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Statistical fluctuations in population bargaining in the ultimatum game: static and evolutionary aspects
Roberto da Silva1, Gustavo A Kellermann, Luis C Lamb
1Institute of Informatics, Federal University of Rio Grande do Sul, 91501-970 Porto Alegre RS, Brazil. rdasilva@inf.ufrgs.br
Journal of Theoretical Biology
|June 4, 2009
Summary
In ultimatum game simulations, evolutionary dynamics lead to fair wealth distribution. Strategies adapt to limit greed, increasing average payoffs and reducing variance over time.
Area of Science:
- Behavioral economics
- Game theory
- Agent-based modeling
Background:
- The ultimatum game involves a proposer and responder negotiating resource division.
- Rejection of an offer results in no payoff for either player.
- Heterogeneous populations exhibit complex emergent behaviors.
Purpose of the Study:
- To analyze emergent strategies and payoff distributions in heterogeneous populations using the ultimatum game.
- To investigate population evolution and strategy adaptation based on received payoffs.
- To examine the impact of a specific evolutionary dynamic on economic outcomes.
Main Methods:
- Exact analytical methods for calculating payoff moments in static populations.
- Monte Carlo simulations to model population evolution with adaptive agents.
- Analysis of strategy parameter changes (proposing and accepting) based on payoffs.
Main Results:
- A stationary state is reached where accumulated payoffs (wealth) are fairly distributed.
- Evolutionary dynamics show an increase in average payoffs over time.
- A decrease in payoff variance is observed, indicating reduced inequality.
Conclusions:
- The studied evolutionary dynamic, incorporating limited greed, promotes economic fairness and efficiency.
- Adaptive strategies in heterogeneous populations can lead to stable, equitable outcomes.
- Simulations and analytical methods confirm the emergent properties of the population model.
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