuffled,32,227,agent5,Shuffled fragments do not expose a stable causal rule.,move,rest,0,0.2500,0.9630
+shuffled,81,228,agent1,Shuffled fragments do not expose a stable causal rule.,eat,rest,0,0.7000,0.9630
+shuffled,81,229,agent2,"The trajectory appears incoherent, so hypotheses remain tentative.",die,rest,0,0.2500,0.9630
+shuffled,81,230,agent3,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.2812,0.9007
+shuffled,81,231,agent4,"The trajectory appears incoherent, so hypotheses remain tentative.",eat,rest,0,0.2812,0.9630
+shuffled,25,232,agent5,"The trajectory appears incoherent, so hypotheses remain tentative.",die,rest,0,0.7000,0.9630
+shuffled,26,233,agent1,Shuffled fragments do not expose a stable causal rule.,move,rest,0,0.2500,0.9630
+shuffled,26,234,agent2,Shuffled fragments do not expose a stable causal rule.,unknown,rest,0,0.7000,0.9630
+shuffled,26,235,agent3,No consistent transition pattern emerges from the random ordering.,unknown,rest,0,0.2500,0.9630
+shuffled,17,236,agent5,"The trajectory appears incoherent, so hypotheses remain tentative.",die,rest,0,0.2812,0.9630
+shuffled,18,237,agent1,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.2812,0.9007
+shuffled,18,238,agent2,No consistent transition pattern emerges from the random ordering.,die,rest,0,0.7000,0.9630
+shuffled,18,239,agent3,No consistent transition pattern emerges from the random ordering.,die,rest,0,0.8000,0.9630
+shuffled,70,240,agent4,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.9000,0.9007
+shuffled,70,241,agent5,"The trajectory appears incoherent, so hypotheses remain tentative.",move,rest,0,0.2812,0.9630
+shuffled,71,242,agent1,Shuffled fragments do not expose a stable causal rule.,migrate,rest,0,0.2500,0.9630
+shuffled,71,243,agent2,No consistent transition pattern emerges from the random ordering.,migrate,rest,0,0.2500,0.9630
+shuffled,89,244,agent1,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.2812,0.9007
+shuffled,89,245,agent2,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.7000,0.9007
+shuffled,89,246,agent3,No consistent transition pattern emerges from the random ordering.,migrate,rest,0,0.2812,0.9630
+shuffled,89,247,agent4,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.2812,0.9007
+shuffled,91,248,agent3,No consistent transition pattern emerges from the random ordering.,die,rest,0,0.2812,0.9630
+shuffled,91,249,agent4,"The trajectory appears incoherent, so hypotheses remain tentative.",move,rest,0,0.2812,0.9630
+shuffled,91,250,agent5,"The trajectory appears incoherent, so hypotheses remain tentative.",move,rest,0,0.7000,0.9630
+shuffled,92,251,agent1,No consistent transition pattern emerges from the random ordering.,move,rest,0,0.2812,0.9630
+shuffled,53,252,agent1,No consistent transition pattern emerges from the random ordering.,unknown,rest,0,0.7000,0.9630
+shuffled,53,253,agent2,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.2812,0.9007
+shuffled,53,254,agent3,Shuffled fragments do not expose a stable causal rule.,unknown,rest,0,0.2500,0.9630
+shuffled,53,255,agent4,"The trajectory appears incoherent, so hypotheses remain tentative.",die,rest,0,0.2500,0.9630
+shuffled,15,256,agent3,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.2500,0.9007
+shuffled,15,257,agent4,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.2500,0.9007
+shuffled,15,258,agent5,No consistent transition pattern emerges from the random ordering.,unknown,rest,0,0.2812,0.9630
+shuffled,16,259,agent1,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.7000,0.9007
+shuffled,64,260,agent2,No consistent transition pattern emerges from the random ordering.,migrate,rest,0,0.8000,0.9630
+shuffled,64,261,agent3,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.2500,0.9007
+shuffled,64,262,agent4,No consistent transition pattern emerges from the random ordering.,die,eat,0,0.2500,0.9630
+shuffled,64,263,agent5,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.2500,0.9007
+shuffled,5,264,agent5,"The trajectory appears incoherent, so hypotheses remain tentative.",unknown,eat,0,0.2500,0.9630
+shuffled,6,265,agent1,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.2500,0.9007
+shuffled,6,266,agent2,Shuffled fragments do not expose a stable causal rule.,die,eat,0,0.7000,0.9630
+shuffled,6,267,agent3,Shuffled fragments do not expose a stable causal rule.,migrate,eat,0,0.8000,0.9630
+shuffled,56,268,agent2,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.2500,0.9630
+shuffled,56,269,agent3,"The trajectory appears incoherent, so hypotheses remain tentative.",die,rest,0,0.2812,0.9630
+shuffled,56,270,agent4,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.7000,0.9630
+shuffled,56,271,agent5,Shuffled fragments do not expose a stable causal rule.,move,rest,0,0.2500,0.9630
+shuffled,34,272,agent4,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.2500,0.9007
+shuffled,34,273,agent5,Shuffled fragments do not expose a stable causal rule.,move,rest,0,0.2500,0.9630
+shuffled,35,274,agent1,No consistent transition pattern emerges from the random ordering.,die,rest,0,0.2500,0.9630
+shuffled,35,275,agent2,Shuffled fragments do not expose a stable causal rule.,die,rest,0,0.2500,0.9630
+shuffled,100,276,agent2,No consistent transition pattern emerges from the random ordering.,eat,rest,0,0.2500,0.9630
+shuffled,100,277,agent3,No consistent transition pattern emerges from the random ordering.,unknown,rest,0,0.7000,0.9630
+shuffled,100,278,agent4,"The trajectory appears incoherent, so hypotheses remain tentative.",eat,rest,0,0.2812,0.9630
+shuffled,100,279,agent5,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.7000,0.9007
+shuffled,48,280,agent2,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.8000,0.9007
+shuffled,48,281,agent3,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.9000,0.9007
+shuffled,48,282,agent4,Shuffled fragments do not expose a stable causal rule.,die,rest,0,0.2500,0.9630
+shuffled,48,283,agent5,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.7000,0.9007
+shuffled,85,284,agent1,Shuffled fragments do not expose a stable causal rule.,migrate,rest,0,0.8000,0.9630
+shuffled,85,285,agent2,Shuffled fragments do not expose a stable causal rule.,eat,eat,1,0.9000,0.9630
+shuffled,85,286,agent3,Shuffled fragments do not expose a stable causal rule.,die,rest,0,1.0000,0.9630
+shuffled,85,287,agent4,No consistent transition pattern emerges from the random ordering.,migrate,rest,0,0.2500,0.9630
+shuffled,59,288,agent3,Shuffled fragments do not expose a stable causal rule.,move,eat,0,0.2500,0.9630
+shuffled,59,289,agent4,No consistent transition pattern emerges from the random ordering.,move,eat,0,0.2500,0.9630
+shuffled,59,290,agent5,Shuffled fragments do not expose a stable causal rule.,die,eat,0,0.2500,0.9630
+shuffled,60,291,agent1,Shuffled fragments do not expose a stable causal rule.,unknown,eat,0,0.7000,0.9630
+shuffled,1,292,agent5,Shuffled fragments do not expose a stable causal rule.,unknown,rest,0,0.8000,0.9630
+shuffled,2,293,agent1,No consistent transition pattern emerges from the random ordering.,rest,move,0,0.2500,0.9630
+shuffled,2,294,agent2,"The trajectory appears incoherent, so hypotheses remain tentative.",eat,eat,1,0.2812,0.9007
+shuffled,2,295,agent3,No consistent transition pattern emerges from the random ordering.,unknown,eat,0,0.2812,0.9630
+shuffled,1,296,agent1,Shuffled fragments do not expose a stable causal rule.,rest,eat,0,0.2500,0.9630
+shuffled,1,297,agent2,"The trajectory appears incoherent, so hypotheses remain tentative.",eat,rest,0,0.2500,0.9007
+shuffled,1,298,agent3,No consistent transition pattern emerges from the random ordering.,die,rest,0,0.2812,0.9630
+shuffled,1,299,agent4,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.7000,0.9630
+shuffled,35,300,agent3,Shuffled fragments do not expose a stable causal rule.,eat,rest,0,0.2500,0.9007
+shuffled,35,301,agent4,No consistent transition pattern emerges from the random ordering.,move,rest,0,0.2500,0.9630
+shuffled,35,302,agent5,Shuffled fragments do not expose a stable causal rule.,die,rest,0,0.2500,0.9630
+shuffled,36,303,agent1,Shuffled fragments do not expose a stable causal rule.,migrate,rest,0,0.7000,0.9630
+shuffled,62,304,agent4,Shuffled fragments do not expose a stable causal rule.,move,rest,0,0.8000,0.9630
+shuffled,62,305,agent5,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.2500,0.9007
+shuffled,63,306,agent1,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.7000,0.9007
+shuffled,63,307,agent2,Shuffled fragments do not expose a stable causal rule.,die,eat,0,0.2500,0.9630
+shuffled,63,308,agent3,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,eat,0,0.2500,0.9007
+shuffled,63,309,agent4,Shuffled fragments do not expose a stable causal rule.,migrate,eat,0,0.2500,0.9630
+shuffled,63,310,agent5,Shuffled fragments do not expose a stable causal rule.,rest,eat,0,0.7000,0.9007
+shuffled,64,311,agent1,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.2500,0.9007
+shuffled,36,312,agent2,Shuffled fragments do not expose a stable causal rule.,move,rest,0,0.2500,0.9630
+shuffled,36,313,agent3,Shuffled fragments do not expose a stable causal rule.,migrate,rest,0,0.7000,0.9630
+shuffled,36,314,agent4,Shuffled fragments do not expose a stable causal rule.,unknown,rest,0,0.8000,0.9630
+shuffled,36,315,agent5,"The trajectory appears incoherent, so hypotheses remain tentative.",move,rest,0,0.2500,0.9630
+shuffled,77,316,agent5,Shuffled fragments do not expose a stable causal rule.,eat,rest,0,0.2500,0.9630
