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Model Zoo Research Examples

ABMForge model zoo examples are small, executable research workflow examples.

They are not intended to be complete empirical studies. Their purpose is to show how ABMForge models can be connected to scenario files, experiment files, validated archives, analysis scripts, and robustness summaries.

Current Research Examples

Threshold-Adoption Reference Study

Path:

examples/reproducible_study/

Run:

python examples/reproducible_study/reproduce.py

This example demonstrates a reviewer-facing ABMForge research bundle:

  • experiment YAML;
  • deterministic multi-seed parameter grid;
  • validated experiment archive;
  • JSON/CSV summary tables;
  • ODD Markdown and JSON documentation;
  • research protocol;
  • artifact manifest;
  • lightweight adoption-curve analysis artifacts.

Wealth Inequality

Path:

examples/model_zoo/wealth_inequality/

Run:

cd examples/model_zoo/wealth_inequality
abmforge run configs/baseline.yaml --archive outputs/baseline_archive --overwrite
abmforge validate outputs/baseline_archive
python analysis/analyze.py outputs/baseline_archive

This example demonstrates:

  • agent wealth state;
  • stochastic transfers;
  • inequality metrics;
  • agent-level recording;
  • archive analysis.

Network Diffusion

Path:

examples/model_zoo/network_diffusion/

Run:

cd examples/model_zoo/network_diffusion
abmforge run configs/baseline.yaml --archive outputs/baseline_archive --overwrite
abmforge validate outputs/baseline_archive
python analysis/analyze.py outputs/baseline_archive

This example demonstrates:

  • network exposure;
  • threshold adoption;
  • parameterized diffusion;
  • model-level and agent-level records;
  • archive analysis.

For each example:

abmforge run configs/baseline.yaml --archive outputs/baseline_archive --overwrite
abmforge validate outputs/baseline_archive
abmforge summarize outputs/baseline_archive --json
python analysis/analyze.py outputs/baseline_archive

For experiments:

abmforge experiment configs/experiment.yaml --archive outputs/experiment_archive --overwrite
abmforge validate outputs/experiment_archive
abmforge summarize outputs/experiment_archive --json
python analysis/analyze.py outputs/experiment_archive

Research Caveat

A valid archive confirms that outputs satisfy ABMForge's archive expectations. It does not prove that the model is scientifically valid or empirically calibrated.

Researchers should still report assumptions, parameter ranges, validation evidence, sensitivity checks, and limitations.