Evolution of altruistic punishments among heterogeneous conditional cooperators

Balaraju Battu

doi:10.1038/s41598-021-89563-z

Abstract

It has been known that altruistic punishments solve the free rider problem in public goods games. Considering spatial structure and considering pure strategies significant advances have been made in understanding the evolution of altruistic punishments. However, these models have not considered key behavior regularities observed in experimental and field settings, where the individuals behave like conditional cooperators who are more willing to donate and are also more willing to punish free riders. Considering these behavioral regularities, without imposing a spatial structure on the population, I propose an evolutionary agent-based model in which agents behave like conditional cooperators, each agent’s donation conditional on the difference between the number of donations in the past and the threshold value and the propensity value of the agent. Altruistic punishment depends on the difference between the threshold value of the focal agent and the randomly matched another agent. The simulations show that, for certain inflicted costs of punishments, generous altruistic punishments evolve and stabilize cooperation. The results show that, unlike previous models, it is not necessary to punish all free riders equally; it is necessary to do so in the case of the selfish free riders but not in the case of negative reciprocators.

Highlights

The free r iders[20,21], the population can solve the second order free rider problem
Agents starting with arbitrary CCCand β values, donating to the public good by using Eq (1), enforcing altruistic punishments using Eq (2), and updating strategies using Eq (4), for certain punishment costs, meant that altruistic punishment evolved in the population
The results show that lower CCCagents proliferate in the population with higher inflected costs, these individuals donate to the public good and enforce altruistic punishments

Summary

Introduction

The free r iders[20,21], the population can solve the second order free rider problem. There are a few studies set in spatial public goods games, such as conditional p unishments[28] and class dependent s trategies[24], which consider the composition of the population and define punishment strategies, but in these studies the individuals are not conditional cooperators. In the current model, I address how altruistic punishment strategies evolve in the population of heterogeneous conditional cooperators who are more willing to donate and more willing to punish free riders. By considering the behavioral regularities observed in repeated public goods games in the field and experimental settings, I propose an evolutionary agent-based model with a population of heterogeneous conditional cooperators. For certain inflected costs, evolution favors generous altruistic punishment strategies more than strict punishment strategies, i.e., agents punish the occasional free riders with less frequency and the selfish free riders with higher frequency

Methods

Results

Conclusion