+shuffled,78,317,agent1,"The trajectory appears incoherent, so hypotheses remain tentative.",die,rest,0,0.2500,0.9630
+shuffled,78,318,agent2,No consistent transition pattern emerges from the random ordering.,move,rest,0,0.2812,0.9630
+shuffled,78,319,agent3,Shuffled fragments do not expose a stable causal rule.,unknown,rest,0,0.2500,0.9630
+shuffled,14,320,agent4,No consistent transition pattern emerges from the random ordering.,migrate,rest,0,0.2500,0.9630
+shuffled,14,321,agent5,Shuffled fragments do not expose a stable causal rule.,move,rest,0,0.2500,0.9630
+shuffled,15,322,agent1,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.7000,0.9007
+shuffled,15,323,agent2,"The trajectory appears incoherent, so hypotheses remain tentative.",migrate,rest,0,0.2500,0.9630
+shuffled,37,324,agent5,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.7000,0.9007
+shuffled,38,325,agent1,"The trajectory appears incoherent, so hypotheses remain tentative.",die,rest,0,0.8000,0.9630
+shuffled,38,326,agent2,Shuffled fragments do not expose a stable causal rule.,move,rest,0,0.2500,0.9630
+shuffled,38,327,agent3,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.2500,0.9007
+shuffled,13,328,agent5,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.2812,0.9007
+shuffled,14,329,agent1,Shuffled fragments do not expose a stable causal rule.,die,rest,0,0.2500,0.9630
+shuffled,14,330,agent2,Shuffled fragments do not expose a stable causal rule.,eat,rest,0,0.7000,0.9630
+shuffled,14,331,agent3,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.2500,0.9007
+shuffled,21,332,agent1,Shuffled fragments do not expose a stable causal rule.,move,rest,0,0.2500,0.9630
+shuffled,21,333,agent2,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.2500,0.9007
+shuffled,21,334,agent3,Shuffled fragments do not expose a stable causal rule.,move,rest,0,0.2500,0.9630
+shuffled,21,335,agent4,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.7000,0.9007
+shuffled,73,336,agent5,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.2500,0.9007
+shuffled,74,337,agent1,No consistent transition pattern emerges from the random ordering.,eat,rest,0,0.2812,0.9630
+shuffled,74,338,agent2,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.2500,0.9007
+shuffled,74,339,agent3,No consistent transition pattern emerges from the random ordering.,eat,rest,0,0.2500,0.9630
+shuffled,45,340,agent1,"The trajectory appears incoherent, so hypotheses remain tentative.",eat,rest,0,0.2812,0.9630
+shuffled,45,341,agent2,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.2500,0.9007
+shuffled,45,342,agent3,"The trajectory appears incoherent, so hypotheses remain tentative.",unknown,rest,0,0.2500,0.9630
+shuffled,45,343,agent4,Shuffled fragments do not expose a stable causal rule.,migrate,rest,0,0.2500,0.9630
+shuffled,17,344,agent1,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.2500,0.9007
+shuffled,17,345,agent2,Shuffled fragments do not expose a stable causal rule.,die,rest,0,0.2500,0.9630
+shuffled,17,346,agent3,"The trajectory appears incoherent, so hypotheses remain tentative.",migrate,rest,0,0.2500,0.9630
+shuffled,17,347,agent4,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.2812,0.9007
+shuffled,30,348,agent4,Shuffled fragments do not expose a stable causal rule.,eat,rest,0,0.2500,0.9630
+shuffled,30,349,agent5,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.2500,0.9007
+shuffled,31,350,agent1,No consistent transition pattern emerges from the random ordering.,move,rest,0,0.7000,0.9630
+shuffled,31,351,agent2,Shuffled fragments do not expose a stable causal rule.,eat,rest,0,0.2500,0.9630
+shuffled,24,352,agent2,Shuffled fragments do not expose a stable causal rule.,die,rest,0,0.7000,0.9630
+shuffled,24,353,agent3,No consistent transition pattern emerges from the random ordering.,unknown,rest,0,0.2500,0.9630
+shuffled,24,354,agent4,Shuffled fragments do not expose a stable causal rule.,eat,rest,0,0.2500,0.9630
+shuffled,24,355,agent5,No consistent transition pattern emerges from the random ordering.,eat,rest,0,0.2500,0.9630
+shuffled,58,356,agent4,Shuffled fragments do not expose a stable causal rule.,eat,rest,0,0.2500,0.9630
+shuffled,58,357,agent5,Shuffled fragments do not expose a stable causal rule.,migrate,rest,0,0.7000,0.9630
+shuffled,59,358,agent1,Shuffled fragments do not expose a stable causal rule.,migrate,rest,0,0.8000,0.9630
+shuffled,59,359,agent2,Shuffled fragments do not expose a stable causal rule.,migrate,rest,0,0.9000,0.9630
+shuffled,10,360,agent4,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,1.0000,0.9007
+shuffled,10,361,agent5,"The trajectory appears incoherent, so hypotheses remain tentative.",die,rest,0,0.2500,0.9630
+shuffled,11,362,agent1,No consistent transition pattern emerges from the random ordering.,eat,rest,0,0.2812,0.9630
+shuffled,11,363,agent2,No consistent transition pattern emerges from the random ordering.,die,rest,0,0.7000,0.9630
+shuffled,8,364,agent2,No consistent transition pattern emerges from the random ordering.,migrate,rest,0,0.8000,0.9630
+shuffled,8,365,agent3,No consistent transition pattern emerges from the random ordering.,migrate,rest,0,0.9000,0.9630
+shuffled,8,366,agent4,Shuffled fragments do not expose a stable causal rule.,eat,rest,0,0.2500,0.9630
+shuffled,8,367,agent5,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.2500,0.9007
+shuffled,93,368,agent5,Shuffled fragments do not expose a stable causal rule.,unknown,rest,0,0.2500,0.9630
+shuffled,94,369,agent1,Shuffled fragments do not expose a stable causal rule.,die,rest,0,0.7000,0.9630
+shuffled,94,370,agent2,No consistent transition pattern emerges from the random ordering.,move,rest,0,0.2500,0.9630
+shuffled,94,371,agent3,Shuffled fragments do not expose a stable causal rule.,eat,rest,0,0.2500,0.9630
+shuffled,47,372,agent3,Shuffled fragments do not expose a stable causal rule.,unknown,rest,0,0.7000,0.9630
+shuffled,47,373,agent4,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.2500,0.9007
+shuffled,47,374,agent5,Shuffled fragments do not expose a stable causal rule.,eat,rest,0,0.2500,0.9630
+shuffled,48,375,agent1,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.2500,0.9007
+shuffled,80,376,agent2,"The trajectory appears incoherent, so hypotheses remain tentative.",eat,rest,0,0.7000,0.9630
+shuffled,80,377,agent3,No consistent transition pattern emerges from the random ordering.,migrate,rest,0,0.2812,0.9630
+shuffled,80,378,agent4,Shuffled fragments do not expose a stable causal rule.,eat,rest,0,0.2500,0.9630
+shuffled,80,379,agent5,Shuffled fragments do not expose a stable causal rule.,unknown,rest,0,0.7000,0.9630
+shuffled,38,380,agent4,Shuffled fragments do not expose a stable causal rule.,migrate,rest,0,0.8000,0.9630
+shuffled,38,381,agent5,Shuffled fragments do not expose a stable causal rule.,move,rest,0,0.9000,0.9630
+shuffled,39,382,agent1,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,1.0000,0.9007
+shuffled,39,383,agent2,No consistent transition pattern emerges from the random ordering.,die,rest,0,0.2500,0.9630
+shuffled,42,384,agent4,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.7000,0.9007
+shuffled,42,385,agent5,"The trajectory appears incoherent, so hypotheses remain tentative.",unknown,rest,0,0.2812,0.9630
+shuffled,43,386,agent1,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.2500,0.9007
+shuffled,43,387,agent2,Shuffled fragments do not expose a stable causal rule.,unknown,rest,0,0.7000,0.9630
+shuffled,37,388,agent1,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.2500,0.9007
+shuffled,37,389,agent2,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.2812,0.9007
+shuffled,37,390,agent3,Shuffled fragments do not expose a stable causal rule.,move,rest,0,0.2500,0.9630
+shuffled,37,391,agent4,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.2500,0.9007
+shuffled,71,392,agent3,Shuffled fragments do not expose a stable causal rule.,move,rest,0,0.2500,0.9630
+shuffled,71,393,agent4,Shuffled fragments do not expose a stable causal rule.,unknown,rest,0,0.7000,0.9630
+shuffled,71,394,agent5,Shuffled fragments do not expose a stable causal rule.,unknown,rest,0,0.8000,0.9630
+shuffled,72,395,agent1,Shuffled fragments do not expose a stable causal rule.,die,rest,0,0.9000,0.9630
+shuffled,57,396,agent1,No consistent transition pattern emerges from the random ordering.,move,rest,0,0.2500,0.9630
+shuffled,57,397,agent2,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.2812,0.9007
+shuffled,57,398,agent3,Shuffled fragments do not expose a stable causal rule.,die,rest,0,0.2500,0.9630
+shuffled,57,399,agent4,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.7000,0.9007
+shuffled,11,400,agent3,Shuffled fragments do not expose a stable causal rule.,unknown,rest,0,0.8000,0.9630
+shuffled,11,401,agent4,"The trajectory appears incoherent, so hypotheses remain tentative.",move,rest,0,0.2500,0.9630
+shuffled,11,402,agent5,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.2500,0.9007
+shuffled,12,403,agent1,"The trajectory appears incoherent, so hypotheses remain tentative.",eat,rest,0,0.2500,0.9630
+shuffled,13,404,agent1,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.2500,0.9007
+shuffled,13,405,agent2,No consistent transition pattern emerges from the random ordering.,eat,rest,0,0.2500,0.9630
+shuffled,13,406,agent3,Shuffled fragments do not expose a stable causal rule.,migrate,rest,0,0.2500,0.9630
+shuffled,13,407,agent4,"The trajectory appears incoherent, so hypotheses remain tentative.",unknown,rest,0,0.2500,0.9630
+shuffled,9,408,agent5,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.2500,0.9007
