What is reciprocity, anyway?
/in Newsletter– by Diego Guevara Beltrán, Jessica Ayers, Lee Cronk, & Athena Aktipis
Thirty-four experts on cooperation walk into a bar and begin to discuss the ‘true’ meaning of the term ‘reciprocity.’ A biologist offers one definition. She says, “we observe reciprocity when the gift of some food, or another favor, increases the probability for the donor to receive the same thing, or something else, from the receiver”.
Perhaps because that approximates one of Robert Trivers’ definitions of reciprocal altruism, all experts nod in agreement with the biologist. However, not being quite content with this definition, an economist retorts “[that the] formulation is rather sloppy.” His colleague adds that “reciprocity should be conceptualized as reciprocal strategies in repeated games, normative obligations, and individual preferences to respond to kind acts with kind acts and to unkind acts with unkind acts.”
“Yes, yes, but how do we operationalize reciprocity?” asks one psychologist. He says, “The best statistical definition is the contemporaneous correlation of two individuals’ responses within time-series regression analyses and as partner effects among these variables.” Intrigued but bewildered, another psychologist adds “reciprocity is reciprocity, that’s what I was taught! Because he gave to him and he gave to me, then I gave to her, albeit indirectly. But I couldn’t keep track of who gave what to whom, what I owed to him, or who I should groom. Maybe we should just avoid this definitional animosity and just call all transfers something-something reciprocity!”
A review of the literature yields a proliferation of reciprocity-related terms – thirty-four at last count. That might lead you to believe that experts indeed agreed with the bewildered psychologist. Perhaps experts want to avoid definitional animosity and decided to call all transfers “something-something” reciprocity. With several conflicting, overlapping, and even a few redundant terms, it was clear there is no consensus among scholars regarding what truly constitutes reciprocity. In fact, these terms continue to proliferate, as evidenced by the preceding paper in the same issue of Evolution and Human Behavior (i.e., “dynamic indirect reciprocity”). This lack of consensus and the continued proliferation of terms motivated us to review the term reciprocity and survey cooperation experts about it.
In our study, we asked 85 cooperation experts to rate the extent to which they believe thirty scholarly definitions of the term reciprocity were truly reciprocity. Some of these definitions (e.g., indirect reciprocity: return is expected from someone other than recipient of benefit) are common in the literature, while others (e.g., homeomorphic reciprocity: exchange of things that are the same) not so much.
We first wanted to know whether experts’ responses would help us identify underlying dimensions of reciprocity (i.e., characteristics that cluster some, but not other, terms together). Applying factor analyses, we find that the scholarly definitions of the term reciprocity can be represented by four broad dimensions: (1) Balanced Transfers (i.e., transfers that are of equal or equivalent value), (2) Reputation-based Transfers (i.e., transfers where individuals give to others who have given in the past and receive from others if they have given in the past, (3) Debt-based Transfers (i.e., transfers where individuals keep track of, and expect repayment for, what they give to others), and (4) Unconditional Transfers (i.e., transfers that do not revolve around concepts of debt or account keeping). This four-factor framework captures greater variation in types, or dimensions, of transfers than previous frameworks (e.g., direct, indirect, and generalized reciprocity).
We encourage experts to consider adopting these terms. In doing so, we can provide specific qualifiers to a transfer while at the same time reducing the possible number of dimensions that describe a transfer based on whether, or the extent to which, they are balanced, debt-based, reputation-based, or unconditional. For example, we saw that several experts used the same term (e.g., indirect reciprocity) to describe multiple different reciprocity terms. Hence, our framework would allow experts to avoid using overlapping terms to describe different dimensions of transfers, ultimately improving communication within and between disciplines.
We then assessed the level of consensus among experts regarding what truly constitutes reciprocity. There was no consensus regarding what should be considered reciprocity. However, over 90% of experts agreed that unconditional transfers (e.g., generalized reciprocity I: non-conditional sharing and giving of assistance) should not be considered reciprocity. So, although experts do not yet agree on what truly constitutes reciprocity, these findings suggest that the scholarly community can move beyond discussions of whether unconditional transfers should or should not be considered reciprocity. They should not be.
