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Do economic games show evidence of altruistic or self-interested motivations in humans? A huge body of empirical work has found contrasting results. While many participants routinely make costly decisions that benefit strangers, consistent with the hypothesis that humans exhibit a biologically novel form of altruism (or "prosociality"), many participants also typically learn to pay fewer costs with experience, consistent with self-interested individuals adapting to an unfamiliar environment. Key to resolving this debate is explaining the famous "restart effect," a puzzling enigma whereby failing cooperation in public goods games can be briefly rescued by a surprise restart. Here we replicate this canonical result, often taken as evidence of uniquely human altruism, and show that it 1) disappears when cooperation is invisible, meaning individuals can no longer affect the behavior of their groupmates, consistent with strategically motivated, self-interested, cooperation; and 2) still occurs even when individuals are knowingly grouped with computer players programmed to replicate human decisions, consistent with confusion. These results show that the restart effect can be explained by a mixture of self-interest and irrational beliefs about the game's payoffs, and not altruism. Consequently, our results suggest that public goods games have often been measuring self-interested but confused behaviors and reject the idea that conventional theories of evolution cannot explain the results of economic games.

What motivates human behaviour in social dilemmas? The results of public goods games are commonly interpreted as showing that humans are altruistically motivated to benefit others. However, there is a competing 'confused learners' hypothesis: that individuals start the game either uncertain or mistaken (confused) and then learn from experience how to improve their payoff (payoff-based learning). Here we (1) show that these competing hypotheses can be differentiated by how they predict contributions should decline over time; and (2) use metadata from 237 published public goods games to test between these competing hypotheses. We found, as predicted by the confused learners hypothesis, that contributions declined faster when individuals had more influence over their own payoffs. This predicted relationship arises because more influence leads to a greater correlation between contributions and payoffs, facilitating learning. Our results suggest that humans, in general, are not altruistically motivated to benefit others but instead learn to help themselves., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

Economic experiments are often used to study if humans altruistically value the welfare of others. A canonical result from public-good games is that humans vary in how they value the welfare of others, dividing into fair-minded conditional cooperators, who match the cooperation of others, and selfish noncooperators. However, an alternative explanation for the data are that individuals vary in their understanding of how to maximize income, with misunderstanding leading to the appearance of cooperation. We show that (i) individuals divide into the same behavioral types when playing with computers, whom they cannot be concerned with the welfare of; (ii) behavior across games with computers and humans is correlated and can be explained by variation in understanding of how to maximize income; (iii) misunderstanding correlates with higher levels of cooperation; and (iv) standard control questions do not guarantee understanding. These results cast doubt on certain experimental methods and demonstrate that a common assumption in behavioral economics experiments, that choices reveal motivations, will not necessarily hold.

Economic games such as the public goods game are increasingly being used to measure social behaviours in humans and non-human primates. The results of such games have been used to argue that people are pro-social, and that humans are uniquely altruistic, willingly sacrificing their own welfare in order to benefit others. However, an alternative explanation for the empirical observations is that individuals are mistaken, but learn, during the game, how to improve their personal payoff. We test between these competing hypotheses, by comparing the explanatory power of different behavioural rules, in public goods games, where individuals are given different amounts of information. We find: (i) that individual behaviour is best explained by a learning rule that is trying to maximize personal income; (ii) that conditional cooperation disappears when the consequences of cooperation are made clearer; and (iii) that social preferences, if they exist, are more anti-social than pro-social., (© 2015 The Author(s) Published by the Royal Society. All rights reserved.)

Humans are an intensely social species, frequently performing costly behaviors that benefit others. Efforts to solve the evolutionary puzzle of altruism have a lengthy history, and recent years have seen many important advances across a range of disciplines. Here we bring together this interdisciplinary body of research and review the main theories that have been proposed to explain human prosociality, with an emphasis on kinship, reciprocity, indirect reciprocity, punishment, and morality. We highlight recent methodological advances that are stimulating research and point to some areas that either remain controversial or merit more attention.