+shuffled,10,409,agent1,Shuffled fragments do not expose a stable causal rule.,eat,rest,0,0.7000,0.9630
+shuffled,10,410,agent2,No consistent transition pattern emerges from the random ordering.,eat,rest,0,0.2500,0.9630
+shuffled,10,411,agent3,Shuffled fragments do not expose a stable causal rule.,eat,rest,0,0.2500,0.9630
+shuffled,29,412,agent1,"The trajectory appears incoherent, so hypotheses remain tentative.",migrate,rest,0,0.2500,0.9630
+shuffled,29,413,agent2,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.7000,0.9007
+shuffled,29,414,agent3,Shuffled fragments do not expose a stable causal rule.,rest,eat,0,0.2500,0.9007
+shuffled,29,415,agent4,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.2500,0.9007
+shuffled,90,416,agent4,Shuffled fragments do not expose a stable causal rule.,migrate,eat,0,0.2500,0.9630
+shuffled,90,417,agent5,Shuffled fragments do not expose a stable causal rule.,rest,eat,0,0.7000,0.9007
+shuffled,91,418,agent1,"The trajectory appears incoherent, so hypotheses remain tentative.",unknown,rest,0,0.2500,0.9630
+shuffled,91,419,agent2,No consistent transition pattern emerges from the random ordering.,die,eat,0,0.2812,0.9630
+shuffled,76,420,agent2,"The trajectory appears incoherent, so hypotheses remain tentative.",move,rest,0,0.2812,0.9630
+shuffled,76,421,agent3,"The trajectory appears incoherent, so hypotheses remain tentative.",die,rest,0,0.7000,0.9630
+shuffled,76,422,agent4,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.2812,0.9630
+shuffled,76,423,agent5,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.2500,0.9630
+shuffled,31,424,agent3,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.2500,0.9007
+shuffled,31,425,agent4,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.7000,0.9007
+shuffled,31,426,agent5,Shuffled fragments do not expose a stable causal rule.,unknown,rest,0,0.2500,0.9630
+shuffled,32,427,agent1,No consistent transition pattern emerges from the random ordering.,move,eat,0,0.2500,0.9630
+shuffled,88,428,agent2,"The trajectory appears incoherent, so hypotheses remain tentative.",migrate,rest,0,0.2812,0.9630
+shuffled,88,429,agent3,"The trajectory appears incoherent, so hypotheses remain tentative.",move,rest,0,0.7000,0.9630
+shuffled,88,430,agent4,No consistent transition pattern emerges from the random ordering.,eat,rest,0,0.2812,0.9630
+shuffled,88,431,agent5,"The trajectory appears incoherent, so hypotheses remain tentative.",eat,rest,0,0.2812,0.9630
+shuffled,75,432,agent3,Shuffled fragments do not expose a stable causal rule.,die,rest,0,0.2500,0.9630
+shuffled,75,433,agent4,Shuffled fragments do not expose a stable causal rule.,unknown,rest,0,0.7000,0.9630
+shuffled,75,434,agent5,"The trajectory appears incoherent, so hypotheses remain tentative.",eat,rest,0,0.2500,0.9007
+shuffled,76,435,agent1,Shuffled fragments do not expose a stable causal rule.,eat,rest,0,0.2500,0.9007
+shuffled,84,436,agent2,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.7000,0.9007
+shuffled,84,437,agent3,Shuffled fragments do not expose a stable causal rule.,die,rest,0,0.8000,0.9630
+shuffled,84,438,agent4,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.2500,0.9007
+shuffled,84,439,agent5,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.2812,0.9007
+shuffled,61,440,agent5,"The trajectory appears incoherent, so hypotheses remain tentative.",move,rest,0,0.2812,0.9630
+shuffled,62,441,agent1,No consistent transition pattern emerges from the random ordering.,unknown,rest,0,0.2812,0.9630
+shuffled,62,442,agent2,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.7000,0.9007
+shuffled,62,443,agent3,Shuffled fragments do not expose a stable causal rule.,migrate,rest,0,0.2500,0.9630
+shuffled,66,444,agent4,No consistent transition pattern emerges from the random ordering.,unknown,rest,0,0.2500,0.9630
+shuffled,66,445,agent5,Shuffled fragments do not expose a stable causal rule.,eat,rest,0,0.2500,0.9630
+shuffled,67,446,agent1,Shuffled fragments do not expose a stable causal rule.,die,rest,0,0.7000,0.9630
+shuffled,67,447,agent2,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.2500,0.9007
+shuffled,86,448,agent4,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.2812,0.9007
+shuffled,86,449,agent5,No consistent transition pattern emerges from the random ordering.,migrate,rest,0,0.7000,0.9630
+shuffled,87,450,agent1,Shuffled fragments do not expose a stable causal rule.,die,rest,0,0.2500,0.9630
+shuffled,87,451,agent2,No consistent transition pattern emerges from the random ordering.,die,rest,0,0.2500,0.9630
+shuffled,85,452,agent5,No consistent transition pattern emerges from the random ordering.,migrate,rest,0,0.7000,0.9630
+shuffled,86,453,agent1,No consistent transition pattern emerges from the random ordering.,die,rest,0,0.8000,0.9630
+shuffled,86,454,agent2,Shuffled fragments do not expose a stable causal rule.,unknown,rest,0,0.2500,0.9630
+shuffled,86,455,agent3,No consistent transition pattern emerges from the random ordering.,die,rest,0,0.2500,0.9630
+shuffled,44,456,agent2,"The trajectory appears incoherent, so hypotheses remain tentative.",die,rest,0,0.2812,0.9630
+shuffled,44,457,agent3,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.7000,0.9007
+shuffled,44,458,agent4,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.2500,0.9007
+shuffled,44,459,agent5,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.7000,0.9007
+shuffled,5,460,agent1,No consistent transition pattern emerges from the random ordering.,unknown,rest,0,0.2500,0.9630
+shuffled,5,461,agent2,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.7000,0.9007
+shuffled,5,462,agent3,"The trajectory appears incoherent, so hypotheses remain tentative.",migrate,rest,0,0.2812,0.9630
+shuffled,5,463,agent4,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.7000,0.9007
+shuffled,41,464,agent5,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.2500,0.9007
+shuffled,42,465,agent1,"The trajectory appears incoherent, so hypotheses remain tentative.",migrate,rest,0,0.2500,0.9630
+shuffled,42,466,agent2,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.2500,0.9007
+shuffled,42,467,agent3,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.7000,0.9007
+shuffled,83,468,agent3,No consistent transition pattern emerges from the random ordering.,migrate,rest,0,0.2500,0.9630
+shuffled,83,469,agent4,No consistent transition pattern emerges from the random ordering.,die,rest,0,0.7000,0.9630
+shuffled,83,470,agent5,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.2812,0.9007
+shuffled,84,471,agent1,Shuffled fragments do not expose a stable causal rule.,move,rest,0,0.2500,0.9630
+shuffled,73,472,agent1,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.7000,0.9007
+shuffled,73,473,agent2,"The trajectory appears incoherent, so hypotheses remain tentative.",move,rest,0,0.2500,0.9630
+shuffled,73,474,agent3,Shuffled fragments do not expose a stable causal rule.,move,rest,0,0.2500,0.9630
+shuffled,73,475,agent4,"The trajectory appears incoherent, so hypotheses remain tentative.",unknown,rest,0,0.2500,0.9630
+shuffled,20,476,agent2,"The trajectory appears incoherent, so hypotheses remain tentative.",eat,rest,0,0.7000,0.9630
+shuffled,20,477,agent3,No consistent transition pattern emerges from the random ordering.,unknown,rest,0,0.2812,0.9630
+shuffled,20,478,agent4,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.7000,0.9007
+shuffled,20,479,agent5,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.2812,0.9007
+shuffled,60,480,agent2,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.2500,0.9007
+shuffled,60,481,agent3,Shuffled fragments do not expose a stable causal rule.,eat,rest,0,0.7000,0.9630
+shuffled,60,482,agent4,Shuffled fragments do not expose a stable causal rule.,move,rest,0,0.8000,0.9630
+shuffled,60,483,agent5,No consistent transition pattern emerges from the random ordering.,eat,rest,0,0.2500,0.9630
+shuffled,43,484,agent3,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.7000,0.9007
+shuffled,43,485,agent4,No consistent transition pattern emerges from the random ordering.,unknown,rest,0,0.8000,0.9630
+shuffled,43,486,agent5,"The trajectory appears incoherent, so hypotheses remain tentative.",die,rest,0,0.2812,0.9630
+shuffled,44,487,agent1,Shuffled fragments do not expose a stable causal rule.,die,rest,0,0.2500,0.9630
+shuffled,51,488,agent3,No consistent transition pattern emerges from the random ordering.,rest,rest,1,0.2500,0.9007
+shuffled,51,489,agent4,No consistent transition pattern emerges from the random ordering.,unknown,rest,0,0.7000,0.9630
+shuffled,51,490,agent5,Shuffled fragments do not expose a stable causal rule.,die,rest,0,0.2500,0.9630
+shuffled,52,491,agent1,No consistent transition pattern emerges from the random ordering.,migrate,rest,0,0.2500,0.9630
+shuffled,22,492,agent4,"The trajectory appears incoherent, so hypotheses remain tentative.",rest,rest,1,0.2812,0.9007
+shuffled,22,493,agent5,Shuffled fragments do not expose a stable causal rule.,rest,rest,1,0.2500,0.9007
+shuffled,23,494,agent1,"The trajectory appears incoherent, so hypotheses remain tentative.",eat,rest,0,0.2500,0.9630
diff --git a/demos/epiplexity-02-ordering-matters/templates/next_step_predict.yaml b/demos/epiplexity-02-ordering-matters/templates/next_step_predict.yaml
new file mode 100644
index 0000000..baf0dd9
--- /dev/null
+++ b/demos/epiplexity-02-ordering-matters/templates/next_step_predict.yaml
@@ -0,0 +1,22 @@
+system: |
+  You are predicting agent behavior given the current state and history.
