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The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm
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Evolutionary dynamics of complex multiple games.

Vandana Revathi Venkateswaran1, Chaitanya S Gokhale1

  • 1Research Group for Theoretical Models of Eco-evolutionary Dynamics, Department of Evolutionary Theory, Max Planck Institute for Evolutionary Biology , August Thienemann Strasse 2, 24306 Plön , Germany.

Proceedings. Biological Sciences
|June 27, 2019
PubMed
Summary
This summary is machine-generated.

Evolutionary game theory now includes multiple interactions and players. Studying single games is insufficient; a broader approach is needed for accurate evolutionary dynamics.

Keywords:
evolutionary game theoryfinite populationmultiplayer gamesmultiple games

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Area of Science:

  • Evolutionary biology
  • Game theory
  • Mathematical modeling

Background:

  • Evolutionary game theory traditionally models single interactions between individuals.
  • Organisms engage in numerous simultaneous intraspecies and interspecies interactions.
  • Existing models lack the complexity to represent these multifaceted natural interactions.

Purpose of the Study:

  • To develop a theoretical framework for multiple game dynamics (MGD) involving numerous players and strategies.
  • To analyze the emergent dynamics arising from the interplay of multiple games.
  • To provide a more realistic model for evolutionary processes in natural settings.

Main Methods:

  • Formulation of a replicator equation for MGD with arbitrary numbers of players and strategies.
  • Analysis of game dynamics when individual games possess more than two strategies.
  • Calculation of fixation probability for finite populations within the MGD framework.

Main Results:

  • The dynamics of multiple games cannot be predicted by analyzing individual games in isolation, especially with more than two strategies.
  • Trajectories in MGD can exhibit different limiting behaviors compared to single-game models.
  • Fixation probability calculations reveal crucial stochastic properties in finite populations.

Conclusions:

  • Modeling interactions through a single game can be misleading; the broader context of multiple interactions is essential.
  • A comprehensive understanding of evolutionary processes requires the development of evolutionary game(s) theory.
  • The proposed MGD framework offers a more accurate and nuanced approach to studying complex biological interactions.