It has become an accepted paradigm that humans have "prosocial preferences" that lead to higher levels of cooperation than those that would maximize their personal financial gain. However, the existence of prosocial preferences has been inferred post hoc from the results of economic games, rather than with direct experimental tests. Here, we test how behavior in a public-goods game is influenced by knowledge of the consequences of actions for other players. We found that (i) individuals cooperate at similar levels, even when they are not informed that their behavior benefits others; (ii) an increased awareness of how cooperation benefits others leads to a reduction, rather than an increase, in the level of cooperation; and (iii) cooperation can be either lower or higher than expected, depending on experimental design. Overall, these results contradict the suggested role of the prosocial preferences hypothesis and show how the complexity of human behavior can lead to misleading conclusions from controlled laboratory experiments.

Why does human cooperation often unravel in economic experiments despite a promising start? Previous studies have interpreted the decline as the reaction of disappointed altruists retaliating in response to non-altruists (Conditional Cooperators hypothesis). This interpretation has been considered evidence of a uniquely human form of cooperation, motivated by an altruistic concern for equality (‘fairness’) and requiring special evolutionary explanations. However, experiments have typically shown individuals not only information about the decisions of their groupmates (social information) but also information about their own payoffs. Showing both confounds explanations based on conditional cooperation with explanations based on confused individuals learning how to better play the game (Confused Learners hypothesis). Here we experimentally decouple these two forms of information, and thus these two hypotheses, in a repeated public-goods game. Analysing 616 Swiss university participants, we find that payoff information leads to a greater decline, supporting the Confused Learners hypothesis. In contrast, social information has a small or negligible effect, contradicting the Conditional Cooperators hypothesis. We also find widespread evidence of both confusion and selfish motives, suggesting that human cooperation is maybe not so unique after all.

The black box method was developed as an “asocial control” to allow for payoff-based learning while eliminating social responses in repeated public goods games. Players are told they must decide how many virtual coins they want to input into a virtual black box that will provide uncertain returns. However, in truth, they are playing with each other in a repeated social game. By “black boxing” the game’s social aspects and payoff structure, the method creates a population of self-interested but ignorant or confused individuals that must learn the game’s payoffs. This low-information environment, stripped of social concerns, provides an alternative, empirically derived null hypothesis for testing social behaviours, as opposed to the theoretical predictions of rational self-interested agents (Homo economicus). However, a potential problem is that participants can unwittingly affect the learning of other participants. Here, we test a solution to this problem in a range of public goods games by making participants interact, unknowingly, with simulated players (“computerised black box”). We find no significant differences in rates of learning between the original and the computerised black box, therefore either method can be used to investigate learning in games. These results, along with the fact that simulated agents can be programmed to behave in different ways, mean that the computerised black box has great potential for complementing studies of how individuals and groups learn under different environments in social dilemmas.

The strategy method is often used in public goods games to measure an individual’s willingness to cooperate depending on the level of cooperation by their groupmates (conditional cooperation). However, while the strategy method is informative, it risks conflating confusion with a desire for fair outcomes, and its presentation may risk inducing elevated levels of conditional cooperation. This problem was highlighted by two previous studies which found that the strategy method could also detect equivalent levels of cooperation even among those grouped with computerized groupmates, indicative of confusion or irrational responses. However, these studies did not use large samples (n = 40 or 72) and only made participants complete the strategy method one time, with computerized groupmates, preventing within-participant comparisons. Here, in contrast, 845 participants completed the strategy method two times, once with human and once with computerized groupmates. Our research aims were twofold: (1) to check the robustness of previous results with a large sample under various presentation conditions; and (2) to use a within-participant design to categorize participants according to how they behaved across the two scenarios. Ideally, a clean and reliable measure of conditional cooperation would find participants conditionally cooperating with humans and not cooperating with computers. Worryingly, only 7% of participants met this criterion. Overall, 83% of participants cooperated with the computers, and the mean contributions towards computers were 89% as large as those towards humans. These results, robust to the various presentation and order effects, pose serious concerns for the measurement of social preferences and question the idea that human cooperation is motivated by a concern for equal outcomes.

