How strategy environment and wealth shape altruistic behaviour: cooperation rules affecting wealth distribution in dynamic networks

Abstract

Societies rely on individual contributions to sustain public goods that benefit the entire community. Several mechanisms, that specify how individuals change their decisions based on past experiences, have been proposed to explain how altruists are not outcompeted by selfish counterparts. A key aspect of such strategy updates involves a comparison of an individual's latest payoff with that of a random neighbour. In reality, both the economic and social milieu often shapes cooperative behaviour. We propose a new decision heuristic, where the propensity of an individual to cooperate depends on the local strategy environment in which she is embedded as well as her wealth relative to that of her neighbours. Our decision-making model allows cooperation to be sustained and also explains the results of recent experiments on social dilemmas in dynamic networks. Final cooperation levels depend only on the extent to which the strategy environment influences altruistic behaviour but are largely unaffected by network restructuring. However, the extent of wealth inequality in the community is affected by a subtle interplay between the environmental influence on a person's decision to contribute and the likelihood of reshaping social ties, with wealth-inequality levels rising with increasing likelihood of network restructuring in some situations.

Network rewiring promotes cooperation in an aspirational learning model

“…The network rewiring model used in this section was inspired by behavioural experiments 7 performed to analyse evolution of cooperation and wealth distribution using PGG. Each of the ρ fraction of the existing links are maintained or broken based on the action of one of the two participating players, chosen randomly 4,7 . If the partner of the decision-maker had defected in the previous round, the decision-maker, regardless of her own action in the previous round, chooses to break the connection with probability p b .…”

“…Thus, allowing players to access information about their neighbors' actions in the previous round induce individuals who have cooperated in the past-round to establish more connections than past defectors. Each of the re fraction of the existing links are maintained or broken based on the action of one of the two participating players, chosen randomly 4 . If the partner of the decision-maker had defected in the previous round, the decision-maker, regardless of her own action in the previous round, chooses to break the connection with probability p b .…”

“…Many of the mechanisms 2,3 that have been proposed for explaining the sustenance of cooperators are reactive in the sense that they specify an appropriate response to the altruistic or selfish actions of others. The response itself can change through individual learning that can be conditioned on a variety of factors like the latest payoff, strategy and wealth environment [4][5][6] , social norms [7][8][9][10][11][12][13][14] and even personal values. However, most evolutionary game-theoretic models emphasize the importance of latest-payoff in shaping the evolution of individual decisions which typically occurs through the comparison of the focal player's payoff with that of a randomly chosen neighbor 15,16 .…”

Inferring to cooperate: Evolutionary games with Bayesian inferential strategies

“…In view of the above, it is quite natural that one takes up the endeavour of not only considering different strategy update rules [49][50][51][52][53][54][55], that are not just dependent on the payoff [51], but also allow for the possibility that individuals may learn the appropriate rule (model for strategy update) from the experience gained through repeated interactions. In other words, a more complex strategy could involve adopting a strategy contingent on the belief about environmental state of player.…”