Validation and Inference of Agent Based Models
This work addresses the challenge of parameter inference and validation for ABMs, which are used to simulate complex systems, but it appears incremental as it investigates and compares existing ABC algorithms.
The paper tackles the problem of conducting inference for Agent Based Models (ABMs) where likelihood functions are intractable, using Approximate Bayesian Computation (ABC) to validate simulations and infer parameters, with results demonstrated through a pedestrian model in the Hamilton CBD.
Agent Based Modelling (ABM) is a computational framework for simulating the behaviours and interactions of autonomous agents. As Agent Based Models are usually representative of complex systems, obtaining a likelihood function of the model parameters is nearly always intractable. There is a necessity to conduct inference in a likelihood free context in order to understand the model output. Approximate Bayesian Computation is a suitable approach for this inference. It can be applied to an Agent Based Model to both validate the simulation and infer a set of parameters to describe the model. Recent research in ABC has yielded increasingly efficient algorithms for calculating the approximate likelihood. These are investigated and compared using a pedestrian model in the Hamilton CBD.