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How to compare policy interventions across shared seeds with Trustworthy Agent Simulation

Run the policy suite on fixed seeds and read means, errors and 95% intervals.

  • Research
  • Advanced
  • 45 minutes
  • From the README
  • Any agent

Who it is forA policy researcher who wants to test whether a subsidy or source disclosure changes town outcomes, and needs to know if the difference is more than noise.

The problemOne simulation run cannot show whether a policy effect is stable, and single numbers hide the spread across runs.

The outcomeA batch output with runs.csv, summary.json and paired_effects.json covering no intervention, subsidy, source disclosure and their combination, using seeds 11, 22 and 33.

What you need first

  • Python 3.12 or 3.13 installed, with access to the python3.12 command on Linux
  • A terminal opened at the repository root
  • Internet access to install locked dependencies with uv
  • For real model runs only: an API key and a Chat Completions compatible endpoint with tool calling

Steps

  1. Complete the install steps from the README Linux section so .venv exists
  2. Run .venv/bin/python -m tass.cli batch --config configs/town.yaml --output runs/policies --suite policy --seeds 11,22,33 (inferred, check the README)
  3. Open runs.csv in the batch output for one row per independent run
  4. Read summary.json for means and standard errors, and paired_effects.json for Student-t 95% intervals

Prompt for your agent

In the apromisedland/trustworthy-agent-simulation repo, run the policy batch suite with seeds 11,22,33 into runs/policies. Report the mean and 95% interval for each condition from the batch outputs. Verify that a single-seed result is not presented as an interval, since the README says one run does not yield one.

Caveat. The README says baseline policies need not reproduce real model behavior, so these results are not evidence of real policy validity.

Use this in your AI agent

Ask your agent

Using the Deduped MCP, get the setup steps for apromisedland/trustworthy-agent-simulation and "Compare policy interventions across shared seeds"

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