Sensitivity to Initial Conditions in Agent-Based Models

In the last thirty years, agent-based modelling has become a well-known technique for studying and simulating dynamical systems. Still, there are some open issues to be addressed. One of these is the substantial absence of studies about the sensitivity to initial conditions, that is the effect of small variations at the beginning of simulation on the macro-level behaviour of the model. The goal of this preliminary work is to explore how a single modification on one agent affects the evolution of the simulation. Through the analysis of two deterministic models (a simple market model and Reynolds' flocking model), we obtain two main results. First, we observe that the impact of the variation of a single initial condition on the simulation behaviour is high in both models. Second, there is evidence of an at least qualitative relation between some general agent-based model settings (numerosity of agents in the model and rate of connections between agents) and the sensitivity to the modified initial condition. We conclude that at least some significant classes of agent-based models are affected by a high sensitivity to initial conditions that have a negative effect on the predictive power of simulations.