Dynamic landscape models of coevolutionary games
This provides an alternative mathematical tool for analyzing coevolutionary games, which is incremental as it applies existing landscape concepts to a specific domain.
The authors tackled the modeling of coevolutionary games where players update strategies and interaction networks, proposing dynamic landscape models based on fitness interpretations for Prisoner's Dilemma and Snowdrift games. They established relations between landscape properties (e.g., modality, ruggedness) and game dynamics quantifiers like fixation probabilities and network characteristics.
Players of coevolutionary games may update not only their strategies but also their networks of interaction. Based on interpreting the payoff of players as fitness, dynamic landscape models are proposed. The modeling procedure is carried out for Prisoner's Dilemma (PD) and Snowdrift (SD) games that both use either birth--death (BD) or death--birth (DB) strategy updating. The main focus is on using dynamic fitness landscapes as a mathematical model of coevolutionary game dynamics. Hence, an alternative tool for analyzing coevolutionary games becomes available, and landscape measures such as modality, ruggedness and information content can be computed and analyzed. In addition, fixation properties of the games and quantifiers characterizing the interaction networks are calculated numerically. Relations are established between landscape properties expressed by landscape measures and quantifiers of coevolutionary game dynamics such as fixation probabilities, fixation times and network properties.