Comparative Efficiency of Altruism and Egoism as Voting Strategies in Stochastic Environment

TL;DR

This paper compares altruistic and egoistic voting strategies in a stochastic social model, analyzing their efficiency under different distributions and environments, revealing conditions where each strategy excels or fails.

Contribution

It introduces a comparative analysis of altruistic and egoistic voting strategies in stochastic environments, highlighting their effectiveness under various distribution types and environmental conditions.

Findings

Egoistic strategies better prevent extinction in aggressive environments.

Altruistic strategies are more efficient in favorable environments.

Heavy-tailed distributions eliminate the 'pit of losses' paradox.

Abstract

In this paper, we study the efficiency of egoistic and altruistic strategies within the model of social dynamics determined by voting in a stochastic environment (the ViSE model) using two criteria: maximizing the average capital increment and minimizing the number of bankrupt participants. The proposals are generated stochastically; three families of the corresponding distributions are considered: normal distributions, symmetrized Pareto distributions, and Student's $t$-distributions. It is found that the "pit of losses" paradox described earlier does not occur in the case of heavy-tailed distributions. The egoistic strategy better protects agents from extinction in aggressive environments than the altruistic ones, however, the efficiency of altruism is higher in more favorable environments. A comparison of altruistic strategies with each other shows that in aggressive environments, everyone should be supported to minimize extinction, while under more favorable conditions, it is more efficient to support the weakest participants. Studying the dynamics of participants' capitals we identify situations where the two considered criteria contradict each other. At the next stage of the study, combined voting strategies and societies involving participants with selfish and altruistic strategies will be explored.