Evolutionary game theory using agent-based methods

Authored by Christoph Adami, Jory Schossau, Arend Hintze

Date Published: 2016

DOI: 10.1016/j.plrev.2016.08.015

Sponsors: United States National Science Foundation (NSF) United States Department of Energy

Platforms: No platforms listed

Model Documentation: Other Narrative Mathematical description

Model Code URLs: Model code not found

Abstract

Evolutionary game theory is a successful mathematical framework geared towards understanding the selective pressures that affect the evolution of the strategies of agents engaged in interactions with potential conflicts. While a mathematical treatment of the costs and benefits of decisions can predict the optimal strategy in simple settings, more realistic settings such as finite populations, non-vanishing mutations rates, stochastic decisions, communication between agents, and spatial interactions, require agent-based methods where each agent is modeled as an individual, carries its own genes that determine its decisions, and where the evolutionary outcome can Only be ascertained by evolving the population of agents forward in time. While highlighting standard mathematical results, we compare those to agent-based methods that can go beyond the limitations of equations and simulate the complexity of heterogeneous populations and an ever-changing set of interactors. We conclude that agent-based methods can predict evolutionary outcomes where purely mathematical treatments cannot tread (for example in the weak selection strong mutation limit), but that mathematics is crucial to validate the computational simulations. (C) 2016 Elsevier B.V. All rights reserved.

Tags

Public-goods games Altruistic punishment Iterated prisoners-dilemma Zero-determinant strategies Rock-paper-scissors Stochastic strategies Mutational robustness Beneficial mutations Asexual populations Stable strategies