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The HoneyComb Paradigm for Research on Collective Human Behavior
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Published on: January 19, 2019

A general framework for analysing multiplayer games in networks using territorial interactions as a case study.

Mark Broom1, Jan Rychtář

  • 1Centre for Mathematical Science, City University London, Northampton Square, London EC1V 0HB, UK. mark.broom@city.ac.uk

Journal of Theoretical Biology
|March 13, 2012
PubMed
Summary

This study introduces a new framework for modeling evolutionary dynamics in structured populations, moving beyond pairwise interactions to include multiplayer dynamics in territorial animal competition. This advances evolutionary graph theory by accommodating more complex ecological interactions.

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Area of Science:

  • Evolutionary Biology
  • Theoretical Ecology
  • Game Theory

Background:

  • Evolutionary graph theory models evolution on structured populations but is limited to pairwise interactions.
  • Many real-world animal interactions, especially territorial competition, involve multiple individuals or groups.

Purpose of the Study:

  • To present a more general modeling framework for interactions in structured populations.
  • To focus on competition among territorial animals with overlapping home ranges and variable group sizes.
  • To extend evolutionary game theory to accommodate multiplayer interactions in ecological contexts.

Main Methods:

  • Development of a novel modeling framework for structured populations.
  • Incorporation of territoriality and overlapping 'home ranges' to define interaction structures.
  • Embedding various evolutionary games (e.g., Prisoner's Dilemma, Hawk-Dove) within this framework.

Main Results:

  • The proposed framework accommodates interactions involving various group sizes, unlike traditional pairwise models.
  • It allows for the integration of diverse evolutionary game outcomes into a spatially structured context.
  • Identifies key differences and advancements compared to standard evolutionary graph theory.

Conclusions:

  • This generalized framework offers a more realistic approach to modeling evolution in structured populations.
  • It is particularly useful for understanding complex ecological dynamics like territorial competition.
  • Provides a foundation for future research into multiplayer evolutionary games in spatial contexts.