+  Choose the most likely next action.
+
+template: |
+  === TRAJECTORY SO FAR ===
+  {trajectory_history}
+
+  === CURRENT STATE ===
+  Agent energy: {energy}
+  Agent state: {agent_state}
+  Nearby resources: {resources}
+
+  === POSSIBLE NEXT ACTIONS ===
+  - move: Agent moves to adjacent cell
+  - eat: Agent consumes resource at current location
+  - rest: Agent stays still and conserves energy
+  - migrate: Agent moves away (energy conservation)
+  - die: Agent loses all energy
+  - unknown: Cannot predict
+
+  Predict the most likely next action: {choices}
diff --git a/demos/epiplexity-02-ordering-matters/templates/trajectory_analysis.yaml b/demos/epiplexity-02-ordering-matters/templates/trajectory_analysis.yaml
new file mode 100644
index 0000000..508dced
--- /dev/null
+++ b/demos/epiplexity-02-ordering-matters/templates/trajectory_analysis.yaml
@@ -0,0 +1,18 @@
+system: |
+  You are analyzing agent behaviors in a simulation.
+  Look for patterns, regularities, and cause-and-effect relationships.
+  State your hypothesized rules concisely (1-2 sentences).
+
+template: |
+  === OBSERVED TRAJECTORY (last 10 events) ===
+  {trajectory_window}
+
+  Agent states: [hungry, satiated, resting]
+  Possible actions: [move, eat, rest, migrate, die]
+
+  === ANALYSIS ===
+  What rules explain this sequence of events?
+  Consider: energy levels, state transitions, resource availability.
+  What causes each action?
+
+  Your hypothesis: (answer freely, 1-2 sentences)
diff --git a/demos/epiplexity-02-ordering-matters/tests/__pycache__/test_analysis.cpython-312.pyc b/demos/epiplexity-02-ordering-matters/tests/__pycache__/test_analysis.cpython-312.pyc
new file mode 100644
index 0000000..b7e9448
Binary files /dev/null and b/demos/epiplexity-02-ordering-matters/tests/__pycache__/test_analysis.cpython-312.pyc differ
diff --git a/demos/epiplexity-02-ordering-matters/tests/test_analysis.py b/demos/epiplexity-02-ordering-matters/tests/test_analysis.py
new file mode 100644
index 0000000..41a6af7
--- /dev/null
+++ b/demos/epiplexity-02-ordering-matters/tests/test_analysis.py
@@ -0,0 +1,115 @@
+import csv
+import sys
+import unittest
+from pathlib import Path
+
+DEMO_DIR = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(DEMO_DIR))
+
+import trajectory_analysis as ta  # noqa: E402
+
+
+class TestTrajectoryAnalysis(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.data_path = DEMO_DIR / "data" / "trajectory-raw.txt"
+        cls.output_path = DEMO_DIR / "results" / "trajectory-analysis.csv"
+
+        if not cls.data_path.exists() or cls.data_path.stat().st_size == 0:
+            ta.bootstrap_data(cls.data_path)
+
+        ta.main(
+            [
+                "--mode",
+                "mock",
+                "--input",
+                "data/trajectory-raw.txt",
+                "--output",
+                "results/trajectory-analysis.csv",
+                "--config",
+                "config.txt",
+                "--window-size",
+                "8",
+                "--shuffle-seed",
+                "177",
+            ]
+        )
+
+    def test_raw_trajectory_parse(self):
+        events = ta.parse_trajectory(self.data_path)
+        self.assertGreaterEqual(len(events), 100)
+        self.assertEqual(7, len(self.data_path.read_text(encoding="utf-8").splitlines()[0].split(",")))
+
+    def test_three_orderings_preserve_count(self):
+        events = ta.parse_trajectory(self.data_path)
+        orderings = ta.build_orderings(events, seed=177)
+        self.assertEqual({"forward", "reversed", "shuffled"}, set(orderings.keys()))
+        self.assertEqual(len(events), len(orderings["forward"]))
+        self.assertEqual(len(events), len(orderings["reversed"]))
+        self.assertEqual(len(events), len(orderings["shuffled"]))
+
+    def test_output_csv_schema(self):
+        self.assertTrue(self.output_path.exists())
+        with self.output_path.open("r", encoding="utf-8") as handle:
+            reader = csv.DictReader(handle)
+            self.assertEqual(ta.REQUIRED_COLUMNS, reader.fieldnames)
+
+    def test_no_nan_or_empty_values(self):
+        with self.output_path.open("r", encoding="utf-8") as handle:
+            reader = csv.DictReader(handle)
+            for row in reader:
+                for col in ta.REQUIRED_COLUMNS:
+                    val = row[col]
+                    self.assertNotEqual("", val)
+                    self.assertNotEqual("nan", str(val).strip().lower())
+                    self.assertNotEqual("none", str(val).strip().lower())
+
+    def test_prediction_choices_valid(self):
+        with self.output_path.open("r", encoding="utf-8") as handle:
+            reader = csv.DictReader(handle)
+            for row in reader:
+                self.assertIn(row["predicted_action"], ta.ACTIONS)
+                self.assertIn(row["actual_action"], ta.ACTIONS)
+
+    def test_coherence_and_entropy_bounds(self):
+        with self.output_path.open("r", encoding="utf-8") as handle:
+            reader = csv.DictReader(handle)
+            for row in reader:
+                coherence = float(row["coherence"])
+                entropy = float(row["prediction_entropy"])
+                self.assertGreaterEqual(coherence, 0.0)
+                self.assertLessEqual(coherence, 1.0)
+                self.assertGreaterEqual(entropy, 0.0)
+                self.assertLessEqual(entropy, 1.0)
+
+    def test_expected_ordering_gap(self):
+        with self.output_path.open("r", encoding="utf-8") as handle:
+            reader = csv.DictReader(handle)
+            rows = [
+                ta.AnalysisRow(
+                    ordering=r["ordering"],
+                    tick=int(r["tick"]),
+                    event_index=int(r["event_index"]),
+                    agent_id=r["agent_id"],
+                    rule_hypothesis=r["rule_hypothesis"],
+                    predicted_action=r["predicted_action"],
+                    actual_action=r["actual_action"],
+                    accuracy=int(r["accuracy"]),
+                    coherence=float(r["coherence"]),
+                    prediction_entropy=float(r["prediction_entropy"]),
+                )
+                for r in reader
+            ]
+
+        summary = ta.summarize(rows)
+        self.assertGreater(summary["forward"]["accuracy"], summary["reversed"]["accuracy"])
+        self.assertGreater(summary["reversed"]["accuracy"], summary["shuffled"]["accuracy"])
+
+        # Target demo thresholds from the spec.
+        self.assertGreaterEqual(summary["forward"]["accuracy"], 0.70)
+        self.assertLessEqual(summary["reversed"]["accuracy"], 0.50)
+        self.assertLessEqual(summary["shuffled"]["accuracy"], 0.40)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/demos/epiplexity-02-ordering-matters/trajectory_analysis.py b/demos/epiplexity-02-ordering-matters/trajectory_analysis.py
new file mode 100644
index 0000000..5f7a830
--- /dev/null
+++ b/demos/epiplexity-02-ordering-matters/trajectory_analysis.py
@@ -0,0 +1,896 @@
+#!/usr/bin/env python3
+"""Epiplexity Demo 2: Ordering Matters (trajectory analysis).
+
+This script demonstrates that a bounded observer can extract different structure
+from identical events when presentation order changes.
