Eleanor A. Power

We conduct experimental economic games designed to study how reputational concern influences people's economic decisions. Donors make a series of ``Dictator Game'' decisions, splitting an endowment between themselves and a recipient. Crucially, recipients are not anonymous strangers, but are other individuals from the same community as the donor, presented to donors via photographs. By varying the identity of the recipient and whether or not the recipient will come to know the identity of the donor, we can effectively vary the reputational stake of the donor's decision. We expect that when Dictator Game allocation decisions have greater reputational stakes, donors will be more generous.Greater reputational stakes could arise from: the revelation of the donor's identity, the social proximity of donor and recipient, and the general network position of donor and recipient, all of which we consider. We conduct these games in eight rural communities in five countries, where we already have full sociodemographic and social network data on players and their connections. While there is substantial cross-site variation in the amounts given, we find strikingly similar effects of revelation and social proximity on allocation decisions: in all sites, donors give only slightly more in contexts where their identity is revealed, as opposed to being kept anonymous. Whether anonymous or revealed, donors give appreciably less to recipients who are more socially distant, with the largest drop occurring between immediate connections and connections-of-connections. We find mixed evidence that the effect of revealing a donor's identity diminishes with social distance. Finally, we do not find clear effects for the network position of donor and recipient. These results improve our understanding of how economic behaviour is intertwined with reputational dynamics in a wide variety of contexts. While altruistic behaviour does increase with reputational concern cross-culturally, it also appears that in tight-knit, small communities, absence of reputational concern may, contra expectations, lead only to a small increase in selfishness.

Networked datasets can be enriched by different types of information about individual nodes or edges. However, most existing methods for analyzing such datasets struggle to handle the complexity of heterogeneous data, often requiring substantial model-specific analysis. In this article, we develop a probabilistic generative model to perform inference in multilayer networks with arbitrary types of information. Our approach employs a Bayesian framework combined with the Laplace matching technique to ease interpretation of inferred parameters. Furthermore, the algorithmic implementation relies on automatic differentiation, avoiding the need for explicit derivations. This makes our model scalable and flexible to adapt to any combination of input data. We demonstrate the effectiveness of our method in detecting overlapping community structures and performing various prediction tasks on heterogeneous multilayer data, where nodes and edges have different types of attributes. Additionally, we showcase its ability to unveil a variety of patterns in a social support network among villagers in rural India by effectively utilizing all input information in a meaningful way.

To address claims of human exceptionalism, we determine where humans fit within the greater mammalian distribution of reproductive inequality. We show that humans exhibit lower reproductive skew (i.e., inequality in the number of surviving offspring) among males and smaller sex differences in reproductive skew than most other mammals, while nevertheless falling within the mammalian range. Additionally, female reproductive skew is higher in polygynous human populations than in polygynous nonhumans mammals on average. This patterning of skew can be attributed in part to the prevalence of monogamy in humans compared to the predominance of polygyny in nonhuman mammals, to the limited degree of polygyny in the human societies that practice it, and to the importance of unequally held rival resources to women’s fitness. The muted reproductive inequality observed in humans appears to be linked to several unusual characteristics of our species—including high levels of cooperation among males, high dependence on unequally held rival resources, complementarities between maternal and paternal investment, as well as social and legal institutions that enforce monogamous norms.

Social network data are often constructed by incorporating reports from multiple individuals. However, it is not obvious how to reconcile discordant responses from individuals. There may be particular risks with multiply reported data if people’s responses reflect normative expectations—such as an expectation of balanced, reciprocal relationships. Here, we propose a probabilistic model that incorporates ties reported by multiple individuals to estimate the unobserved network structure. In addition to estimating a parameter for each reporter that is related to their tendency of over- or under-reporting relationships, the model explicitly incorporates a term for ‘mutuality’, the tendency to report ties in both directions involving the same alter. Our model’s algorithmic implementation is based on variational inference, which makes it efficient and scalable to large systems. We apply our model to data from a Nicaraguan community collected with a roster-based design and 75 Indian villages collected with a name-generator design. We observe strong evidence of ‘mutuality’ in both datasets, and find that this value varies by relationship type. Consequently, our model estimates networks with reciprocity values that are substantially different than those resulting from standard deterministic aggregation approaches, demonstrating the need to consider such issues when gathering, constructing, and analysing survey-based network data.

Social network data are often constructed by incorporating reports from multiple individuals. However, it is not obvious how to reconcile discordant responses from individuals. There may be particular risks with multiply-reported data if people's responses reflect normative expectations -- such as an expectation of balanced, reciprocal relationships. Here, we propose a probabilistic model that incorporates ties reported by multiple individuals to estimate the unobserved network structure. In addition to estimating a parameter for each reporter that is related to their tendency of over- or under-reporting relationships, the model explicitly incorporates a term for ``mutuality,'' the tendency to report ties in both directions involving the same alter. Our model's algorithmic implementation is based on variational inference, which makes it efficient and scalable to large systems. We apply our model to data from 75 Indian villages collected with a name-generator design, and a Nicaraguan community collected with a roster-based design. We observe strong evidence of ``mutuality'' in both datasets, and find that this value varies by relationship type. Consequently, our model estimates networks with reciprocity values that are substantially different than those resulting from standard deterministic aggregation approaches, demonstrating the need to consider such issues when gathering, constructing, and analysing survey-based network data.

