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Dynamic landscape models of coevolutionary games.

Hendrik Richter1

  • 1HTWK Leipzig University of Applied Sciences, Faculty of Electrical Engineering and Information Technology, Postfach 301166, D-04251 Leipzig, Germany.

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|February 28, 2017
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Summary
This summary is machine-generated.

This study introduces dynamic fitness landscapes to model coevolutionary games like Prisoner's Dilemma and Snowdrift. It links landscape properties to game dynamics, offering a new analysis tool for strategy and network evolution.

Keywords:
Coevolutionary gamesFixation propertiesLandscape analysisLandscape measures

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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.