TL;DR: The paper provides an explanation for altruistic behavior based on the matching and learning technology in the population in a infinite structured population, in which individuals meet and interact with their neighbors, individuals learn by imitating their more successful neighbors.
Abstract: The paper provides an explanation for altruistic behavior based on the matching and learning technology in the population. In a infinite structured population, in which individuals meet and interact with their neighbors, individuals learn by imitating their more successful neighbors. We ask which strategies are robust against invasion of mutants: A strategy is unbeatable if when all play it and a finite group of identical mutants enters then the learning process eliminates the mutants with probability 1. We find that such an unbeatable strategy is necessarily one in which each individual behaves as if he is related to his neighbors and takes into account their welfare as well as his. The degree to which he cares depends on the radii of his neighborhoods.
TL;DR: In this article, a model of a population with a Local Interaction structure is presented, where individuals interact with others in a given interaction neighborhood to obtain their payoff, and an unbeatable strategy can repel the invasion of any mutant.
Abstract: A model of a population with a Local Interaction structure is presented. Individuals interact with others in a given Interaction neighborhood to obtain their payoff. Individuals either imitate or else they die and are replaced by one of their neighbors in another neighborhood- the Propagation neighborhood. An individual with a higher payoff is more likely to be imitated or to replace his neighbor. An unbeatable strategy can repel the invasion of any mutant. We show that the (unique, if it exists) unbeatable strategy is an ESS of a population game with inclu- sive tness parameter which depends on the size of the interaction and propagation neighborhoods. We analyze the evolution of altruistic traits in such populations and observe that allowing the players more information eases the development of altruistic behavior.
TL;DR: In the Prisoner's Dilemma, players adopting "extortion" respond with cooperation to cooperation in most cases, defect in other rounds, but respond to defection with defection as discussed by the authors.
Abstract: Recently Press and Dyson have dramatically changed our view on the Prisoner's Dilemma by proposing a new class of strategies that enforce a linear relationship between the two players' scores. Players adopting 'extortion' respond with cooperation to cooperation in most cases, defect in other rounds, but respond to defection with defection. In this way, extortion enforces full cooperation of the partner who accedes to it because he profits from doing so. This unbeatable strategy is nevertheless prosocial because it is mostly cooperative and induces cooperation even though it gains most itself. Experiments show that about 40% of humans choose to use extortion in competitive situations or when they have the power to exchange coplayers. On being punished in egalitarian situations, they use a generous strategy.
TL;DR: In this paper, the authors investigate the static and dynamic properties of unbeatable strategies and compare them with related equilibrium concepts, and give equivalent conditions for a strategy to be unbeatable and compare it with related equilibria.
Abstract: A strategy is unbeatable if it is immune to any entrant strategy of any size. This paper investigates static and dynamic properties of unbeatable strategies. We give equivalent conditions for a strategy to be unbeatable and compare it with related equilibrium concepts. An unbeatable strategy is globally stable under replicator dynamics. In contrast, an unbeatable strategy can fail to be globally stable under best response dynamics even if it is also a unique and strict Nash equilibrium.