# Validation and Inference of Agent Based Models

arXiv: https://arxiv.org/abs/2107.03619 · HTML: https://mlsanity.com/paper/2107.03619/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 30 references that are in the map.
- First posted on arXiv 8 Jul 2021.

## Builds on (most cited first, 2 of 2)

- [Bayesian Indirect Inference Using a Parametric Auxiliary Model](https://mlsanity.com/paper/1505.03372/) (2015)
- [A practical efficient and effective method for the Hamiltonian cycle problem that runs on a standard computer](https://mlsanity.com/paper/1701.03136/) (2017)

## Abstract (arXiv)

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.