Helping behaviour is thought to play a major role in the evolution of group-living animals. Yet, it is unclear to what extent human males and human females use the same strategies to secure support. Accordingly, we investigate help-seeking over a 5-year period in relation to gender using data from virtually all adults in two Tamil villages ( N = 782). Simulations of network dynamics (i.e. stochastic actor-oriented models) calibrated to these data broadly indicate that women are more inclined than men to create and maintain supportive bonds via multiple mechanisms of cooperation (e.g. reciprocity, kin bias, friend bias, generalized exchange). However, gender-related differences in the simulated dynamics of help-seeking are modest, vary based on structural position (e.g. out-degree), and do not appear to translate to divergence in the observed structure of respondents' egocentric networks. Findings ultimately suggest that men and women in the two villages are similarly social but channel their sociality differently. This article is part of the theme issue ‘Cooperation among women: evolutionary and cross-cultural perspectives’.

Across species, social hierarchies are often governed by dominance relations. In humans, where there are multiple culturally valued axes of distinction, social hierarchies can take a variety of forms and need not rest on dominance relations. Consequently, humans navigate multiple domains of status, i.e. relative standing. Importantly, while these hierarchies may be constructed from dyadic interactions, they are often more fundamentally guided by subjective peer evaluations and group perceptions. Researchers have typically focused on the distinct elements that shape individuals’ relative standing, with some emphasizing individual-level attributes and others outlining emergent macro-level structural outcomes. Here, we synthesize work across the social sciences to suggest that the dynamic interplay between individual-level and meso-level properties of the social networks in which individuals are embedded are crucial for understanding the diverse processes of status differentiation across groups. More specifically, we observe that humans not only navigate multiple social hierarchies at any given time but also simultaneously operate within multiple, overlapping social networks. There are important dynamic feedbacks between social hierarchies and the characteristics of social networks, as the types of social relationships, their structural properties, and the relative position of individuals within them both influence and are influenced by status differentiation. This article is part of the theme issue ‘The centennial of the pecking order: current state and future prospects for the study of dominance hierarchies’.

Research in various disciplines has highlighted that humans are uniquely able to solve the problem of cooperation through the informal mechanisms of reputation and gossip. Reputation coordinates the evaluative judgments of individuals about one another. Direct observation of actions and communication are the essential routes that are used to establish and update reputations. In large groups, where opportunities for direct observation are limited, gossip becomes an important channel to share individual perceptions and evaluations of others that can be used to condition cooperative action. Although reputation and gossip might consequently support large-scale human cooperation, four puzzles need to be resolved to understand the operation of reputation-based mechanisms. First, we need empirical evidence of the processes and content that form reputations and how this may vary cross-culturally. Second, we lack an understanding of how reputation is determined from the muddle of imperfect, biased inputs people receive. Third, coordination between individuals is only possible if reputation sharing and signaling is to a large extent reliable and valid. Communication, however, is not necessarily honest and reliable, so theoretical and empirical work is needed to understand how gossip and reputation can effectively promote cooperation despite the circulation of dishonest gossip. Fourth, reputation is not constructed in a social vacuum; hence we need a better understanding of the way in which the structure of interactions affects the efficiency of gossip for establishing reputations and fostering cooperation.

Performing a dramatic act of religious devotion, creating an art exhibit, or releasing a new product are all examples of public acts that signal quality and contribute to building a reputation. Signalling theory predicts that these public displays can reliably reveal quality. However, data from ethnographic work in South India suggests that more prominent individuals gain more from reputation-building religious acts than more marginalized individuals. To understand this phenomenon, we extend signalling theory to include variation in people’s social prominence or social capital, first with an analytical model and then with an agent-based model. We consider two ways in which social prominence/capital may alter signalling: (i) it impacts observers’ priors, and (ii) it alters the signallers’ pay-offs. These two mechanisms can result in both a ‘reputational shield,’ where low quality individuals are able to ‘pass’ as high quality thanks to their greater social prominence/capital, and a ‘reputational poverty trap,’ where high quality individuals are unable to improve their standing owing to a lack of social prominence/capital. These findings bridge the signalling theory tradition prominent in behavioural ecology, anthropology and economics with the work on status hierarchies in sociology, and shed light on the complex ways in which individuals make inferences about others. This article is part of the theme issue ‘The language of cooperation: reputation and honest signalling’.

Large-scale non-kin cooperation is a unique ingredient of human success. This type of cooperation is challenging to explain in a world of self-interested individuals. There is overwhelming empirical evidence from different disciplines that reputation and gossip promote cooperation in humans in different contexts. Despite decades of research, important details of reputation systems are still unclear. Our goal with this theme issue is to promote an interdisciplinary approach that allows us to explore and understand the evolution and maintenance of reputation systems with a special emphasis on gossip and honest signalling. The theme issue is organized around four main questions: What are the necessary conditions for reputation-based systems? What is the content and context of reputation systems? How can reputations promote cooperation? And, what is the role of gossip in maintaining reputation systems and thus cooperation? This article is part of the theme issue ‘The language of cooperation: reputation and honest signalling’.