Dynamic landscape models of coevolutionary games

Area of Science:

• Evolutionary Game Theory
• Computational Biology
• Complex Systems

Background:

• Coevolutionary games involve dynamic strategies and interaction networks.
• Player payoffs are often interpreted as fitness in evolutionary models.
• Existing models may not fully capture the interplay between strategy and network evolution.

Purpose of the Study:

• To propose and utilize dynamic fitness landscapes as a mathematical framework for analyzing coevolutionary game dynamics.
• To investigate the relationship between landscape properties and evolutionary outcomes in games like Prisoner's Dilemma and Snowdrift.
• To provide an alternative analytical tool for understanding strategy and network coevolution.

Main Methods:

• Interpreting player payoffs as fitness to construct dynamic landscape models.
• Applying the framework to Prisoner's Dilemma (PD) and Snowdrift (SD) games with birth-death (BD) and death-birth (DB) updating rules.
• Computing and analyzing landscape measures (modality, ruggedness, information content) and game dynamics quantifiers (fixation properties, network properties).

Main Results:

• Dynamic fitness landscapes offer a novel perspective on coevolutionary game dynamics.
• Established quantitative relationships between landscape measures and fixation probabilities, fixation times, and network properties.
• Demonstrated the applicability of the dynamic landscape approach to PD and SD games under different updating schemes.

Conclusions:

• Dynamic fitness landscapes provide a powerful mathematical tool for studying coevolutionary dynamics.
• Landscape properties are significant predictors of evolutionary outcomes in games with evolving strategies and networks.
• This approach enhances our understanding of evolutionary processes in complex adaptive systems.