Analyzing coevolutionary games with dynamic fitness landscapes

TL;DR

This paper introduces a landscape-based framework to analyze coevolutionary games, capturing how strategy and network changes create dynamic fitness landscapes, with experiments on PD and SD games using different updating rules.

Contribution

It presents a novel approach to model coevolutionary game dynamics through dynamic landscape models, linking game theory with landscape analysis.

Findings

Coevolutionary games generate dynamic, evolving fitness landscapes.

Landscape modality and ruggedness vary with game type and update rule.

Numerical experiments reveal landscape characteristics for PD and SD games.

Abstract

Coevolutionary games cast players that may change their strategies as well as their networks of interaction. In this paper a framework is introduced for describing coevolutionary game dynamics by landscape models. It is shown that coevolutionary games invoke dynamic landscapes. Numerical experiments are shown for a prisoner's dilemma (PD) and a snow drift (SD) game that both use either birth-death (BD) or death-birth (DB) strategy updating. The resulting landscapes are analyzed with respect to modality and ruggedness