Instead, the scholarly community might consider focusing on outstanding disagreements, such as those identified in our study. For example, some experts believe that transfers are reciprocity only if agents have the intention to reward a giver or incentivize a receiver to give back, while others do not think intentionality is a defining feature of reciprocity. Similarly, some experts believe that reciprocity must involve a cost, while others believe that reciprocity does not involve cost or transfers with negative utility.
Focusing on these issues might help experts reach a consensus regarding what truly constitutes reciprocity. However, we might also supersede these disagreements by adopting the language of Balanced, Debt-based, Reputation-based, and Unconditional transfers. Does it matter for the study of evolution and human behavior whether unconditional giving is or is not reciprocity? We think not. What matters is that we reach a deeper understanding of the ultimate causes and proximate mechanisms underlying the diversity of transfers that have shaped the evolution of human behavior. While our framework does not include information about the ultimate causes of behavior, it provides information about the proximate mechanisms underlying an individual’s decision to engage in a transfer. Overall, we hope our study allows the scientific community to gauge the current level of consensus, or lack thereof, regarding the term reciprocity, and ease communication across academic fields.
Read the original article: Guevara Beltrán, D., Ayers, J.D., Munoz, A., Cronk, L., & Aktipis, A. (2023). What is reciprocity? A review and expert-based classification of cooperative transfers. Evolution & Human Behavior, 44(4), 384-393.
Are parents naturally biased towards their sons or daughters, depending on their conditions?
/in Newsletter, Uncategorized– by Valentin Thouzeau
According to Robert Trivers and Dan Willard’s hypothesis, in many species, parents in good condition should favour male offspring, while parents in poor condition should favour female offspring. Why? First, parents with more resources can support more offspring. Second, in polygynous species, males with more resources are more likely to have many offspring. Natural selection should therefore favour investment in male offspring when parents are in good condition, since their sons will have a chance to have many children in turn. Conversely, natural selection should favour investment in female offspring when parents are in poorer condition, since their daughters have a high chance of having children even if they do not have many resources (see Figure 1 for a schematic representation of this hypothesis). This prediction was tested in a numerous non-human species. For instance, in ungulates, the results showed that when a female’s partner can invest more resources in her offspring, she does indeed produce more sons.

Figure 1 – a) Example of a group in which males with many resources (represented by large circles) are likely to have more offspring than females with many resources, since they can have children with multiple females. b) Representation of the optimal choice in terms of investment by parents in a population represented in a. Parents with few resources should favour their female offspring, while parents with many resources should favour males. c) Prediction of the Trivers-Willard hypothesis arising from the choice in b. The sex ratio of offspring from parents with few resources should favour females, while it should favour males for parents with many resources.
But can this prediction be applied to humans? We often think we are above biology – yet some results seem to contradict this: one study showed that American millionaires have an average of 60% sons and 40% daughters! Does this prediction apply to how we treat our children? In another U.S. study, no difference was found between parents based on socioeconomic status in the amount of time they spend with their sons or daughters. So the results are uncertain.
At present, there are literally hundreds of studies testing the hypothesis in humans, and it is difficult to get a comprehensive view. Also, can we trust the proportion of results that support the Trivers-Willard hypothesis? It may be that researchers were more inclined to publish results that supported the hypothesis rather than those that invalidated it, which may have led to publication bias. We were surprised to find that results from studies involving many groups of animals were synthesised (this is the case, for example, for ungulates and non-human primates), but no synthesis of the work done in the human species had been undertaken until now. This is why we started this work.
We collected 87 studies reporting a total of 821 tests of the Trivers-Willard hypothesis. Although the majority of the samples were based in North America, the geographic coverage was considerable. The analysis of all these studies reveals that the results are largely compatible with the Trivers-Willard hypothesis and that there is no publication bias in this scientific literature that could alone explain the compatibility of the results with this hypothesis.
