experiment-runner-run

name	experiment-runner-run
description	Run survival arena experiments

Experiment Runner (proj-012)

Adaptive experiment runner for survival arena. Runs experiments one by one, analyzes results, commits. Between experiments -- human/AI decides what to change next.

Workflow (adaptive)

1. --status        → view what's been done
2. --next          → run the next pending experiment
3. Analysis        → view analysis.json, understand the result
4. Decision        → what to change? (only environment conditions, not behavior)
5. Edit YAML       → modify the next experiment or add a new one
6. Repeat from #2

Rule: we only change conditions (pressure, resources, architecture). Never hardcode agent behavior.

When to use

• "run experiment" / "next experiment"
• "what is the experiment status"
• "run EXP-011c"
• "run the next 2 experiments"
• proj-012 experiment pipeline

Dependencies

• Python 3, PyYAML (pip install pyyaml)
• Claude CLI (for LLM queries in the arena)
• Git (for committing results)

Paths

What	Path
Arena	$AGENTS_PATH/survival-arena/arena.py
Orchestrator	$AGENTS_PATH/survival-arena/run_experiments.py
Plan (YAML)	$AGENTS_PATH/survival-arena/experiments.yaml
Results	$AGENTS_PATH/survival-arena/experiment_results.json
Logs	$AGENTS_PATH/survival-arena/logs/experiments/
Documentation	$PROJECT_ROOT/projects/docs/proj-012-agi-consciousness/

How to execute

Next experiment (main mode)

cd $AGENTS_PATH/survival-arena
python3 run_experiments.py --next

Next N experiments

python3 run_experiments.py --next 2

View status

python3 run_experiments.py --status

Run a specific experiment

python3 run_experiments.py --experiment EXP-011c

Run all pending (batch mode)

python3 run_experiments.py --resume

View commands without running

python3 run_experiments.py --dry-run --next 3

Check arena config

python3 arena.py --config-dump --upkeep-base 0 --architecture single

arena.py parameters

Parameter	Description	Default
--upkeep-base N	Pressure: maintenance cost per turn	2
--regen-rate N	Resource regeneration rate per turn	3
--num-nodes N	Number of resource nodes (distributed across clusters)	12
--child-ratio F	Child token share	0.35
--repro-threshold N	Reproduction threshold	120
--repro-cost N	Reproduction cost	70
--architecture TYPE	single / dual-same / dual-split / dual-kahneman	dual-kahneman
--experiment-id ID	Identifier for logs	-
--config-dump	Show config as JSON and exit	-
--model MODEL	haiku / sonnet	sonnet
--turns N	Number of turns	50
--seed N	Random seed	-
--parallel N	Parallel LLM calls	4

run_experiments.py parameters

Parameter	Description
--next [N]	Run next N pending (default: 1)
--status	Show status and exit
--phase P2	Run a specific phase
--experiment EXP-011c	Run a single experiment
--resume	Run all pending (batch)
--dry-run	Show commands without executing
--plan FILE	Path to experiments.yaml

Phases (roadmap, adapts as we go)

Phase	What we test	Initial experiments
P1	Validation of v4.2c (map + clusters)	3
P2	Yerkes-Dodson (pressure)	5
P3	Architecture (phase transition)	4
P4	Emergent parenting	3
P5	Model phenotypes	4
P6	Long evolution (200t)	2

What the orchestrator does for each experiment

1. Reads config from experiments.yaml (merge: defaults < phase < experiment)
2. Builds CLI command for arena.py
3. Runs subprocess, timeout 2 hours
4. Analyzes JSONL
5. Saves to logs/experiments/EXP-XXX/ (config.json, analysis.json, console.txt)
6. Updates experiment_results.json
7. Commits to git
8. On error -- retries 2 times, 30s backoff

Analysis results

For each experiment computes:

• Shannon entropy (action distribution diversity)
• Social action % (TRADE + COMMUNICATE + REPRODUCE)
• MOVE+GATHER % (survival focus)
• GATHER success rate (v4.2c: do agents understand the map)
• MOVE % (migration to clusters)
• Dual-system distribution (panic/normal/strategic %)
• Parent-child trades
• NAP detection (alliance/pact/peace keywords)
• Population dynamics (start/end/max/min)
• Reproductions count
• Max generation reached

Troubleshooting

Problem	Solution
pyyaml not found	pip3 install pyyaml
Timeout on 200t experiment	Increase timeout in run_experiments.py (7200 -> 14400)
Rate limit from API	Decrease --parallel (4 -> 2)
Cannot find log	Check that arena.py creates a file in logs/
Git commit failed	Check that you're on main, no conflicts

Related skills

• git-workflow -- commit procedure