Decoupling cooperation and punishment in humans shows that punishment is not an altruistic trait

Economic experiments have suggested that cooperative humans will altruistically match local levels of cooperation (conditional cooperation) and pay to punish non-cooperators (altruistic punishment). Evolutionary models have suggested that if altruists punish non-altruists this could favour the evolution of costly helping behaviours (cooperation) among strangers. An often-key requirement is that helping behaviours and punishing behaviours form one single conjoined trait (strong reciprocity). Previous economics experiments have provided support for the hypothesis that punishment and cooperation form one conjoined, altruistically motivated, trait. However, such a conjoined trait may be evolutionarily unstable, and previous experiments have confounded a fear of being punished with being surrounded by cooperators, two factors that could favour cooperation. Here, we experimentally decouple the fear of punishment from a cooperative environment and allow cooperation and punishment behaviour to freely separate (420 participants). We show, that if a minority of individuals is made immune to punishment, they (i) learn to stop cooperating on average despite being surrounded by high levels of cooperation, contradicting the idea of conditional cooperation and (ii) often continue to punish, ‘hypocritically’, showing that cooperation and punishment do not form one, altruistically motivated, linked trait.

Evolution of prosocial punishment in unstructured and structured populations and in the presence of antisocial punishment

A large body of empirical evidence suggests that altruistic punishment abounds in human societies. Based on such evidence, it is suggested that punishment serves an important role in promoting cooperation in humans and possibly other species. However, as punishment is costly, its evolution is subject to the same problem that it tries to address. To suppress this so-called second-order free-rider problem, known theoretical models on the evolution of punishment resort to one of the few established mechanisms for the evolution of cooperation. This leaves the question of whether altruistic punishment can evolve and give rise to the evolution of cooperation in the absence of such auxiliary cooperation-favoring mechanisms unaddressed. Here, by considering a population of individuals who play a public goods game, followed by a public punishing game, introduced here, we show that altruistic punishment indeed evolves and promotes cooperation in the absence of a cooperation-favoring mechanism. In our model, the punishment pool is considered a public resource whose resources are used for punishment. We show that the evolution of a punishing institution is facilitated when resources in the punishment pool, instead of being wasted, are used to reward punishers when there is nobody to punish. Besides, we show that higher returns to the public resource or punishment pool facilitate the evolution of prosocial instead of antisocial punishment. We also show that an optimal cost of investment in the punishment pool facilitates the evolution of prosocial punishment. Finally, our analysis shows that being close to a physical phase transition facilitates the evolution of altruistic punishment.

Evolution of altruistic punishments among heterogeneous conditional cooperators

It has been known that altruistic punishments solve the free rider problem in public goods games. Considering spatial structure and considering pure strategies significant advances have been made in understanding the evolution of altruistic punishments. However, these models have not considered key behavior regularities observed in experimental and field settings, where the individuals behave like conditional cooperators who are more willing to donate and are also more willing to punish free riders. Considering these behavioral regularities, without imposing a spatial structure on the population, I propose an evolutionary agent-based model in which agents behave like conditional cooperators, each agent’s donation conditional on the difference between the number of donations in the past and the threshold value and the propensity value of the agent. Altruistic punishment depends on the difference between the threshold value of the focal agent and the randomly matched another agent. The simulations show that, for certain inflicted costs of punishments, generous altruistic punishments evolve and stabilize cooperation. The results show that, unlike previous models, it is not necessary to punish all free riders equally; it is necessary to do so in the case of the selfish free riders but not in the case of negative reciprocators.

Altruistic Punishment, Status Conflict, and Knowledge Sharing in the Workplace: An Evolutionary Game Model

Second- and third-party altruistic punishments can both improve knowledge sharing, but it is debatable which does so better. We thus concurrently consider the influence of status conflict advantage and disadvantage on knowledge sharing. We also analyze knowledge sharing by introducing an evolutionary game model and conducting simulations, and reveal the following: (1) the appropriate choice for an organization that is increasing its knowledge contribution is third-party punishment; (2) the status conflict advantage and disadvantage play important roles in knowledge sharing. When the status conflict advantage is greater than the disadvantage, the two players eventually share knowledge, and vice versa. Interestingly, when the status conflict advantage is equal to the disadvantage, the player with the stronger sharing inclination has a powerful impact on the player who would rather hoard knowledge, thus ultimately leading him/her to share. (3) Guanxi, knowledge capacity, and synergy revenue have positive effects on knowledge sharing, while the knowledge sharing cost has a negative influence on them. Moreover, the stable equilibrium result is affected by the initial system state. In sum, our results not only promote the understanding of altruistic punishment and status conflict regarding knowledge sharing but also provide practical implications for both organizations and leaders.

Decoupling altruistic punishment: immune punishers learn to not cooperate, but still punish hypocritically.

