Ecosystem

scikit-agent is an open source project that takes part in a larger ecosystem.

We see scikit-agent as a new entrant into the Scientific Python ecosystem, and look to that community for guidance and best practices.

Our scientific domain is the converging fields of computational economics, agent-based modeling, and multi-agent systems.

We believe that high quality, open source tooling, using the best available techniques from computer science, can advance these social scientific fields.

Science

scikit-agent is the product of scientific research.

Publications

Our work on scikit-agent is documented in, and contributes to, scientific publications. Here are some of those publications.

  • Benthall, S. and Lujan, A., 2026. The Design and Composition of Structural Causal Decision Processes. arXiv preprint arXiv:2605.02681.

References

We have drawn on many prior works when designing scikit-agent. Some key references include:

  • Axtell, R.L. and Farmer, J.D., 2025. Agent-based modeling in economics and finance: Past, present, and future. Journal of Economic Literature, 63(1), pp.197-287.

  • Hammond, L., Fox, J., Everitt, T., Carey, R., Abate, A. and Wooldridge, M., 2023. Reasoning about causality in games. Artificial Intelligence, 320, p.103919.

  • Maliar, L., Maliar, S. and Winant, P., 2021. Deep learning for solving dynamic economic models. Journal of Monetary Economics, 122, pp.76-101.

We will add more references as we incorporate more scientific results and algorithms into the library.

Funding

This work was funded in part by:

  • National Science Foundation #2131532

  • National Science Foundation #2131533

  • Future of Life Foundation

Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the sponsors.