The pursuit of social rank pervades all human societies and the position that an individual occupies within a hierarchy has important effects on their social and reproductive success. Whilst recent research has indicated that there are two distinct routes to rank attainmentdominance (through the induction of fear) and prestige (through respect and admiration)this empirical evidence has generally provided only a cross-sectional snapshot of how the two processes operate in human hierarchy. Whether dominance and prestige are potentially viable long-term strategies, rather than more effective short-term tactics, for acquiring rank in groups remains an open question. The current research addresses this gap by examining the temporal dynamics between prestige, dominance and social rank using a dynamic, evolutionary approach to understanding human social hierarchy, and thus supplies the first longitudinal empirical assessment of these variables' relationships. Using naturalistic student project groups comprised of 3-5 teammates, the present research tracks the temporal relationships between prestige, dominance and social rank-provided through round-robin teammate-ratings-from the initial formation of collaborative task groups through to the end of a 16-week long academic semester. Results indicate that, whilst dominance and prestige both promoted social rank in unacquainted groups initially and were distinct processes throughout the period examined, only prestige had a positive effect on social rank over time. Further results reveal that the temporal relationship between prestige and social rank was bidirectional, such that acquiring social rank further perpetuates future prestige. Overall, findings present a framework for the longitudinal distinction between prestige and dominance.

We propose that networks of cooperation and allocation of social status co-emerge in human groups. We substantiate this hypothesis with one of the first longitudinal studies of cooperation in a preindustrial society, spanning 8 years. Using longitudinal social network analysis of cooperation among men, we find large effects of kinship, reciprocity and transitivity in the nomination of cooperation partners over time. Independent of these effects, we show that (i) higher-status individuals gain more cooperation partners, and (ii) individuals gain status by cooperating with individuals of higher status than themselves. We posit that human hierarchies are more egalitarian relative to other primates species, owing in part to greater interdependence between cooperation and status hierarchy.

Abstract

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’.

A hallmark of human societies is the scale at which we cooperate with many others, even when they are not closely genetically related to us. One proposed mechanism that helps explain why we cooperate is punishment; cooperation may pay and proliferate if those who free ride on the cooperation of others are punished. Yet this ‘solution’ raises another puzzle of its own: Who will bear the costs of punishing? While the deterrence of free‐riders via punishment serves collective interests, presumably any single individual—who has no direct incentive to punish—is better off letting others pay the costs of punishment. However, emerging theory and evidence indicate that, while punishment may at times be a costly act, certain individuals are better able to ‘afford’ to pay the price of punishment and are often consequentially rewarded with fitness‐enhancing reputation benefits. Synthesizing across these latest lines of research, we propose a novel framework that considers how high status individuals—that is, individuals with greater prestige or dominance—enjoy lower punishment costs. These individuals are thus more willing to punish, and through their punitive action can in turn reap reputational rewards by further gaining more prestige or dominance. These reputational gains, which work in concert to promote the social success of punishers, recoup the costs of punishing. Together, these lines of work suggest that while punishment is often assumed to be altruistic, it need not always depend on altruism, and motivations to punish may at times be self‐interested and driven (whether consciously or unconsciously) by reputational benefits.

Researchers studying social networks and inter-personal sentiments in bounded or small-scale communities face a trade-off between the use of roster-based and free-recall/name-generator-based survey tools. Roster-based methods scale poorly with sample size, and can more easily lead to respondent fatigue; however, they generally yield higher quality data that are less susceptible to recall bias and that require less post-processing. Name-generator-based methods, in contrast, scale well with sample size and are less likely to lead to respondent fatigue. However, they may be more sensitive to recall bias, and they entail a large amount of highly error-prone post-processing after data collection in order to link elicited names to unique identifiers. Here, we introduce an R package, DieTryin, that allows for roster-based dyadic data to be collected and entered as rapidly as name-generator-based data; DieTryin can be used to run network-structured economic games, as well as collect and process standard social network data and round-robin Likert-scale peer ratings. DieTryin automates photograph standardization, survey tool compilation, and data entry. We present a complete methodological workflow using DieTryin to teach end-users its full functionality.

No abstract

There have been recent calls for wider application of generative modelling approaches in applied social network analysis. These calls have been motivated by the limitations of contemporary empirical frameworks, which have generally relied on post hoc permutation methods that do not actively account for interdependence in network data. At present, however, it remains difficult for typical end-users—e.g., field researchers—to apply generative network models, as there is a dearth of openly available software packages that make application of such methods as simple as other, permutation-based methods.Here, we outline the STRAND R package, which provides a suite of generative models for Bayesian analysis of human and non-human animal social network data that can be implemented using simple, base R syntax.To facilitate ease-of-use, we provide a tutorial demonstrating how STRAND can be used to model binary, count, or proportion data using stochastic blockmodels, social relations models, or a combination of the two modelling frameworks.