Transitions & Dynamics

Transitions dictate how agents migrate between states at each interaction step.

Transition Probability Matrices

A stationary transition matrix is represented as a 2D NumPy array of shape (K, K), where each row must sum to 1.0:

import numpy as np
from behaviorsim import Simulator, State

states = [State("A"), State("B")]

# Row 0: Transitions from A -> [A (80%), B (20%)]
# Row 1: Transitions from B -> [A (10%), B (90%)]
matrix = np.array([
    [0.8, 0.2],
    [0.1, 0.9]
])

sim = Simulator(states=states, transition_matrix=matrix)

Dynamic Transition Rules

Real behaviors are frequently non-stationary: an agent transitions only when a metric crosses a threshold. BehaviorSim supports TransitionRule objects that evaluate conditions over historical feature values.

Supported Operators & Aggregations