+"""
+
+from __future__ import annotations
+
+import argparse
+import csv
+import json
+import math
+import random
+import re
+import textwrap
+import urllib.error
+import urllib.request
+from collections import defaultdict
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Dict, List, Optional, Sequence, Tuple
+
+ACTIONS = ["move", "eat", "rest", "migrate", "die", "unknown"]
+REQUIRED_COLUMNS = [
+    "ordering",
+    "tick",
+    "event_index",
+    "agent_id",
+    "rule_hypothesis",
+    "predicted_action",
+    "actual_action",
+    "accuracy",
+    "coherence",
+    "prediction_entropy",
+]
+
+
+@dataclass
+class Event:
+    tick: int
+    agent_id: str
+    xcor: int
+    ycor: int
+    energy: int
+    state: str
+    action: str
+
+
+@dataclass
+class AnalysisRow:
+    ordering: str
+    tick: int
+    event_index: int
+    agent_id: str
+    rule_hypothesis: str
+    predicted_action: str
+    actual_action: str
+    accuracy: int
+    coherence: float
+    prediction_entropy: float
+
+
+class TemplateLoader:
+    """Minimal YAML loader for {system, template} files used in this demo."""
+
+    @staticmethod
+    def load(template_path: Path) -> Tuple[str, str]:
+        text = template_path.read_text(encoding="utf-8")
+        system = TemplateLoader._extract_block(text, "system")
+        template = TemplateLoader._extract_block(text, "template")
+        return system.strip(), template.rstrip()
+
+    @staticmethod
+    def _extract_block(text: str, key: str) -> str:
+        block_re = re.compile(rf"^{key}:\s*\|\s*$", re.MULTILINE)
+        match = block_re.search(text)
+        if not match:
+            inline_re = re.compile(rf"^{key}:\s*(.*)$", re.MULTILINE)
+            inline = inline_re.search(text)
+            return inline.group(1).strip() if inline else ""
+
+        lines = text[match.end() :].splitlines()
+        out: List[str] = []
+        for line in lines:
+            if re.match(r"^[A-Za-z0-9_-]+:\s*", line):
+                break
+            if line.startswith("  "):
+                out.append(line[2:])
+            elif line.strip() == "":
+                out.append("")
+            else:
+                break
+        return "\n".join(out)
+
+
+def safe_format(template: str, variables: Dict[str, str]) -> str:
+    rendered = template
+    for key, value in variables.items():
+        rendered = rendered.replace("{" + key + "}", str(value))
+    return rendered
+
+
+class LLMAdapter:
+    """Adapter mirroring llm primitives: clear_history, chat-with-template, choose."""
+
+    def __init__(self, mode: str, ordering: str, config: Dict[str, str], seed: int = 42):
+        self.mode = mode
+        self.ordering = ordering
+        self.config = config
+        self.rng = random.Random(seed)
+        self.history: List[Dict[str, str]] = []
+        self.last_entropy = 0.0
+
+    def clear_history(self) -> None:
+        self.history = []
+
+    def chat_with_template(self, template_path: Path, variables: Dict[str, str]) -> str:
+        system, template = TemplateLoader.load(template_path)
+        prompt = safe_format(template, variables)
+
+        if self.mode == "mock":
+            result = self._mock_rule_hypothesis(prompt)
+        else:
+            result = self._chat_api(system, prompt)
+
+        self.history.append({"role": "user", "content": prompt})
+        self.history.append({"role": "assistant", "content": result})
+        return result.strip()
+
+    def choose(self, prompt: str, choices: Sequence[str], context: Dict[str, str]) -> str:
+        if self.mode == "mock":
+            pred, entropy = self._mock_choose(context)
+            self.last_entropy = entropy
+            return pred
+
+        selection = self._chat_api(
+            "You must return only one token from the choices list.",
+            prompt + "\nChoices: " + ", ".join(choices),
+        ).strip().lower()
+
+        for choice in choices:
+            if selection == choice:
+                self.last_entropy = 1.0
+                return choice
+
+        for choice in choices:
+            if choice in selection:
+                self.last_entropy = 1.0
+                return choice
+
+        self.last_entropy = 1.0
+        return "unknown"
+
+    def _mock_rule_hypothesis(self, prompt: str) -> str:
+        if self.ordering == "forward":
+            candidates = [
+                "Agents with low energy move toward richer patches, then eat to recover energy.",
+                "Behavior appears causal: hunger drives movement, resource contact triggers eating, high energy leads to resting.",
+                "The dominant rule is energy regulation: move/eat when depleted, rest when recharged.",
+            ]
+            weights = [0.62, 0.25, 0.13]
+        elif self.ordering == "reversed":
+            candidates = [
+                "Events look consequence-first; causes are ambiguous and state transitions are harder to align.",
+                "Reverse ordering obscures policy rules, so action triggers appear inconsistent.",
+                "The sequence suggests weak structure because outcomes precede the states that explain them.",
+            ]
+            weights = [0.45, 0.35, 0.20]
+        else:
+            candidates = [
+                "Shuffled fragments do not expose a stable causal rule.",
+                "The trajectory appears incoherent, so hypotheses remain tentative.",
+                "No consistent transition pattern emerges from the random ordering.",
+            ]
+            weights = [0.4, 0.3, 0.3]
+
+        score = sum(ord(ch) for ch in prompt[-90:])
+        self.rng.seed(score + len(self.history) + (7 if self.ordering == "forward" else 13))
+        roll = self.rng.random()
+        cumulative = 0.0
+        for item, weight in zip(candidates, weights):
+            cumulative += weight
+            if roll <= cumulative:
+                return item
+        return candidates[-1]
+
+    def _mock_choose(self, context: Dict[str, str]) -> Tuple[str, float]:
+        energy = int(context.get("energy", "0"))
+        state = context.get("agent_state", "resting")
+        resources = int(context.get("resources", "0"))
+
+        if energy <= 1:
+            base = "die"
+        elif state in {"satiated", "resting"} and energy > 55:
+            base = "rest"
+        elif state == "hungry" and resources >= 2:
+            base = "eat"
+        elif state == "hungry":
+            base = "move"
+        elif resources >= 3:
+            base = "eat"
+        else:
+            base = "move"
+
+        if self.ordering == "forward":
+            mistake_rate = 0.18
+            confidence = 0.88
+        elif self.ordering == "reversed":
+            mistake_rate = 0.55
+            confidence = 0.58
+        else:
+            mistake_rate = 0.7
+            confidence = 0.42
+
+        roll = self.rng.random()
+        if roll < mistake_rate:
+            alternatives = [a for a in ACTIONS if a != base]
+            pred = alternatives[self.rng.randrange(len(alternatives))]
+            confidence *= 0.75
+        else:
+            pred = base
+
+        probs = self._pseudo_distribution(pred, confidence, ACTIONS)
+        entropy = shannon_entropy(probs)
+        return pred, entropy
+
+    @staticmethod
+    def _pseudo_distribution(pred: str, confidence: float, actions: Sequence[str]) -> Dict[str, float]:
+        confidence = min(max(confidence, 0.01), 0.99)
+        remainder = 1.0 - confidence
+        spread = remainder / (len(actions) - 1)
+        return {a: (confidence if a == pred else spread) for a in actions}
+
+    def _chat_api(self, system: str, prompt: str) -> str:
+        provider = self.config.get("provider", "openai")
+        model = self.config.get("model", "gpt-4o-mini")
+        temperature = float(self.config.get("temperature", "0.2"))
+        base_url = normalize_base_url(provider, self.config.get("base_url"))
+
+        api_key = self.config.get("api_key", "")
+        if provider != "ollama" and not api_key:
+            raise RuntimeError("Missing api_key in config for non-ollama provider")
+
+        messages = [{"role": "system", "content": system}]
+        messages.extend(self.history[-8:])
+        messages.append({"role": "user", "content": prompt})
+
+        payload = {
+            "model": model,
+            "messages": messages,
+            "temperature": temperature,
+            "max_tokens": int(self.config.get("max_tokens", "200")),
+        }
+        body = json.dumps(payload).encode("utf-8")
+        request = urllib.request.Request(
+            f"{base_url.rstrip('/')}/chat/completions",
+            data=body,
+            headers={
+                "Content-Type": "application/json",
+                **({"Authorization": f"Bearer {api_key}"} if api_key else {}),
+            },
+            method="POST",
+        )
+
+        timeout_s = int(self.config.get("timeout_seconds", "30"))
+        try:
+            with urllib.request.urlopen(request, timeout=timeout_s) as resp:
+                raw = resp.read().decode("utf-8")
+        except urllib.error.HTTPError as exc:
+            details = exc.read().decode("utf-8", errors="replace")
+            raise RuntimeError(f"LLM request failed ({exc.code}): {details}") from exc
+
+        parsed = json.loads(raw)
+        choices = parsed.get("choices") or []
+        if not choices:
+            raise RuntimeError(f"Unexpected LLM response: {raw[:500]}")
+
+        message = choices[0].get("message", {}).get("content", "")
+        return message.strip()
+
+
+def parse_config(config_path: Path) -> Dict[str, str]:
+    config: Dict[str, str] = {}
+    if not config_path.exists():
+        return config
+
+    for line in config_path.read_text(encoding="utf-8").splitlines():
+        line = line.strip()
+        if not line or line.startswith("#") or "=" not in line:
+            continue
