agentsociety-hypothesis

Research

Use when defining or revising research hypotheses, experiment groups, or comparison structure after literature review.

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Prompt to paste

I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/tsinghua-fib-lab/AgentSociety/blob/HEAD/extension/skills/agentsociety-hypothesis/v1.0.0/SKILL.md

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Hypothesis Management

Manage research hypotheses for AgentSociety experiments. Each hypothesis defines experiment groups (control/treatment) and specifies required agent classes and environment modules.

When to Use

User says "hypothesis", "research question", "experiment groups", or "control vs treatment"
Literature search is done and user wants to formulate testable claims
User needs to define what to compare in a simulation experiment

Do NOT use when:

No literature search has been done yet (use literature-search first)
User wants to run an existing experiment (use experiment-config)

Quick Reference

Use the Python interpreter from .env. See CLAUDE.md for setup. Run commands from the workspace root through .agentsociety/bin/ags.py.

Action	Command
List hypotheses	`$PYTHON_PATH .agentsociety/bin/ags.py hypothesis list [--workspace PATH] [--json]`
Add hypothesis	`$PYTHON_PATH .agentsociety/bin/ags.py hypothesis add --description TEXT --rationale TEXT --groups JSON... --agent-classes TYPE... --env-modules TYPE... [--skip-module-validation] [--json]`
Get hypothesis	`$PYTHON_PATH .agentsociety/bin/ags.py hypothesis get --hypothesis-id ID [--json]`
Delete hypothesis	`$PYTHON_PATH .agentsociety/bin/ags.py hypothesis delete --hypothesis-id ID`
Discover modules if needed	`$PYTHON_PATH .agentsociety/bin/ags.py scan-modules list --short`
Inspect one module	`$PYTHON_PATH .agentsociety/bin/ags.py scan-modules info --type agent --name PersonAgent`

Workflow

digraph hypothesis_flow {
    rankdir=LR;
    node [shape=box, style=filled, fillcolor="#E8F4FD"];
    topic [label="read TOPIC.md\nand literature index"];
    clarify [label="clarify question\nand comparison goal"];
    names [label="agent/env names known?"];
    scan [label="scan-modules\noptional helper"];
    groups [label="define control\nand treatment groups"];
    write [label="write HYPOTHESIS.md\nand SIM_SETTINGS.json"];
    sync [label="sync TOPIC.md"];

    topic -> clarify -> names;
    names -> groups [label="yes"];
    names -> scan [label="no"];
    scan -> groups;
    groups -> write -> sync;
}

Module Selection

Every hypothesis must specify at least one agent class and one environment module. If the exact names are uncertain, use scan-modules to discover or validate them before writing SIM_SETTINGS.json.

Group JSON Format:

{
  "name": "treatment",
  "group_type": "treatment",
  "description": "Agents with high social capital scores",
  "agent_selection_criteria": "agents whose social_connections > median"
}

Common Module Combinations:

Domain	Agent Classes	Environment Modules
Social simulation	`PersonAgent`	`SimpleSocialSpace GlobalInformationEnv`
Economic behavior	`LLMDonorAgent`	`EconomySpace`
Game theory	`PrisonersDilemmaAgent`	`PrisonersDilemmaEnv`

Output Structure

hypothesis_{id}/
  HYPOTHESIS.md         # Hypothesis description and groups
  SIM_SETTINGS.json     # Agent and environment module configuration
  experiment_1/
    EXPERIMENT.md
  experiment_2/
    EXPERIMENT.md

After adding/modifying/deleting a hypothesis, update TOPIC.md with the hypothesis overview and link.

Common Mistakes

Mistake	Fix
Writing `SIM_SETTINGS.json` with uncertain module names	Run `scan-modules` to confirm valid class and module names
Omitting agent classes or env modules	Both are required -- hypothesis creation will fail
Using invalid module names	Copy exact names from `ags.py scan-modules list --short` output
Forgetting to update TOPIC.md after changes	Always sync TOPIC.md with hypothesis overview
Defining only one group	Define at least a control and a treatment group for comparison
Not providing rationale	`--rationale` grounds the hypothesis in literature; do not skip it

Pipeline Position

Predecessors: literature-search Optional inputs: web-research (supplementary non-academic context) Optional helpers: scan-modules (when module names are unknown or need validation) Successors: experiment-config

Progress Tracking

After adding a hypothesis successfully:

$PYTHON .agentsociety/bin/ags.py research-pipeline update-stage hypothesis completed --metadata '{"hypotheses_count": N}'