The black box method was developed as an "asocial control" to allow for payoff-based learning while eliminating social responses in repeated public goods games. Players are told they must decide how many virtual coins they want to input into a virtual black box that will provide uncertain returns. However, in truth, they are playing with each other in a repeated social game. By "black boxing" the game's social aspects and payoff structure, the method creates a population of self-interested but ignorant or confused individuals that must learn the game's payoffs. This low-information environment, stripped of social concerns, provides an alternative, empirically derived null hypothesis for testing social behaviours, as opposed to the theoretical predictions of rational self-interested agents (Homo economicus). However, a potential problem is that participants can unwittingly affect the learning of other participants. Here, we test a solution to this problem in a range of public goods games by making participants interact, unknowingly, with simulated players ("computerised black box"). We find no significant differences in rates of learning between the original and the computerised black box, therefore either method can be used to investigate learning in games. These results, along with the fact that simulated agents can be programmed to behave in different ways, mean that the computerised black box has great potential for complementing studies of how individuals and groups learn under different environments in social dilemmas. [ABSTRACT FROM AUTHOR]

The strategy method is often used in public goods games to measure an individual's willingness to cooperate depending on the level of cooperation by their groupmates (conditional cooperation). However, while the strategy method is informative, it risks conflating confusion with a desire for fair outcomes, and its presentation may risk inducing elevated levels of conditional cooperation. This problem was highlighted by two previous studies which found that the strategy method could also detect equivalent levels of cooperation even among those grouped with computerized groupmates, indicative of confusion or irrational responses. However, these studies did not use large samples (n = 40 or 72) and only made participants complete the strategy method one time, with computerized groupmates, preventing within-participant comparisons. Here, in contrast, 845 participants completed the strategy method two times, once with human and once with computerized groupmates. Our research aims were twofold: (1) to check the robustness of previous results with a large sample under various presentation conditions; and (2) to use a within-participant design to categorize participants according to how they behaved across the two scenarios. Ideally, a clean and reliable measure of conditional cooperation would find participants conditionally cooperating with humans and not cooperating with computers. Worryingly, only 7% of participants met this criterion. Overall, 83% of participants cooperated with the computers, and the mean contributions towards computers were 89% as large as those towards humans. These results, robust to the various presentation and order effects, pose serious concerns for the measurement of social preferences and question the idea that human cooperation is motivated by a concern for equal outcomes. [ABSTRACT FROM AUTHOR]

Human social life is rife with uncertainty. In any given encounter, one can wonder whether cooperation will generate future benefits. Many people appear to resolve this dilemma by initially cooperating, perhaps because (a) encounters in everyday life often have future consequences, and (b) the costs of alienating oneself from long-term social partners often outweighed the short-term benefits of acting selfishly over our evolutionary history. However, because cooperating with other people does not always advance self-interest, people might also learn to withhold cooperation in certain situations. Here, we review evidence for two ideas: that people (a) initially cooperate or not depending on the incentives that are typically available in their daily lives and (b) also learn through experience to adjust their cooperation on the basis of the incentives of unfamiliar situations. We compare these claims with the widespread view that anonymously helping strangers in laboratory settings is motivated by altruistic desires. We conclude that the evidence is more consistent with the idea that people stop cooperating in unfamiliar situations because they learn that it does not help them, either financially or through social approval. [ABSTRACT FROM AUTHOR]

hypothesis, we experimentally manipulated the extent an individual's behaviour is known to others, and hence whether or not strategic cooperation is possible. In contrast with many previous studies, we avoided confounding factors by preventing individuals from learning during the game about either pay-offs or about how other individuals behave. We found clear evidence for strategic cooperators--just telling some individuals that their groupmates would be informed about their behaviour led to them tripling their initial level of cooperation, from 17 to 50%. We also found that many individuals play as if they do not understand the game, and their presence obscures the detection of strategic cooperation. Identifying such players allowed us to detect and study strategic motives for cooperation in novel, more powerful, ways. [ABSTRACT FROM AUTHOR]

