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On Playing with Emotion: A Spatial Evolutionary Variation of the Ultimatum Game.

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Summary

This study models bargaining using emotions in the Ultimatum Game. Joyful agents dominated, accumulating more wealth and promoting fairness, while other emotions led to less successful negotiations.

Keywords:
Ultimatum Gameemotional expressionevolutionary gameinformation entropypopulation dynamicssocial networkspatial game

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Area of Science:

  • Behavioral Economics
  • Computational Social Science
  • Game Theory

Background:

  • The Ultimatum Game models social bargaining.
  • Agent-based models simulate complex social interactions.
  • Basic emotions can influence decision-making in economic games.

Purpose of the Study:

  • To evaluate agent-based strategies in a spatial Ultimatum Game.
  • To determine the impact of basic emotions (anger, fear, joy, sadness, surprise) on negotiation outcomes.
  • To analyze the emergence of fairness through simulations.

Main Methods:

  • Agent-based modeling of a modified spatial Ultimatum Game.
  • Simulation of five emotion-inspired strategies: anger, fear, joy, sadness, surprise.
  • Computation of population prevalence, accumulated money, and information entropy.

Main Results:

  • Joyful agents were the most prevalent and accumulated the most money.
  • Simulations showed a tendency towards fairness emerging from interactions.
  • Information entropy analysis revealed dynamics of population diversity and wealth distribution.

Conclusions:

  • Joyful strategies appear dominant in this simulated social network.
  • The spatial Ultimatum Game with emotion-based agents can lead to the emergence of fairness.
  • Agent-based modeling provides insights into the evolution of social behaviors and economic decision-making.