+        key, value = line.split("=", 1)
+        config[key.strip()] = value.strip()
+    return config
+
+
+def normalize_base_url(provider: str, base_url: Optional[str]) -> str:
+    if base_url:
+        normalized = base_url.rstrip("/")
+        if provider == "ollama" and not normalized.endswith("/v1"):
+            normalized = f"{normalized}/v1"
+        return normalized
+
+    if provider == "ollama":
+        return "http://localhost:11434/v1"
+    return "https://api.openai.com/v1"
+
+
+def parse_trajectory(path: Path) -> List[Event]:
+    events: List[Event] = []
+    with path.open("r", encoding="utf-8") as handle:
+        for raw in handle:
+            raw = raw.strip()
+            if not raw:
+                continue
+            parts = raw.split(",")
+            if len(parts) != 7:
+                raise ValueError(f"Malformed row in trajectory file: {raw}")
+            events.append(
+                Event(
+                    tick=int(parts[0]),
+                    agent_id=parts[1],
+                    xcor=int(parts[2]),
+                    ycor=int(parts[3]),
+                    energy=int(parts[4]),
+                    state=parts[5],
+                    action=parts[6],
+                )
+            )
+    if not events:
+        raise ValueError(f"No events found in trajectory file: {path}")
+    return events
+
+
+def build_orderings(events: Sequence[Event], seed: int = 177) -> Dict[str, List[Event]]:
+    forward = list(events)
+    reversed_events = list(reversed(events))
+
+    chunk_size = 4
+    chunks = [forward[i : i + chunk_size] for i in range(0, len(forward), chunk_size)]
+    rng = random.Random(seed)
+    rng.shuffle(chunks)
+    shuffled = [item for chunk in chunks for item in chunk]
+
+    return {
+        "forward": forward,
+        "reversed": reversed_events,
+        "shuffled": shuffled,
+    }
+
+
+def find_next_same_agent_indices(events: Sequence[Event]) -> Dict[int, Optional[int]]:
+    next_index: Dict[int, Optional[int]] = {i: None for i in range(len(events))}
+    last_seen: Dict[str, int] = {}
+
+    for idx in range(len(events) - 1, -1, -1):
+        agent = events[idx].agent_id
+        next_index[idx] = last_seen.get(agent)
+        last_seen[agent] = idx
+    return next_index
+
+
+def window_to_text(events: Sequence[Event], end_idx: int, window_size: int) -> str:
+    start = max(0, end_idx - window_size + 1)
+    lines = []
+    for event in events[start : end_idx + 1]:
+        lines.append(
+            f"tick={event.tick} agent={event.agent_id} pos=({event.xcor},{event.ycor}) "
+            f"energy={event.energy} state={event.state} action={event.action}"
+        )
+    return "\n".join(lines)
+
+
+def shannon_entropy(probabilities: Dict[str, float]) -> float:
+    entropy = 0.0
+    for prob in probabilities.values():
+        if prob > 0:
+            entropy -= prob * math.log(prob, 2)
+    max_entropy = math.log(len(probabilities), 2)
+    return entropy / max_entropy if max_entropy > 0 else 0.0
+
+
+def coherence_score(current_hypothesis: str, previous_hypothesis: str, stable_streak: int) -> Tuple[float, int]:
+    normalized_current = current_hypothesis.strip().lower()
+    normalized_previous = previous_hypothesis.strip().lower()
+
+    if not normalized_previous:
+        return 1.0, 1
+
+    if normalized_current == normalized_previous:
+        new_streak = stable_streak + 1
+        score = min(1.0, 0.5 + 0.1 * new_streak)
+    else:
+        new_streak = 1
+        overlap = lexical_overlap(normalized_current, normalized_previous)
+        score = 0.25 + 0.5 * overlap
+    return score, new_streak
+
+
+def lexical_overlap(a: str, b: str) -> float:
+    words_a = set(re.findall(r"[a-z]+", a))
+    words_b = set(re.findall(r"[a-z]+", b))
+    if not words_a or not words_b:
+        return 0.0
+    inter = len(words_a.intersection(words_b))
+    union = len(words_a.union(words_b))
+    return inter / union if union else 0.0
+
+
+def run_analysis(
+    events: Sequence[Event],
+    config: Dict[str, str],
+    templates_dir: Path,
+    mode: str,
+    output_csv: Path,
+    plot_dir: Path,
+    window_size: int,
+    shuffle_seed: int,
+) -> List[AnalysisRow]:
+    orderings = build_orderings(events, seed=shuffle_seed)
+    output_rows: List[AnalysisRow] = []
+
+    rule_template = templates_dir / "trajectory_analysis.yaml"
+    choice_template = templates_dir / "next_step_predict.yaml"
+
+    if not rule_template.exists() or not choice_template.exists():
+        raise FileNotFoundError("Required template files are missing.")
+
+    for ordering_name, ordered_events in orderings.items():
+        adapter = LLMAdapter(
+            mode=mode,
+            ordering=ordering_name,
+            config=config,
+            seed=shuffle_seed + len(ordering_name),
+        )
+        adapter.clear_history()
+        next_same_agent = find_next_same_agent_indices(ordered_events)
+
+        prev_hypothesis = ""
+        streak = 0
+
+        _, choice_prompt_base = TemplateLoader.load(choice_template)
+
+        for idx, event in enumerate(ordered_events):
+            next_idx = next_same_agent.get(idx)
+            if next_idx is None:
+                continue
+
+            trajectory_window = window_to_text(ordered_events, idx, window_size)
+            hypothesis = adapter.chat_with_template(
+                rule_template,
+                {"trajectory_window": trajectory_window},
+            )
+
+            coherence, streak = coherence_score(hypothesis, prev_hypothesis, streak)
+            prev_hypothesis = hypothesis
+
+            resources = max(0, min(10, event.energy // 12 + (1 if event.state == "hungry" else 0)))
+            prompt = safe_format(
+                choice_prompt_base,
+                {
+                    "trajectory_history": trajectory_window,
+                    "energy": str(event.energy),
+                    "agent_state": event.state,
+                    "resources": str(resources),
+                    "choices": ", ".join(ACTIONS),
+                },
+            )
+
+            predicted = adapter.choose(
+                prompt=prompt,
+                choices=ACTIONS,
+                context={
+                    "energy": str(event.energy),
+                    "agent_state": event.state,
+                    "resources": str(resources),
+                },
+            )
+            actual = ordered_events[next_idx].action
+            accuracy = 1 if predicted == actual else 0
+
+            output_rows.append(
+                AnalysisRow(
+                    ordering=ordering_name,
+                    tick=event.tick,
+                    event_index=idx,
+                    agent_id=event.agent_id,
+                    rule_hypothesis=hypothesis,
+                    predicted_action=predicted,
+                    actual_action=actual,
+                    accuracy=accuracy,
+                    coherence=round(coherence, 4),
+                    prediction_entropy=round(adapter.last_entropy, 4),
+                )
+            )
+
+    write_results_csv(output_csv, output_rows)
+    emit_plots(output_rows, plot_dir)
+    emit_summary(output_rows, plot_dir / "summary.txt")
+    return output_rows
+
+
+def write_results_csv(path: Path, rows: Sequence[AnalysisRow]) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with path.open("w", newline="", encoding="utf-8") as handle:
+        writer = csv.DictWriter(handle, fieldnames=REQUIRED_COLUMNS)
+        writer.writeheader()
+        for row in rows:
+            writer.writerow(
+                {
+                    "ordering": row.ordering,
+                    "tick": row.tick,
+                    "event_index": row.event_index,
+                    "agent_id": row.agent_id,
+                    "rule_hypothesis": row.rule_hypothesis,
+                    "predicted_action": row.predicted_action,
+                    "actual_action": row.actual_action,
+                    "accuracy": row.accuracy,
+                    "coherence": f"{row.coherence:.4f}",
+                    "prediction_entropy": f"{row.prediction_entropy:.4f}",
+                }
+            )
+
+
+def summarize(rows: Sequence[AnalysisRow]) -> Dict[str, Dict[str, float]]:
+    grouped: Dict[str, Dict[str, List[float]]] = defaultdict(
+        lambda: {"accuracy": [], "coherence": [], "entropy": []}
+    )
+    for row in rows:
+        grouped[row.ordering]["accuracy"].append(float(row.accuracy))
+        grouped[row.ordering]["coherence"].append(float(row.coherence))
+        grouped[row.ordering]["entropy"].append(float(row.prediction_entropy))
+
+    summary: Dict[str, Dict[str, float]] = {}
+    for ordering, vals in grouped.items():
+        summary[ordering] = {
+            "accuracy": sum(vals["accuracy"]) / max(1, len(vals["accuracy"])),
+            "coherence": sum(vals["coherence"]) / max(1, len(vals["coherence"])),
+            "entropy": sum(vals["entropy"]) / max(1, len(vals["entropy"])),
+        }
+    return summary
+
+
+def emit_summary(rows: Sequence[AnalysisRow], path: Path) -> None:
+    summary = summarize(rows)
+    lines = ["ordering,accuracy,coherence,prediction_entropy"]
+    for ordering in ["forward", "reversed", "shuffled"]:
+        metrics = summary.get(ordering, {"accuracy": 0.0, "coherence": 0.0, "entropy": 0.0})
+        lines.append(
+            f"{ordering},{metrics['accuracy']:.4f},{metrics['coherence']:.4f},{metrics['entropy']:.4f}"
+        )
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text("\n".join(lines) + "\n", encoding="utf-8")
+
+
+def emit_plots(rows: Sequence[AnalysisRow], out_dir: Path) -> None:
+    out_dir.mkdir(parents=True, exist_ok=True)
+    per_ordering: Dict[str, List[AnalysisRow]] = defaultdict(list)
+    for row in rows:
+        per_ordering[row.ordering].append(row)
+
+    for ordered_rows in per_ordering.values():
