Freeborn, David Peter Wallis (2023) Efficiency and Fairness Trade-offs in Two Player Bargaining Games. [Preprint]

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Abstract

Recent work on the evolution of social contracts and conventions has often used models of bargaining games, with reinforcement learning. A recent innovation is the requirement that every strategy must be invented either through through learning or reinforcement. However, agents frequently get stuck in highly-reinforced ``traps'' that prevent them from arriving at outcomes that are efficient or fair to the both players. Agents face a trade-off between exploration and exploitation, i.e. between continuing to invent new strategies and reinforcing strategies that have already become highly reinforced by yielding high rewards. In this paper I systematically study the relationship between rates of invention and the efficiency and fairness of outcomes in two-player, repeated bargaining games. I use a basic reinforcement learning model with invention, and five variations of this model, designed introduce various forms of forgetting, to prioritize more recent reinforcement, or to maintain a higher rate of invention. I use computer simulations to investigate the outcomes of each model. Each models shows qualitative similarities in the relationship between the efficiency and fairness of outcomes, and the relative amount of exploration or exploitation that takes place. Surprisingly, there are often trade-offs between the efficiency and the fairness of the outcomes.

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Item Type:	Preprint
Creators:	Creators Email ORCID Freeborn, David Peter Wallis dfreebor@uci.edu 0000-0002-2117-8145
Keywords:	invention; learning; reinforcement learning; evolution; evolutionary games; game theory; bargaining games; forgetting; evolutionary game theory; optimisation; modeling; efficiency; fairness; pareto efficiency; urn models; social conventions; evolution of social conventions; social contract; evolution of the social contract; evolutionary social contract theory; machine learning;
Subjects:	Specific Sciences > Biology > Evolutionary Theory General Issues > Computer Simulation Specific Sciences > Cultural Evolution Specific Sciences > Economics General Issues > Formal Learning Theory General Issues > Game Theory
Depositing User:	David Freeborn
Date Deposited:	30 Sep 2023 00:21
Last Modified:	30 Sep 2023 00:21
Item ID:	22602
Subjects:	Specific Sciences > Biology > Evolutionary Theory General Issues > Computer Simulation Specific Sciences > Cultural Evolution Specific Sciences > Economics General Issues > Formal Learning Theory General Issues > Game Theory
Date:	29 September 2023
URI:	https://philsci-archive.pitt.edu/id/eprint/22602

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