Estimation of agent-based models using Bayesian deep learning approach of BayesFlow

Abstract

This paper examines the possibility of applying the novel likelihood-free Bayesian inference called BayesFlow proposed by Radev et al. (2020) to the estimation of agent-based models (ABMs). The BayesFlow is a fully likelihood-free approach, which directly approximates a posterior rather than a likelihood function, by learning an invertible probabilistic mapping that implements a Normalizing Flow between parameters and a standard Gaussian variables conditioned by data from simulations. This deep neural network-based method can mitigate the trilemma in the existing methods that all of the following three ?higher flexibility, lower computational cost, and smaller arbitrariness cannot be achieved at the same time. As a result of the experiments, BayesFlow certainly achieved the superior accuracies in the validation task of recovering the ground-truth values of parameters from the simulated datasets, in case of a minimal stock market ABM. The method did not involve any extensive search of the hyperparameters or handcrafted pre-selections of summary statistics, and took a significantly shorter computational time than an existing non-parametric MCMC approach.