Invasion of Optimal Social Contracts

Author:

Lütz Alessandra F.1ORCID,Amaral Marco Antonio2ORCID,Braga Ian1ORCID,Wardil Lucas1ORCID

Affiliation:

1. Departamento de Física, Universidade de Minas Gerais, Belo Horizonte 31270-901, Brazil

2. Instituto de Humanidades, Artes e Ciências, Universidade Federal do Sul da Bahia, Teixeira de Freitas 45988-058, Brazil

Abstract

The stag-hunt game is a prototype for social contracts. Adopting a new and better social contract is usually challenging because the current one is already well established and stable due to sanctions imposed on non-conforming members. Thus, how does a population shift from the current social contract to a better one? In other words, how can a social system leave a locally optimum configuration to achieve a globally optimum state? Here, we investigate the effect of promoting diversity on the evolution of social contracts. We consider group-structured populations where individuals play the stag-hunt game in all groups. We model the diversity incentive as a snowdrift game played in a single focus group where the individual is more prone to adopting a deviant norm. We show that a moderate diversity incentive is sufficient to change the system dynamics, driving the population over the stag-hunt invasion barrier that prevents the global optimum being reached. Thus, an initial fraction of adopters of the new, better norm can drive the system toward the optimum social contract. If the diversity incentive is not too large, the better social contract is the new equilibrium and remains stable even if the incentive is turned off. However, if the incentive is large, the population is trapped in a mixed equilibrium and the better social norm can only be reached if the incentive is turned off after the equilibrium is reached. The results are obtained using Monte Carlo simulations and analytical approximation methods.

Funder

Brazilian Research Agency CNPq

Brazilian Research Agency CAPES

Minas Gerais State Agency for Research and Development FAPEMIG

Publisher

MDPI AG

Subject

Applied Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability

Reference56 articles.

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