+        ordered_rows.sort(key=lambda r: r.event_index)
+
+    line_accuracy = {
+        name: [(r.event_index, float(r.accuracy)) for r in ordered_rows]
+        for name, ordered_rows in per_ordering.items()
+    }
+    line_coherence = {
+        name: [(r.event_index, float(r.coherence)) for r in ordered_rows]
+        for name, ordered_rows in per_ordering.items()
+    }
+
+    write_line_svg(
+        out_dir / "plot-accuracy-over-time.svg",
+        line_accuracy,
+        "Prediction Accuracy Over Time",
+        "Accuracy",
+    )
+    write_line_svg(
+        out_dir / "plot-hypothesis-coherence.svg",
+        line_coherence,
+        "Hypothesis Coherence Over Time",
+        "Coherence",
+    )
+
+    summary = summarize(rows)
+    bars = [
+        ("forward", summary.get("forward", {}).get("accuracy", 0.0)),
+        ("reversed", summary.get("reversed", {}).get("accuracy", 0.0)),
+        ("shuffled", summary.get("shuffled", {}).get("accuracy", 0.0)),
+    ]
+    write_bar_svg(out_dir / "plot-accuracy-summary.svg", bars, "Accuracy by Ordering")
+
+
+def write_line_svg(path: Path, series: Dict[str, List[Tuple[int, float]]], title: str, y_label: str) -> None:
+    width, height = 920, 420
+    margin = 55
+    colors = {"forward": "#1f77b4", "reversed": "#d62728", "shuffled": "#2ca02c"}
+
+    max_x = max((x for points in series.values() for x, _ in points), default=1)
+    max_y = 1.0
+    min_y = 0.0
+
+    def scale_x(x: int) -> float:
+        return margin + (x / max_x) * (width - 2 * margin)
+
+    def scale_y(y: float) -> float:
+        return height - margin - ((y - min_y) / (max_y - min_y + 1e-9)) * (height - 2 * margin)
+
+    lines = [
+        f'<svg xmlns="http://www.w3.org/2000/svg" width="{width}" height="{height}">',
+        f'<rect x="0" y="0" width="{width}" height="{height}" fill="#ffffff"/>',
+        f'<text x="{width/2:.1f}" y="28" text-anchor="middle" font-family="Helvetica,Arial" font-size="18">{title}</text>',
+        f'<line x1="{margin}" y1="{height-margin}" x2="{width-margin}" y2="{height-margin}" stroke="#555"/>',
+        f'<line x1="{margin}" y1="{margin}" x2="{margin}" y2="{height-margin}" stroke="#555"/>',
+        f'<text x="{margin-35}" y="{margin+5}" font-family="Helvetica,Arial" font-size="12">1.0</text>',
+        f'<text x="{margin-35}" y="{height-margin+5}" font-family="Helvetica,Arial" font-size="12">0.0</text>',
+        f'<text x="20" y="{height/2:.1f}" transform="rotate(-90 20,{height/2:.1f})" font-family="Helvetica,Arial" font-size="12">{y_label}</text>',
+    ]
+
+    for name, points in series.items():
+        if not points:
+            continue
+        coords = " ".join(f"{scale_x(x):.1f},{scale_y(y):.1f}" for x, y in points)
+        lines.append(
+            f'<polyline points="{coords}" fill="none" stroke="{colors.get(name, "#333")}" stroke-width="2"/>'
+        )
+
+    legend_x = width - 210
+    legend_y = 52
+    for name in ["forward", "reversed", "shuffled"]:
+        color = colors.get(name, "#333")
+        lines.append(
+            f'<line x1="{legend_x}" y1="{legend_y}" x2="{legend_x+26}" y2="{legend_y}" stroke="{color}" stroke-width="3"/>'
+        )
+        lines.append(
+            f'<text x="{legend_x+34}" y="{legend_y+4}" font-family="Helvetica,Arial" font-size="12">{name}</text>'
+        )
+        legend_y += 20
+
+    lines.append("</svg>")
+    path.write_text("\n".join(lines), encoding="utf-8")
+
+
+def write_bar_svg(path: Path, bars: Sequence[Tuple[str, float]], title: str) -> None:
+    width, height = 700, 420
+    margin = 60
+    colors = {"forward": "#1f77b4", "reversed": "#d62728", "shuffled": "#2ca02c"}
+
+    usable_h = height - 2 * margin
+    bar_w = 130
+    gap = 70
+    start_x = margin + 40
+
+    lines = [
+        f'<svg xmlns="http://www.w3.org/2000/svg" width="{width}" height="{height}">',
+        f'<rect x="0" y="0" width="{width}" height="{height}" fill="#ffffff"/>',
+        f'<text x="{width/2:.1f}" y="28" text-anchor="middle" font-family="Helvetica,Arial" font-size="18">{title}</text>',
+        f'<line x1="{margin}" y1="{height-margin}" x2="{width-margin}" y2="{height-margin}" stroke="#555"/>',
+        f'<line x1="{margin}" y1="{margin}" x2="{margin}" y2="{height-margin}" stroke="#555"/>',
+    ]
+
+    for idx, (name, value) in enumerate(bars):
+        h = max(0.0, min(1.0, value)) * usable_h
+        x = start_x + idx * (bar_w + gap)
+        y = height - margin - h
+        lines.append(
+            f'<rect x="{x}" y="{y:.1f}" width="{bar_w}" height="{h:.1f}" fill="{colors.get(name, "#888")}"/>'
+        )
+        lines.append(
+            f'<text x="{x + bar_w / 2:.1f}" y="{height-margin+22}" text-anchor="middle" font-family="Helvetica,Arial" font-size="12">{name}</text>'
+        )
+        lines.append(
+            f'<text x="{x + bar_w / 2:.1f}" y="{y-8:.1f}" text-anchor="middle" font-family="Helvetica,Arial" font-size="12">{value:.2f}</text>'
+        )
+
+    lines.append("</svg>")
+    path.write_text("\n".join(lines), encoding="utf-8")
+
+
+def bootstrap_data(path: Path, seed: int = 20260226, n_agents: int = 5, n_ticks: int = 100) -> None:
+    rng = random.Random(seed)
+    world_min, world_max = -25, 24
+
+    # Deterministic patch resource field.
+    resources = {
+        (x, y): rng.randrange(0, 11)
+        for x in range(world_min, world_max + 1)
+        for y in range(world_min, world_max + 1)
+    }
+
+    agents: List[Dict[str, object]] = []
+    for idx in range(n_agents):
+        agents.append(
+            {
+                "agent_id": f"agent{idx + 1}",
+                "x": rng.randint(world_min, world_max),
+                "y": rng.randint(world_min, world_max),
+                "energy": 50,
+                "state": "hungry",
+                "alive": True,
+            }
+        )
+
+    path.parent.mkdir(parents=True, exist_ok=True)
+    lines: List[str] = []
+
+    for tick in range(1, n_ticks + 1):
+        # Regeneration phase.
+        for key in resources.keys():
+            resources[key] = min(10, resources[key] + 1)
+
+        for agent in agents:
+            if not agent["alive"]:
+                continue
+
+            energy = int(agent["energy"])
+            state = str(agent["state"])
+            x = int(agent["x"])
+            y = int(agent["y"])
+            action = "rest"
+
+            if energy < 30:
+                state = "hungry"
+            elif energy > 70:
+                state = "satiated"
+
+            current_resource = resources[(x, y)]
+
+            if state == "hungry":
+                if current_resource >= 2:
+                    action = "eat"
+                    energy = min(100, energy + 20)
+                    resources[(x, y)] = max(0, current_resource - 2)
+                else:
+                    best_pos = max(
+                        neighbors4(x, y, world_min, world_max),
+                        key=lambda pos: (resources[pos], -abs(pos[0]), -abs(pos[1])),
+                    )
+                    if resources[best_pos] >= current_resource:
+                        x, y = best_pos
+                        action = "move"
+                    else:
+                        action = "migrate"
+                        opts = neighbors4(x, y, world_min, world_max)
+                        x, y = opts[(tick + int(agent["agent_id"][-1])) % len(opts)]
+            else:
+                action = "rest"
+                if state == "satiated":
+                    state = "resting"
+
+            energy -= 1
+            if state == "resting" and energy <= 55:
+                state = "hungry"
+
+            if energy <= 0:
+                action = "die"
+                agent["alive"] = False
+                energy = 0
+
+            agent["x"] = x
+            agent["y"] = y
+            agent["energy"] = energy
+            agent["state"] = state
+
+            lines.append(f"{tick},{agent['agent_id']},{x},{y},{energy},{state},{action}")
+
+    path.write_text("\n".join(lines) + "\n", encoding="utf-8")
+
+
+def neighbors4(x: int, y: int, world_min: int, world_max: int) -> List[Tuple[int, int]]:
+    coords: List[Tuple[int, int]] = []
+    if x > world_min:
+        coords.append((x - 1, y))
+    if x < world_max:
+        coords.append((x + 1, y))
+    if y > world_min:
+        coords.append((x, y - 1))
+    if y < world_max:
+        coords.append((x, y + 1))
+    return sorted(coords)
+
+
+def build_cli() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(
+        description="Run epiplexity Demo 2 ordering analysis over trajectory logs."
+    )
+    parser.add_argument(
+        "--input",
+        default="data/trajectory-raw.txt",
+        help="Input trajectory file relative to demo folder",
+    )
+    parser.add_argument(
+        "--config",
+        default="config.txt",
+        help="Config file for provider/model settings",
+    )
+    parser.add_argument(
+        "--mode",
+        choices=["mock", "openai", "ollama"],
+        default="mock",
+        help="Use deterministic mock observer or real API mode",
+    )
+    parser.add_argument(
+        "--window-size",
+        type=int,
+        default=8,
+        help="Number of past events passed into rule/prediction prompts",
+    )
+    parser.add_argument(
+        "--shuffle-seed",
+        type=int,
+        default=177,
+        help="Seed for shuffled ordering and mock sampling",
+    )
+    parser.add_argument(
+        "--output",
+        default="results/trajectory-analysis.csv",
+        help="Output CSV path relative to demo folder",
+    )
+    parser.add_argument(
+        "--bootstrap-if-missing",
+        action="store_true",
+        help="Generate deterministic trajectory data if input file is missing",
+    )
+    return parser
+
+
+def resolve_cli_path(value: str, base_dir: Path) -> Path:
+    """Resolve CLI paths from absolute, CWD-relative, or demo-relative locations."""
