Replacing Rules by Neural Networks A Framework for Agent-Based Modelling

Georg Jäger

doi:10.3390/bdcc3040051

Abstract

Agent-based modelling is a successful technique in many different fields of science. As a bottom-up method, it is able to simulate complex behaviour based on simple rules and show results at both micro and macro scales. However, developing agent-based models is not always straightforward. The most difficult step is defining the rules for the agent behaviour, since one often has to rely on many simplifications and assumptions in order to describe the complicated decision making processes. In this paper, we investigate the idea of building a framework for agent-based modelling that relies on an artificial neural network to depict the decision process of the agents. As a proof of principle, we use this framework to reproduce Schelling’s segregation model. We show that it is possible to use the presented framework to derive an agent-based model without the need of manually defining rules for agent behaviour. Beyond reproducing Schelling’s model, we show expansions that are possible due to the framework, such as training the agents in a different environment, which leads to different agent behaviour.

Highlights

The main idea behind agent-based modelling [1] is to describe a system by using its constituent parts as a starting point
Agent-based modelling is relevant for different scientific disciplines, in which systems can be seen as comprised of a large number of interacting entities
A prominent example would be tax compliance and tax evasion [18], which can be better understood by models that see this behaviour not as a external feature of the system, but as something that emerges on the agent-level and is disseminated via a network

Summary

Introduction

The main idea behind agent-based modelling [1] is to describe a system by using its constituent parts as a starting point. The goal of each agent is clear, but due to their interactions interesting effects can emerge Another important area for agent-based models are traffic simulations [3,4,5]. The goals of the agents are clear here (getting from one point to an other point), but, due to interactions with other agents, they must behave differently from how they would if they could use all roads for themselves, leading to congestion or even stop-and-go traffic Somewhat related to this field is the field of city planning [6], where models with mobile agents, which have various needs, can give crucial input about how efficient a specific plan for a city is. In the fields of ecology [10] and complexity research [11], agent-based models can be used Another important application of agent-based modelling is computational economics [12]. A prominent example would be tax compliance and tax evasion [18], which can be better understood by models that see this behaviour not as a external feature of the system, but as something that emerges on the agent-level and is disseminated via a network

Methods

Results

Discussion

Conclusion