We then tested whether the hypothesis held true in the birth bias of boys and girls, in the investment bias that boys and girls receive after birth, or in both. We found that tests for both versions are equally prevalent in the scientific literature. However, the results indicate that birth bias is better supported than investment bias. Putting together the last 50 years of research on the Trivers-Willard hypothesis thus allows us to conclude that parents in good condition have, on average, more sons, while parents in poor condition have more daughters.
We have to keep in mind that even if the Trivers-Willard hypothesis is validated for the birth bias, this bias is very small (the correlation coefficient is 0.037). There is simply a very small additional probability of giving birth to sons when conditions are favourable, and to daughters when conditions are less favourable. Nevertheless, these results show how the theory of evolution leads to surprising predictions that allow us to discover unsuspected phenomen.
Read the original paper: Thouzeau, V., Bollée, J., Cristia, A., & Chevallier, C. (2023). Decades of Trivers-Willard research on humans: What conclusions can be drawn? Evolution and Human Behavior, 44(4), 324-331.
What’s punishment like in small-scale societies?
/in Newsletter– by Léo Fitouchi (Image credit Frans Huby, Creative Commons 3.0)
Humans want wrongdoers to suffer. From the justice courts of modern societies to the biblical “eye for an eye,” people everywhere have rules for punishing transgressions including theft, murder, assault, or property damage. Why do human societies punish wrongdoers? You might think the answer is simple: to teach offenders a lesson so that they don’t offend again and the group can function well.
This intuition—that punishment serves to enforce collective norms—reflects in many strands of social science. Émile Durkheim, the French founder of sociology, argued that punishment serves to reaffirm the sacred values of the group, thereby reinforcing group solidarity. More recently, evolutionary social scientists assume that humans feel the need to punish norm-violators even when their behavior doesn’t harm the punisher directly—a behavior called third-party punishment. We humans would be normative animals, deeply committed to getting deviants back in line, for the good of the group and at a cost to oneself.
In this view, third-party punishment is not only a universal feature of human psychology; it also explains why humans are so cooperative compared to other species. By imposing costs on antisocial actors, third-party punishment would have selected for higher levels of cooperation during human evolution.
Despite these wide-ranging claims, however, few recent studies have examined how punishment operates in the social contexts where our punitive psychology is assumed to have evolved—namely, small-scale, politically decentralized societies (for notable exceptions, see here, here, and here). Most studies of punishment rely on participants from large-scale societies or, if they include participants from small-scale societies, on artificial experiments such as economic games.
In a recent paper, Manvir Singh and I examine the concrete enactment of punishment in three small-scale societies: Kiowa bison hunters (North America), Mentawai horticulturalists (Indonesia), and Nuer pastoralists (South Sudan). We code ethnographic reports of 91 offenses among the Kiowa, analyze first-hand interviews about 276 offenses among the Mentawai, and review punitive procedures documented among the Nuer.
With find that punitive procedures are often inconsistent with a norm-enforcement function. Most notably, people don’t bother punishing violations that don’t harm them directly. The quasi-totality of the punishments we observe, whether they involve fines, killing the offender, or destroying their property, are administered by the victim themselves or their family in retaliation for the harm suffered. Contrary to the idea that third-party punishment is universal and has shaped the evolution of human cooperation, people often seem indifferent to transgressions that do not affect them directly, even for serious transgressions such as murder.
If people do not punish to enforce collective norms or group cooperation, then what is punishment for? A useful analogy here is the distinction, made in some large-scale societies, between criminal law and civil law. Criminal law deals with violations of the society’s norms, which are punished by third-party officials on behalf of society as a whole. Civil law, by contrast, deals with dyadic disputes between private parties, requiring the offender to compensate the victim for the tort inflicted. Punitive justice in small-scale societies, we argue, serves only the civil function of resolving private disputes between offender and victim and restoring their cooperative relationship that was broken by the offense.
Offenses such as theft, murder, or adultery typically trigger retaliation on the part of the victim—a behavior evolved to deter future exploitation. Yet unrestrained retaliation can trigger counter-punishment on the part on of offender, which risks escalating into destructive feuds that end up harming not only disputants themselves, but also their kin or allies, who often get embroiled in the conflict. People have thus had a mutual interest in designing and retaining punitive procedures that seemed well-suited to reconciling disputants and limiting conflict between offender and victim after offenses.
