Religion as a Seed Crystal for Altruistic Cooperation

The ability to solve problems of collective action is crucial for economic performance. Growth-fostering behavioral propensities such as respecting property, honoring contracts, or helping others are collectively beneficial but individually costly. The paradigmatic formalization of this rationality gap is provided by the non-iterated Prisoners’ Dilemma, where rational players are locked in at a state of mutual defection while mutual cooperation would be better for everyone. In sporadic, ex-ante anonymous interactions (like in modern large-scale societies), the ‘shadow of the future’ cannot sustain cooperation. Cooperation must be altruistic, in the sense that a cooperator enhances her opponent’s payoff at her own expense. In this piece of work another group selection mechanism is presented that generates and sustains altruism in ex-ante anonymous settings. Assuming that cooperative attitudes are coupled with a preference for participating in costly rituals (religious involvement is taken as an example), interactions take place within two endogenously separated groups. The signaling value of religion in the model derives not from differential costliness but from cooperators’ intrinsic nature of motivation. Noncooperative types have to learn about the matching gains from religious involvement while cooperative types need not. This induces an initial advantage to cooperative/religious types at the beginning of each generation, thereby sustaining altruism in the long run via religious participation.Altruism; Prisoners' Dilemma; Evolutionary Game Theory; Signaling; Religion

Religion as a Seed Crystal for Altruistic Cooperation

The ability to solve problems of collective action is crucial for economic performance. Growth-fostering behavioral propensities such as respecting property, honoring contracts, or helping others are collectively beneficial but individually costly. The paradigmatic formalization of this rationality gap is provided by the non-iterated Prisoners’ Dilemma, where rational players are locked in at a state of mutual defection while mutual cooperation would be better for everyone. In sporadic, ex-ante anonymous interactions (like in modern large-scale societies), the ‘shadow of the future’ cannot sustain cooperation. Cooperation must be altruistic, in the sense that a cooperator enhances her opponent’s payoff at her own expense. In this piece of work another group selection mechanism is presented that generates and sustains altruism in ex-ante anonymous settings. Assuming that cooperative attitudes are coupled with a preference for participating in costly rituals (religious involvement is taken as an example), interactions take place within two endogenously separated groups. The signaling value of religion in the model derives not from differential costliness but from cooperators’ intrinsic nature of motivation. Noncooperative types have to learn about the matching gains from religious involvement while cooperative types need not. This induces an initial advantage to cooperative/religious types at the beginning of each generation, thereby sustaining altruism in the long run via religious participation

Exploiting Evolutionary Modeling to Prevail in Iterated Prisoner’s Dilemma Tournaments

The iterated prisoner’s dilemma is a famous model of cooperation and conflict in game theory. Its origin can be traced back to the Cold War, and countless strategies for playing it have been proposed so far, either designed by hand or automatically generated by computers. In the 2000s, scholars started focusing on adaptive players, that is, able to classify their opponent’s behavior and adopt an effective counter-strategy. The player presented in this paper, pushes such idea even further: it builds a model of the current adversary from scratch, without relying on any pre-defined archetypes, and tweaks it as the game develops using an evolutionary algorithm; at the same time, it exploits the model to lead the game into the most favorable continuation. Models are compact non-deterministic finite state machines; they are extremely efficient in predicting opponents’ replies, without being completely correct by necessity. Experimental results show that such player is able to win several one-to- one games against strong opponents taken from the literature, and that it consistently prevails in round-robin tournaments of different sizes

Exploring the Benefits of Teams in Multiagent Learning

For problems requiring cooperation, many multiagent systems implement solutions among either individual agents or across an entire population towards a common goal. Multiagent teams are primarily studied when in conflict; however, organizational psychology (OP) highlights the benefits of teams among human populations for learning how to coordinate and cooperate. In this paper, we propose a new model of multiagent teams for reinforcement learning (RL) agents inspired by OP and early work on teams in artificial intelligence. We validate our model using complex social dilemmas that are popular in recent multiagent RL and find that agents divided into teams develop cooperative pro-social policies despite incentives to not cooperate. Furthermore, agents are better able to coordinate and learn emergent roles within their teams and achieve higher rewards compared to when the interests of all agents are aligned.Comment: 10 pages, 6 figures, Published at IJCAI 2022. arXiv admin note: text overlap with arXiv:2204.0747

Biology and evolutionary games

This chapter surveys some evolutionary games used in biological sciences. These include the Hawk-Dove game, the Prisoner’s Dilemma, Rock–Paper–Scissors, the war of attrition, the Habitat Selection game, predatorprey games, and signalling games

Computing Nash equilibria and evolutionarily stable states of evolutionary games

Stability analysis is an important research direction in evolutionary game theory. Evolutionarily stable states have a close relationship with Nash equilibria of repeated games, which are characterized by the folk theorem. When applying the folk theorem, one needs to compute the minimax profile of the game in order to find Nash equilibria. Computing the minimax profile is an NP-hard problem. In this paper we investigate a new methodology to compute evolutionary stable states based on the level-k equilibrium, a new refinement of Nash equilibrium in repeated games. A level-k equilibrium is implemented by a group of players who adopt reactive strategies and who have no incentive to deviate from their strategies simultaneously. Computing the level-k equilibria is tractable because the minimax payoffs and strategies are not needed. As an application, this paper develops a tractable algorithm to compute the evolutionarily stable states and the Pareto front of n-player symmetric games. Three games, including the iterated prisoner’s dilemma, are analyzed by means of the proposed methodology

Integration of social and economic information drives cooperation in a collective decision making task.

Social decision-making presents arguably the most complex problem an animal can face. Collective, economic decision-making requires the integration of predictions based on the outcomes of prior interactions alongside predictions generated from ongoing social information. Many economic decisions are made as individuals interact with each other, however how the manner in which animals perceive and display social information affects economic decisions remains largely overlooked. Hence we developed a social dilemma task, traditionally focused on how experienced outcomes affect choices, but allow each rat player access to proximate social information.(...