scikit-agent is for agent-based modeling in Python.
- Simple and efficient
- Built on NumPy, SciPy, and Torch
- Open source, commercially usable
It goes by many names: multi-agent systems, agent-based modeling, computational economics. This library aims to make it easy to develop new models, then solve and estimate them using reliable, efficient algorithms.
Functionalities (will) include:
- Building dynamic models from blocks of structural equations
- Solving for optimal decision rules using deep learning
- Structurally estimating model parameters using empirical data
- Displaying model results and predictions
Our goal is for scikit-agent to be for computational social scientific
modeling and statistics what scikit-learn is for machine learning.
- 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.