Estimation of Probability Distribution Representing Agent Heterogeneity in Agent-Based Models

Heterogeneity of agents in agent-based models (ABMs) is generally expressed as parameters of the agents that represent individuals in a population. The heterogeneity of agents is important when analyzing the effect of intervention measures on agents in ABMs. For realistic analysis, it is necessary to estimate agent heterogeneity from empirical data, but there are few studies on estimating agent heterogeneity in ABMs. In this study, we propose a method for estimating agent heterogeneity in ABMs. The proposed method estimates predictive distribution of the agent parameters using a particle filter and a variational Bayesian inference for aggregated observed data obtained from the real world. In order to evaluate the proposed method, we carry out a twin experiment and an empirical analysis using an ABM that represents infectious disease spread. Results of the twin experiment indicate that estimation of predictive distribution becomes stable as the number of particles of the particle filter increases. The results also show that the configuration of prior distribution for hierarchical parameters is important to properly extract the number of the clusters that express heterogeneity. Results of the empirical analysis demonstrate that the proposed method can estimate agent heterogeneity using observed data aggregated from real world population. We find that the proposed method has an advantage over methods based on detailed surveys of individuals in that it estimates the heterogeneity of individuals in a population using easily obtained aggregated observed data.