A Case Study of Agent-Based Models for Evolutionary Game Theory

TL;DR

This paper explores the use of agent-based models as heuristic tools to analyze complex evolutionary game theory scenarios where traditional analytical solutions are difficult to obtain.

Contribution

It introduces an agent-based modeling approach to study complex evolutionary games, providing a practical alternative to intractable analytical solutions.

Findings

Agent-based models can effectively identify evolutionarily stable states in complex games.

The approach offers a heuristic method for analyzing scenarios with relaxed assumptions.

The study demonstrates the applicability of agent-based models in evolutionary game analysis.

Abstract

Evolutionary game theory is a mathematical toolkit to analyse the interactions that an individual agent has in a population and how the composition of strategies in this population evolves over time. While it can provide neat solutions to simple problems, in more complicated situations where assumptions such as infinite population size may be relaxed, deriving analytic solutions can be intractable. In this short paper, we present a game with complex interactions and examine how an agent-based model may be used as a heuristic technique to find evolutionarily stable states.