Human cooperation at the national evolutionary synthesis center
Notice bibliographique
Résumé
Most scholars of human cooperation fall into one of two groups: social scientists who study collective action, coordination, and other aspects of human cooperation without making use of evolutionary theory, and evolutionary scientists, including evolutionary anthropologists, who ground their studies of human cooperation in Darwinian theory. The theme of our recent book Meeting at Grand Central: Understanding the Social and Evolutionary Roots of Cooperation is that these two groups of scholars could learn a lot from each other. With this in mind, we recently hosted a Catalysis Meeting at the National Evolutionary Synthesis Center (NESCent) on “Synthesizing the Evolutionary and Social Science Approaches to Human Cooperation.” The meeting was held April 5–7, 2013, at NESCent's offices in Durham, North Carolina. The separation between the evolutionary and social science approaches to cooperation has led to a wide range of problems. Because the two groups of scholars rarely read each other's work, redundancies have sometimes cropped up. For example, the basic idea behind the theory of reciprocity was invented and reinvented repeatedly by scholars working in both traditions.1 Similarly, the basic idea behind the theory of byproduct mutualism was discovered independently by both biologists and political scientists; economists have developed the closely related notion of positive externalities.2 In addition, both groups of scholars have independently come to appreciate the important role that reputation plays in fostering cooperation among humans. Even some terminology has been invented multiple times, potentially leading to confusion. For example, anthropologist Marshall Sahlins coined the phrase “generalized reciprocity” more than forty years ago to refer to sharing without account-keeping, and a group of biologists, apparently unaware of how the term is used by social scientists, recently coined it again to refer to a propensity to be generous when one has been the recipient of generosity from others.3, 4 Another type of problem created by this division is more difficult to identify and measure but arguably more important: How much potential research simply does not happen because the scholars in these two camps are so seldom in dialogue with one another? In part because of the different paths they have taken thus far, the insights offered by the two groups of scholars mostly complement rather than compete with one another. Evolutionary scholars have tended to be most impressed by how much humans cooperate, particularly with nonrelatives, as compared to most other species, and have proposed a wide range of models and mechanisms to help explain this pattern. Social scientists, in contrast, have been struck not so much by the frequency with which humans cooperate, but with the frequency with which they fail to do so as compared to the number of instances in which people would benefit from cooperating. Thus, political scientists, economists, and sociologists have examined such obstacles to collective action and coordination as free-riding and a lack of common knowledge. Our NESCent Catalysis Meeting focused on the identification of key questions in the study of human cooperation that would allow the two groups to work together. Including ourselves, thirty-two scholars attended. Our invitees represented a variety of disciplines (anthropology, political science, psychology, and biology), geographical locations (U.S., Japan, Hungary, Denmark, and Canada), topical interests, and career stages. We asked twelve mostly junior participants to give presentations; more senior scholars were cast as discussants and group leaders. Several speakers focused on the genetic underpinnings and psychological mechanisms behind cooperation. In that vein, Darren Schreiber (Central European University, Budapest) made a case for the adaptiveness of human behavioral flexibility, Jaime Settle (William and Mary) conveyed her finding that there are innate differences among individuals that shape the ways in which they respond to political contention, and Chris Dawes (NYU) showed how egalitarianism can facilitate cooperation. Several of the presentations examined the links between evolution and institutions. For example, Drew Gerkey (Oregon State) used his field work in Kamchatka5 to frame a discussion of different kinds of groups and the way they shape cooperation. Montserrat Soler (UC-Santa Barbara) used her data on an Afro-Brazilian religion called Candomblé to analyze the phenomenon of religious leadership, and Michael Bang Petersen (Aarhus University, Denmark) related social exchange and cheater detection theory to the phenomenon of the welfare state.6 Reflecting current scholarship on the central roles that moral judgments and reputations play in cooperation, Peter DeScioli (Harvard) argued that moral rules help coordinate the efforts of those who wish to punish wrongdoers, while Nicole Hess (Washington State, Vancouver) discussed the roles of gossip and reputation in fostering cooperation. Three of the participants examined various aspects of generosity, sharing, and exchange. C. Athena Aktipis (Arizona State) argued that risk pooling is enhanced when people simply give to those in need rather than keep accounts of debt; Shane Macfarlan (Missouri) spoke about labor exchange relationships in a community on the Caribbean island of Dominica7; and Wesley Allen-Arave (New Mexico) described his recent findings on the contributions of U.S. households to charity. Mark Moritz (Ohio State) provided a link between the conference's theme and the study of common pool resources by describing a system of cooperation among pastoralists in northern Cameroon in which no tragedy of the commons occurs despite open access to grazing.8 In addition to the twelve speakers and ourselves, eighteen other scholars also attended some or all of the meeting and took part in the wide-ranging discussions that occurred: Rolando de Aguiar (Rutgers), Jacopo Baggio (Arizona State), Frank Baumgartner (UNC- Chapel Hill), Mark Flinn (Missouri), Matthew Gervais (UCLA), Virginia Gray (UNC- Chapel Hill), Brian Hare (Duke), Patricia Hawley (Kansas), John Hibbing (Nebraska), Daniel Hruschka (Arizona State), Padmini Iyer (Rutgers), Carly Jacobs (Nebraska), Adrian Jaeggi (University of California, Santa Barbara), David Lowery (Penn State), Ben Purzycki (British Columbia), Richard Sosis (Connecticut), Masanori Takezawa (Hokkaido), and Jingzhi Tan (Duke). The meeting also included breakout sessions for working groups of four to six participants on six more specific topics within the overall theme. One group focused on the role that religion plays in cooperation.9-11 Another focused on systems of risk-pooling and food sharing.12-14 Two groups dealt with different aspects of coalitions and coalitional psychology, one focusing on the process of political agenda-setting and the other on morality, reputations, and cooperative partner choice.15-17 One group discussed the neurological, hormonal, and genetic underpinnings of institutions.18-25 One group discussed the emergence of social arrangements that facilitate cooperation in human groups. The meeting was a wonderful experience for us, and, we hope, for our participants as well. Possible outcomes of the meeting include new collaborations, applications for future Catalysis Meetings on more specific topics, and a general sense of excitement regarding the prospect of studying human cooperation in ways that combine insights from both the evolutionary and social sciences.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,007 | 0,004 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».