Our results show that the justice systems of the Kiowa, the Mentawai, and the Nuer exhibit many features consistent with this restorative function. First, punishment was entangled with compensation: the offender had to pay costs, yes, but often while providing the victim with material benefits such as pigs, cattle, horses, or other valuables. This allows the victim to experience forgiveness and appeases their urge for revenge, preventing harsh retaliations that could spark destructive feuds. Second, the prescribed level of punishment and compensation was proportionate to the tort inflicted on the victim. This, again, is well suited to satisfy the victim’s urge for revenge while avoiding disproportionate retaliation that could spark counter-revenge. Third, while third parties rarely punished, they often intervened to mediate, pacify disputants, and help them achieve peaceful resolution.
Our results echo longstanding observations of legal anthropologists. For decades, researchers working in small-scale, politically decentralized societies have insisted on the rarity of third-party punishment, the private nature of law, and the compensatory logic of justice. Yet these features of punishment have been largely ignored by evolutionary social scientists and psychologists, leading to misleading assumptions about the evolution of punishment and cooperation. Rather than third-party punishers enforcing cooperation, as often occurs in large-scale, anonymous societies, social order in most human societies may have been far more decentralized, emerging from individuals and families negotiating over how to best treat each other.
Read the original paper: Fitouchi, L., & Singh, M. (2023). Punitive justice serves to restore reciprocal cooperation in three small-scale societies. Evolution & Human Behavior, 44(5), 502-514.
Long-distance friends are good friends to have around
/in Newsletter– by Kristopher Smith
(Image description: Women in a Tanzanian fishing village “planting” shared algae lines during low tide.)
Long-distance relationships, whether they be friends, family, or colleagues, are found across the world. And while you might think that long-distance relationships are products of planes, phones, or the internet, long-distance relationships have actually long been an important component of human sociality. For example, obsidian trade networks in East Africa stretching hundreds of kilometers indicate humans were maintaining long-distance relationships as early as 700,000 years ago. Why do people bother being friends with people who live so far away from them?
Friends are one strategy people use to manage risk. Life is unpredictable, and while today you might be well-fed and in good health, tomorrow you might find your fortunes down. Friends help deal with this uncertainty by providing help to one another during down times, giving each other support whenever they need it, regardless of who helped whom last – that is, engaging in need-based sharing. Tracking debts between close-distance friends is a faux pas across cultures, and friends are expected to help each other as long as they are able to help–denying help because a previous act of support has not been reciprocated can be a quick way to end a friendship. Need-based helping is more robust compared to tit-for-tat reciprocity; if friends only helped each other when they can be paid back, then when someone was most in need of help, their friend would be unwilling to help due to concerns of not being repaid. By always helping each other when one of them is in need, friends can be assured they have the support to get through hard times.
But risks in the environment are often clustered in time and space, hitting everyone in a given area at the same time. In these cases, close-distance friends might not be able to provide help. For example, if you are a fisherman depending on a daily catch to feed your family, a large storm that stops you from going out to the ocean for days can spell trouble. And your friends might literally be in the same boat, unable to help you because they too need help. However, if you have friends further down the coast where the storm has not hit, you can reach out to them for support. In other words, long-distance friends are useful for providing access to resources not available in your local environment. Even when times are good, these friends can get you resources you cannot get locally, such as a place to stay while traveling or information about job opportunities in another city.
While long-distance friends can be useful for supplementing help from close-distance friends by providing access to non-local resources, they also present additional challenges. First, long-distance friends are seen less frequently. This makes it harder to monitor whether a friend is declining to help because they are intentionally refusing to help–and thus defecting on their cooperative partner–or are simply unable to help at the time. Second, they are unlikely to belong to the same cooperative institutions, which among close-distance friends can help smooth over disagreements and provide outside enforcement of cooperative agreements. For example, when friends attend the same religious congregation, they can appeal to their religious leader to mediate conflict between them, such as by reminding a stingy friend of the virtue of helping. Taken together, it is easier for long-distance friends to cheat the need-based sharing common among friends, and it is harder to encourage them to help via institutions. As a result, long-distance friends may be less likely to rely on need-based sharing and more carefully track debts between each other, only providing help when accounts are nearly balanced between each other.
