ArXiv TLDR

Overcoming unfairness via repeated interactions in mini-ultimatum game

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2604.03625

Prosanta Mandal, Arunava Patra, Sagar Chakraborty

nlin.AOecon.THphysics.bio-phq-bio.PE

TLDR

This paper models how repeated interactions in a mini-ultimatum game promote fairness, showing specific strategies maintain it against unfairness.

Key contributions

  • Theoretically models fairness evolution in a repeated mini-ultimatum game using reactive strategies.
  • Investigates resilience of fair reactive strategies against unfair mutants via two-species ESS.
  • Identifies a critical game length influencing how fairness is promoted and maintained.
  • Develops a two-population stochastic dynamics model for long-term fairness sustenance.

Why it matters

This study fills a gap by theoretically modeling fairness evolution in repeated interactions, moving beyond cooperation. It provides insights into how specific strategies and game lengths can foster and maintain fairness, relevant for understanding social behavior.

Original Abstract

Repeated interactions are ubiquitous and known to promote social behaviour. While research often focuses on cooperation in the Prisoner's Dilemma, experimental evidence suggests repeated interactions also foster fairness. This study addresses a gap in the literature by theoretically modelling the evolution of fairness within a repeated mini-ultimatum game. Specifically, we construct a repeated-game framework where offerers and accepters interact using reactive strategies. We then investigate whether fair reactive strategy pairs are resilient against unfair mutants in a two-species population. By analyzing short-term evolutionary stability via the concept of two-species evolutionary stable strategy, we identify a critical effective game length: below this value, fairness is promoted by offerers and accepters who comply with their partner's past actions. Above this critical value, fairness is maintained by `complier' offerers and fair accepters. We also show that specific reactive strategies effectively facilitate the emergence and sustenance of fairness in long-term mutation-selection dynamics. To this end, we develop a two-population stochastic dynamics model -- a generalization of classical adaptive dynamics -- that accounts for finite population sizes and non-local mutants in the reactive strategy space.

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