scikit-agent¶

scikit-agent is a scientific Python toolkit for agent-based economic modeling and multi-agent systems design. It provides a unified interface for creating, solving, and simulating economic models using modern computational methods including deep learning as well as more traditional numerical techniques.

Our goal is for scikit-agent to be for computational social scientific modeling and statistics what scikit-learn is for machine learning.

Key Features¶

Built on Scientific Python and Torch for easy integration with the Python ecosystem
Modular modeling system. Construct multi-agent environments from modular blocks of structural equations.
Solution algorithms including deep learning methods.
Simulation tools for generating synthetic data and running policy experiments

Installation¶

pip install scikit-agent

For development installation:

git clone https://github.com/scikit-agent/scikit-agent.git
cd scikit-agent
pip install -e ".[dev,docs]"

Quick Example¶

import skagent as ska
from skagent.models.consumer import cons_problem, calibration

# A consumption-saving model: a consumption block chained with a
# tick block that carries end-of-period assets into next period's
# capital. The simulator constructs the shock distributions from
# the calibration internally.
model = cons_problem

# Define simple decision rule
decision_rules = {"c": lambda m: 0.9 * m}

# Run simulation
simulator = ska.MonteCarloSimulator(
    calibration=calibration,
    block=model,
    dr=decision_rules,
    initial={"k": 1.0},
    agent_count=1000,
    T_sim=50,
)

simulator.initialize_sim()
results = simulator.simulate()