Rural villages along the coast of the Western Indian Ocean in Tanzania primarily rely on small-scale fishing for their food and income. The productivity of different fishing spots changes across seasons, and to access better fishing spots, fishermen must travel to different villages, where they meet and befriend locals. Other professions connected to fisheries, such as fish processors and fishmongers, also travel to where business is booming, forming useful relationships beyond their home villages. As a result of people following the fish, they build a sprawling network of long-distance friendships between villages. Do these long-distance friends provide the same kind of need-based support as friends living in the same village?
To answer this, my collaborators and I interviewed 917 people in 21 fishing villages in the Tanga Region of Tanzania, asking participants about their friendships in their village and neighboring villages. We randomly chose one friend in their village and one friend in another village to ask about different kinds of help the participant had received from each friend, such as advice, loaned tools, and cash help in the form of loans and gifts. Loans and gifts offer interesting insight into account keeping because loans have an explicit expectation that the help is repaid in the future–it is literally tracking debts between friends, which is usually not the case for gifts.
Unsurprisingly, people were more likely to receive help in general from close-distance than long-distance friends. However, contrary to our prediction, long-distance friends were actually a little less likely to give loans than gifts, while close-distance friends were a little more likely to give loans than gifts. Long-distance friends did give larger loans than gifts, which at first glance is consistent with our predictions, but looking at why these loans were given revealed that long-distance friends were much more likely to give a loan for business investments compared to close-distance friends. And help as a business investment was the largest kind of help given, for both loans and gifts and for close- and long-distance friends. That is, long-distance friends are often providing loans for big-ticket items like new boats or ring nets. In many of these villages, that kind of cash is just not available to give out, and people are likely piecing together investments from their wider social network, receiving support that is unavailable locally.
While there were some differences between close- and long-distance friends, more notable was the similarity between them. Regardless of distance, the biggest kinds of help given by friends was advice, help when sick, and help with getting work done. And even among cash help, the differences between close- and long-distance friends were small. Previous research on long-distance relationships have focused on ritual relationships, such as osotua among the Maasai, where social and supernatural sanctions help reinforce the cooperative relationships, making them robust sources of support. Here, even among friendships with no special status, distance does not impede support flowing freely between friends.
The fact that long-distance friends provide the same quality support as close-distance friends has important implications for cooperation more broadly. Many collective action projects, such as managing open-access fisheries, require people to cooperate at a distance with people living in other communities. This can be a hard obstacle to overcome. Just like cooperation between friends, difficulty in monitoring others and lack of shared institutions can breed a lack of trust and interest in cooperating with people who live in other communities, causing between-community collective action efforts to fail. But if long-distance friends provide support to one another, then this can foster interdependence between them–their fortunes are tied together, such that a person providing help to their friend indirectly benefits because their friend is more able to help them in the future. This interdependence makes it more likely for them to participate in between-community collective action because of their mutual benefits from it. Long-distance friends then can be a foundation to build-up larger cooperative efforts, bridging communities with their shared interest, an idea my collaborators and I are currently testing in this context.
A friend in need is a friend indeed. And as it turns out, this is true regardless of whether your friend is your neighbor or lives a world away. People lean on their friends to get through hard times and long-distance friends can provide some kinds of help that close-distance friends might not be able to provide, no strings attached.
Read the original article: Smith, K.M., Pisor, A.C., Aron, B., Bernard, K., Fimbo, P., Kimesera, R., & Borgerhoff Mulder, M. (2023). Friends near and afar, through thick and thin: comparing contingency of help between close-distance and long-distance friends in Tanzanian fishing villages. Evolution & Human Behavior, 44(5), 454-465.
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/in NewsletterEvolutionary faculty job in Oklahoma
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