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Extensive cooperation among unrelated individuals is unique to humans, who often sacrifice personal benefits for the common good and work together to achieve what they are unable to execute alone. The evolutionary success of our species is indeed due, to a large degree, to our unparalleled other-regarding abilities. Yet, a comprehensive understanding of human cooperation remains a formidable challenge. Recent research in social science indicates that it is important to focus on the collective behavior that emerges as the result of the interactions among individuals, groups, and even societies. Non-equilibrium statistical physics, in particular Monte Carlo methods and the theory of collective behavior of interacting particles near phase transition points, has proven to be very valuable for understanding counterintuitive evolutionary outcomes. By studying models of human cooperation as classical spin models, a physicist can draw on familiar settings from statistical physics. However, unlike pairwise interactions among particles that typically govern solid-state physics systems, interactions among humans often involve group interactions, and they also involve a larger number of possible states even for the most simplified description of reality. The complexity of solutions therefore often surpasses that observed in physical systems. Here we review experimental and theoretical research that advances our understanding of human cooperation, focusing on spatial pattern formation, on the spatiotemporal dynamics of observed solutions, and on self-organization that may either promote or hinder socially favorable states., 48 two-column pages, 35 figures; Review accepted for publication in Physics Reports

In social dilemmas, cooperation among randomly interacting individuals is often hard to achieve. The situation changes if migration takes place, where game structure jointly evolves with the migration from adverse neighborhoods. Here we introduce a framework combining migration with the individual heterogeneity of migration scopes. When faced with a gloomy outlook, some players prefer vacant sites within their adjacent neighborhoods, while others may migrate within the whole network, provided the sites are empty. Thus, we can make a thorough inquiry of the sustainability of cooperation in a spatially distributed population divided by these two groups, and all possible mixtures between them. Our main result is that small-scope migration and suitable population density can gear up high cooperation levels in the midst of dense populations structured by scale-free networks, while large-scope migration and sparsity favor the cooperator clustering among lattice-structured populations. In this sense, the conditions for the emergence of cooperation are prevailingly created by the spatial reciprocity, and migration also has enough potential to help cooperation to prevail in suitable combination of game parameters in dynamics (e.g., population density, the type of interaction structure and also the migration scope). (C) 2013 Elsevier Ltd. All rights reserved.

How effective are rewards (for cooperation) and punishment (for noncooperation) as tools to promote cooperation in social dilemmas or situations when immediate self-interest and longer term collective interest conflict? What variables can promote the impact of these incentives? Although such questions have been examined, social and behavioral scientists provide different answers. To date, there is no theoretical and/or quantitative review of rewards and punishments as incentives for cooperation in social dilemmas. Using a novel interdependence-theoretic framework, we propose that rewards and punishments should both promote cooperation, and we identify 2 variables-cost of incentives and source of incentives-that are predicted to magnify the effectiveness of these incentives in promoting cooperation. A meta-analysis involving 187 effect sizes revealed that rewards and punishments exhibited a statistically equivalent positive effect on cooperation (d = 0.51 and 0.70, respectively). The effectiveness of incentives was stronger when the incentives were costly to administer, compared to free. Centralization of incentives did not moderate the effect size. Punishments were also more effective during iterated dilemmas when participants continued to interact in the same group, compared to both (a) iterated dilemmas with reassignment to a new group after each trial and (b) one-shot dilemmas. We also examine several other potential moderators, such as iterations, partner matching, group size, country, and participant payment. We discuss broad conclusions, consider implications for theory, and suggest directions for future research on rewards and punishment in social dilemmas. © 2011 American Psychological Association.

Studies aimed at explaining the evolution of phenotypic traits have often solely focused on fitness considerations, ignoring underlying mechanisms. In recent years, there has been an increasing call for integrating mechanistic perspectives in evolutionary considerations, but it is not clear whether and how mechanisms affect the course and outcome of evolution. To study this, we compare four mechanistic implementations of two well-studied models for the evolution of cooperation, the Iterated Prisoner's Dilemma (IPD) game and the Iterated Snowdrift (ISD) game. Behavioural strategies are either implemented by a 1 : 1 genotype–phenotype mapping or by a simple neural network. Moreover, we consider two different scenarios for the effect of mutations. The same set of strategies is feasible in all four implementations, but the probability that a given strategy arises owing to mutation is largely dependent on the behavioural and genetic architecture. Our individual-based simulations show that this has major implications for the evolutionary outcome. In the ISD, different evolutionarily stable strategies are predominant in the four implementations, while in the IPD each implementation creates a characteristic dynamical pattern. As a consequence, the evolved average level of cooperation is also strongly dependent on the underlying mechanism. We argue that our findings are of general relevance for the evolution of social behaviour, pleading for the integration of a mechanistic perspective in models of social evolution.

Humans behave altruistically in one-shot interactions under total anonymity. In search of explanations for such behavior, it has been argued that at least some individuals have a general tendency to behave altruistically independent of profitability. In fact, a stable altruistic trait would be adaptive if it were recognizable. Then, altruists could choose each other in order to retain benefits through mutual cooperation. Previous research has shown that individuals can predict the degree of altruistic behavior of strangers by reading signs of emotions evoked in significant social decisions. However, the identification of benevolent emotional states is no guarantee of the existence of permanent altruistic traits, though permanent traits are the preferable criterion for selection of good interaction partners. In this study, we tested whether individuals are able to identify altruistic traits. Judges watched 20-s silent video clips of unacquainted target persons and were asked to estimate the behavior of these target persons in a money-sharing task. As the videotapes of the target persons had been recorded in a setting unrelated to altruistic behavior, the judges could not base their estimates on situational cues related to the money-sharing task but instead had to draw on stable signals of altruism. Estimates were significantly better than chance, indicating that individuals can identify permanent altruistic traits in others. As this mechanism raises opportunities for selective interactions between altruists, our findings are discussed with respect to their relevance for explaining the evolution of altruism through assortment. (C) 2010 Elsevier Inc. All rights reserved.