Economic experiments, and evolutionary models, have suggested that human cooperation is sustained by altruistically motivated cooperators paying to punish non-cooperators. Consequently, punishment allows cooperators to happily match each other (conditionally cooperate), confident that they will not be exploited by non-cooperators. However, it is not clear that punishment is altruistically motivated, and previous experiments have confounded a fear of being punished with being surrounded by cooperators. Here, we experimentally decouple the fear of punishment from a cooperative environment and allow cooperation and punishment to freely separate (420 participants). We show, that if a minority of individuals are made immune to punishment, they (1) learn to stop cooperating despite being surrounded by high levels of cooperation; and (2) continue to punish, ‘hypocritically’, showing that cooperation and punishment do not form one, altruistically motivated, linked trait. Overall, 48% of punishers were ‘anti-social’ during the experiment, suggesting humans are not in general altruistic punishers.

Egoistic punishment outcompetes altruistic punishment in the spatial public goods game

The evolution of costly punishment is a puzzle due to cooperators’ second-order free-riding. Previous studies have proposed many solutions mainly focused on reducing the punishment cost or punishing second-order free riders directly or indirectly. We attempt to explain this confusion from the perspective of punishment motivation, which is why the punisher is willing to pay the cost. The answer is that the punisher is egoistic. Egoistic punishment aims to protect punishers’ own cooperative benefits shared by the defectors. In such case, egoistic punishers would pay a cost in punishing defectors and retrieve lost payoffs from defectors. Here, we examined the evolution and performance of egoistic punishment and compared it with typical altruistic punishment using classic peer-punishment and pool-punishment modes. Results showed egoistic punishment can evolve and effectively promote cooperation within a large parameter range, whether in a well-mixed or structured population, or through peer-punishment or pool-punishment modes. This result is also robust to different strategy-updating rules. The evolution under the pool-punishment mechanism is more complicated. The influence of parameters is counter-intuitive because of cycle dominance; namely, the cost is the key factor to control the level of cooperation and the fine determines the ratio of the punishers and cooperators. Compared with altruistic punishment, egoistic punishment can promote cooperation in a lower-fine and higher-cost area, especially in the pool punishment mode, and the egoistic punishers have stronger survivability. Egoistic punishers represent the natural fairness in a social system. Results revealed that focusing on individual equity can significantly promote collective cooperation. This study provides another explanation for the evolution of costly punishment.

Mathematical foundations of moral preferences

One-shot anonymous unselfishness in economic games is commonly explained by social preferences, which assume that people care about the monetary pay-offs of others. However, during the last 10 years, research has shown that different types of unselfish behaviour, including cooperation, altruism, truth-telling, altruistic punishment and trustworthiness are in fact better explained by preferences for following one's own personal norms—internal standards about what is right or wrong in a given situation. Beyond better organizing various forms of unselfish behaviour, this moral preference hypothesis has recently also been used to increase charitable donations, simply by means of interventions that make the morality of an action salient. Here we review experimental and theoretical work dedicated to this rapidly growing field of research, and in doing so we outline mathematical foundations for moral preferences that can be used in future models to better understand selfless human actions and to adjust policies accordingly. These foundations can also be used by artificial intelligence to better navigate the complex landscape of human morality.

Mathematical foundations of moral preferences

One-shot anonymous unselfishness in economic games is commonly explained by social preferences, which assume that people care about the monetary payoffs of others. However, during the last ten years, research has shown that different types of unselfish behaviour, including cooperation, altruism, truth-telling, altruistic punishment, and trustworthiness are in fact better explained by preferences for following one's own personal norms -- internal standards about what is right or wrong in a given situation. Beyond better organising various forms of unselfish behaviour, this moral preference hypothesis has recently also been used to increase charitable donations, simply by means of interventions that make the morality of an action salient. Here we review experimental and theoretical work dedicated to this rapidly growing field of research, and in doing so we outline mathematical foundations for moral preferences that can be used in future models to better understand selfless human actions and to adjust policies accordingly. These foundations can also be used by artificial intelligence to better navigate the complex landscape of human morality.

Altruistic punishment in modern intentional communities

Evolutionists studying human cooperation disagree about how to best explain it. One view is that humans are predisposed to engage in costly cooperation and punishment of free-riders as a result of culture/gene coevolution via group selection. Alternatively, some researchers argue that context-specific cognitive mechanisms associated with traditional neo-Darwinian self- and kin-maximization models sufficiently explain all aspects of human cooperation and punishment. There has been a great deal of research testing predictions derived from both positions; still, researchers generally agree that more naturalistic data are needed to complement mathematical modeling and laboratory and field experiments. Most of these data have been obtained from small-scale forager and other societies, but modern intentional communities offer another productive source of information. This exploratory study describes context-specific patterns of punishment in 46 American intentional communities that cast doubt on the prediction that people are predisposed to punish free-riders in naturalistic interactions.