Data-Driven Automatic Calibration for Validation of Agent-Based Social Simulations

Abstract

Though agent based models are used in many domains, the usage have been either very abstract model for conceptual experiments or very detailed models with huge engineering efforts in their modeling and calibration. One reason of this limited usage comes from the difficulties in calibrating and validating the model with observed data because the models are very generative in its nature with many hand-picked parameters. This paper presents a noble framework of augmenting machine learning techniques to agent-based models for better calibration and validation. The framework identifies periods of deviation between the simulation and the observation with hierarchical Dirichlet process hidden Markov Model, or HDP-HMM, and the framework automatically calibrates the temporal macro parameters by searching parameter spaces with more likelihoods of validation. After iterations of this framework, our experiments demonstrated sucessful validations on a hypothestical simple segregation model as well as a real world real estate model. This framework is generally usable in any agent based models with temporal macro parameters, which could be true in many existing models.