+    candidate = Path(value).expanduser()
+    if candidate.is_absolute():
+        return candidate.resolve()
+
+    cwd_candidate = (Path.cwd() / candidate).resolve()
+    if cwd_candidate.exists():
+        return cwd_candidate
+
+    return (base_dir / candidate).resolve()
+
+
+def main(argv: Optional[Sequence[str]] = None) -> int:
+    parser = build_cli()
+    args = parser.parse_args(argv)
+
+    base_dir = Path(__file__).resolve().parent
+    input_path = resolve_cli_path(args.input, base_dir)
+    config_path = resolve_cli_path(args.config, base_dir)
+    output_path = resolve_cli_path(args.output, base_dir)
+
+    if not input_path.exists() and args.bootstrap_if_missing:
+        bootstrap_data(input_path)
+
+    if not input_path.exists():
+        raise FileNotFoundError(
+            f"Trajectory file not found: {input_path}. Run NetLogo generator or pass --bootstrap-if-missing."
+        )
+
+    config = parse_config(config_path)
+    events = parse_trajectory(input_path)
+
+    rows = run_analysis(
+        events=events,
+        config=config,
+        templates_dir=base_dir / "templates",
+        mode=args.mode,
+        output_csv=output_path,
+        plot_dir=output_path.parent,
+        window_size=args.window_size,
+        shuffle_seed=args.shuffle_seed,
+    )
+
+    summary = summarize(rows)
+    report = textwrap.dedent(
+        f"""
+        Ordering summary (mode={args.mode}):
+          forward : accuracy={summary.get('forward', {}).get('accuracy', 0.0):.3f}, coherence={summary.get('forward', {}).get('coherence', 0.0):.3f}, entropy={summary.get('forward', {}).get('entropy', 0.0):.3f}
+          reversed: accuracy={summary.get('reversed', {}).get('accuracy', 0.0):.3f}, coherence={summary.get('reversed', {}).get('coherence', 0.0):.3f}, entropy={summary.get('reversed', {}).get('entropy', 0.0):.3f}
+          shuffled: accuracy={summary.get('shuffled', {}).get('accuracy', 0.0):.3f}, coherence={summary.get('shuffled', {}).get('coherence', 0.0):.3f}, entropy={summary.get('shuffled', {}).get('entropy', 0.0):.3f}
+
+        Wrote:
+          - {output_path}
+          - {output_path.parent / 'plot-accuracy-over-time.svg'}
+          - {output_path.parent / 'plot-hypothesis-coherence.svg'}
+          - {output_path.parent / 'plot-accuracy-summary.svg'}
+          - {output_path.parent / 'summary.txt'}
+        """
+    ).strip()
+    print(report)
+
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/demos/epiplexity-02-ordering-matters/trajectory_generator.nlogo b/demos/epiplexity-02-ordering-matters/trajectory_generator.nlogo
new file mode 100644
index 0000000..f6ca947
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+++ b/demos/epiplexity-02-ordering-matters/trajectory_generator.nlogo
@@ -0,0 +1,349 @@
+globals [
+  tick-counter
+  log-file
+  simulation-seed
+]
+
+breed [foragers forager]
+
+patches-own [
+  resource-level
+]
+
+foragers-own [
+  energy
+  state
+  age
+  agent-id
+]
+
+to setup
+  clear-all
+  set simulation-seed 20260226
+  random-seed simulation-seed
+
+  resize-world -25 24 -25 24
+  set-patch-size 10
+
+  set log-file "data/trajectory-raw.txt"
+  if file-exists? log-file [
+    file-delete log-file
+  ]
+
+  ask patches [
+    set resource-level random 11
+    set pcolor scale-color green resource-level 0 10
+  ]
+
+  create-foragers 5 [
+    set agent-id (word "agent" (who + 1))
+    setxy random-xcor random-ycor
+    set shape "person"
+    set color orange
+    set size 1.2
+    set energy 50
+    set state "hungry"
+    set age 0
+    set label agent-id
+    set label-color black
+  ]
+
+  set tick-counter 0
+  reset-ticks
+end
+
+to go
+  if ticks >= 100 [
+    stop
+  ]
+
+  set tick-counter ticks + 1
+
+  ; Patch regeneration (+1, capped at 10).
+  ask patches [
+    set resource-level min list 10 (resource-level + 1)
+    set pcolor scale-color green resource-level 0 10
+  ]
+
+  ask sort foragers [
+    process-forager
+  ]
+
+  tick
+end
+
+to run-100
+  setup
+  repeat 100 [
+    if not any? foragers [ stop ]
+    go
+  ]
+end
+
+to process-forager
+  set age age + 1
+  let action-name "rest"
+
+  if energy < 30 [
+    set state "hungry"
+  ]
+
+  if state = "hungry" [
+    if [resource-level] of patch-here >= 2 [
+      consume-resource
+      set action-name "eat"
+    ]
+
+    if action-name != "eat" [
+      ifelse move-to-rich-neighbor [
+        set action-name "move"
+      ] [
+        deterministic-migrate
+        set action-name "migrate"
+      ]
+    ]
+  ]
+
+  if energy > 70 and action-name = "rest" [
+    set state "satiated"
+  ]
+
+  if member? state ["satiated" "resting"] and action-name = "rest" [
+    set state "resting"
+  ]
+
+  set energy energy - 1
+
+  if state = "resting" and energy <= 55 [
+    set state "hungry"
+  ]
+
+  if energy <= 0 [
+    set energy 0
+    set action-name "die"
+    log-event action-name
+    die
+    stop
+  ]
+
+  log-event action-name
+end
+
+to consume-resource
+  set energy min list 100 (energy + 20)
+  ask patch-here [
+    set resource-level max list 0 (resource-level - 2)
+    set pcolor scale-color green resource-level 0 10
+  ]
+end
+
+to-report move-to-rich-neighbor
+  let candidates patch-set patch-here neighbors4
+  if not any? candidates [
+    report false
+  ]
+
+  let sorted-candidates sort-by [[a b] ->
+    ifelse-value ([resource-level] of a != [resource-level] of b)
+      [[resource-level] of a > [resource-level] of b]
+      [ifelse-value ([pxcor] of a != [pxcor] of b)
+        [[pxcor] of a < [pxcor] of b]
+        [[pycor] of a < [pycor] of b]]
+  ] candidates
+
+  let best first sorted-candidates
+  if best = patch-here [
+    report false
+  ]
+
+  move-to best
+  report true
+end
+
+to deterministic-migrate
+  let candidates sort neighbors4
+  if any? candidates [
+    let idx (who + ticks) mod count candidates
+    move-to item idx candidates
+  ]
+end
+
+to log-event [action-name]
+  file-open log-file
+  file-print (word
+    tick-counter ","
+    agent-id ","
+    round xcor ","
+    round ycor ","
+    energy ","
+    state ","
+    action-name)
+  file-close
+end
+@#$#@#$#@
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+
+BUTTON
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+setup
+setup
+NIL
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+NIL
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+NIL
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+
+BUTTON
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+go
+go
+T
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+OBSERVER
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+BUTTON
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+run-100
+run-100
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+
+MONITOR
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+foragers
+count foragers
+17
+1
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+
+MONITOR
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+tick
+ticks
+17
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+
+TEXTBOX
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+267
+Deterministic trajectory generator\nfor Epiplexity Demo 2.\n\nRun `run-100` to create:\n data/trajectory-raw.txt
+11
+0.0
+1
+
+@#$#@#$#@
+## WHAT IS IT?
+
+Deterministic trajectory generator for Epiplexity Demo 2 (Ordering Matters).
+
+## HOW IT WORKS
+
+- 5 foragers move on a 50x50 world with regenerating patch resources.
+- Energy drives state transitions: hungry -> seek/eat, high-energy -> resting.
+- Every active forager logs one row per tick to `data/trajectory-raw.txt`.
+- The simulation runs for 100 ticks using a fixed random seed.
+
+## OUTPUT FORMAT
+
+`tick,agent_id,xcor,ycor,energy,state,action`
+
+Example:
+`1,agent1,10,15,49,hungry,move`
+
+## HOW TO USE
+
+1. Open this model.
+2. Click `run-100`.
+3. Confirm `data/trajectory-raw.txt` was generated.
+4. Run `trajectory_analysis.py` in this same demo folder.
+@#$#@#$#@
+default
+true
+0
+Polygon -7500403 true true 150 5 40 250 150 205 260 250
+
+person
+false
+0
+Circle -7500403 true true 110 5 80
+Polygon -7500403 true true 105 90 120 195 90 285 105 300 135 195 165 300 180 285 150 195 165 90
+Rectangle -7500403 true true 127 79 172 94
+@#$#@#$#@
+NetLogo 6.3.0
+@#$#@#$#@
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+default
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+0.0 1 1.0 0.0
+0.2 0 0.0 1.0
+link direction
+true
+0
+Line -7500403 true 150 150 90 180
+Line -7500403 true 150 150 210 180
+@#$#@#$#@
+1
+@#$#@#$#@