Humans have a sophisticated ability to learn from others, termed social learning, which has allowed us to spread over the planet, construct complex societies, and travel to the moon. It has been hypothesized that social learning has played a pivotal role in making human societies cooperative, by favouring cooperation even when it is not favoured by genetical selection. However, this hypothesis lacks direct experimental testing, and the opposite prediction has also been made, that social learning disfavours cooperation. We experimentally tested how different aspects of social learning affect the level of cooperation in public-goods games. We found that: (i) social information never increased cooperation and usually led to decreased cooperation; (ii) cooperation was lowest when individuals could observe how successful individuals behaved; and (iii) cooperation declined because individuals preferred to copy successful individuals, who cooperated less, rather than copy common behaviours. Overall, these results suggest that individuals use social information to try and improve their own success, and that this can lead to lower levels of cooperation. [ABSTRACT FROM AUTHOR]

Hamilton's rule predicts that individuals should be more likely to altruistically help closer kin and this theory is well supported from zoological studies of nonhumans. In contrast, there is a paucity of relevant human data. This is largely due to the difficulties of either experimentally testing relatives or of collecting data on genuinely costly cooperation. We test Hamilton's rule in humans by seeing if the availability of help in times of crises is predicted by the degree of genetic relatedness. In social network research, the pool of people that one can go to for support during times of crisis is termed the support network. By definition, the members of a support network provide various benefits in times of need, and larger support networks have been shown to be important for general health. As this level of support bears costs for the providers and has clear benefits for the receivers, it therefore allows us to test Hamilton's rule. We use an Internet sample to analyze the composition of 540 people's support networks. We had people rank their support network members in order of who would be most likely to help and found that relatives were more likely to be ranked in primary positions and that the degree of relatedness correlated with rank. [ABSTRACT FROM AUTHOR]

The results of numerous economic games suggest that humans behave more cooperatively than would be expected if they were maximizing selfish interests. It has been argued that this is because individuals gain satisfaction from the success of others, and that such prosocial preferences require a novel evolutionary explanation. However, in previous games, imperfect behavior would automatically lead to an increase in cooperation, making it impossible to decouple any form of mistake or error from prosocial cooperative decisions. Here we empirically test between these alternatives by decoupling imperfect behavior from prosocial preferences in modified versions of the public goods game, in which individuals would maximize their selfish gain by completely (100%) cooperating. We found that, although this led to higher levels of cooperation, it did not lead to full cooperation, and individuals still perceived their group mates as competitors. This is inconsistent with either selfish or prosocial preferences, suggesting that the most parsimonious explanation is imperfect behavior triggered by psychological drives that can prevent both complete defection and complete cooperation. More generally, our results illustrate the caution that must be exercised when interpreting the evolutionary implications of economic experiments, especially the absolute level of cooperation in a particular treatment. [ABSTRACT FROM AUTHOR]

Economic games such as the public goods game are increasingly being used to measure social behaviours in humans and non-human primates. The results of such games have been used to argue that people are pro-social, and that humans are uniquely altruistic, willingly sacrificing their own welfare in order to benefit others. However, an alternative explanation for the empirical observations is that individuals are mistaken, but learn, during the game, how to improve their personal payoff. We test between these competing hypotheses, by comparing the explanatory power of different behavioural rules, in public goods games, where individuals are given different amounts of information. We find: (i) that individual behaviour is best explained by a learning rule that is trying to maximize personal income; (ii) that conditional cooperation disappears when the consequences of cooperation are made clearer; and (iii) that social preferences, if they exist, are more anti-social than pro-social. [ABSTRACT FROM AUTHOR]