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Institutions have been described as 'the humanly devised constraints that structure political, economic, and social interactions.' This broad definition of institutions spans social norms, laws, companies, and even scientific theories. We describe a non-equilibrium, multi-scale learning framework supporting institutional quasi-stationarity, periodicity, and switching. Individuals collectively construct ledgers constituting institutions. Agents read only a part of the ledger-positive and negative opinions of an institution-its "public position" whose value biases one agent's preferences over those of rivals. These positions encode collective perception and action relating to laws, the power of parties in political office, and advocacy for scientific theories. We consider a diversity of complex temporal phenomena in the history of social and research culture (e.g. scientific revolutions) and provide a new explanation for ubiquitous cultural resistance to change and novelty-a systemic endowment effect through hysteresis., Competing Interests: NO authors have competing interests.

The COVID-19 pandemic has highlighted the inequitable access to resources, leading to a disproportionate burden of disease in vulnerable communities in the USA. However, these inequities in health outcomes are not limited to COVID-19. Approximately 18% of cancers are related to dietary behaviors and excess body weight. Underserved communities, such as minority racial/ethnic groups living in neighborhoods of low socioeconomic status, experience barriers to healthy eating including lack of access to high-quality healthy foods and higher availability of unhealthy foods and beverages in local retail food outlets. Strikingly, these same populations are more likely to die from cancers related to dietary intake and obesity like colorectal, liver, and pancreatic cancers. To reduce cancer inequities, policy makers can act by supporting programs that incentivize healthy food purchases and improve the local food environment in underserved communities., (© Society of Behavioral Medicine 2021. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

In the United States, >5.4 million people age 65 and older are affected by cognitive impairment and dementia, including Alzheimer's disease. African Americans are more likely than non-Hispanic whites to suffer from these disorders. Obesity is linked to accelerated age-related cognitive decline, and weight loss through caloric restriction is a potential strategy to prevent this cognitive impairment. Adherence to a healthful dietary pattern, such as the Mediterranean Diet (MedDiet), has also shown positive effects on reducing risk for dementia. African Americans are disproportionately affected by obesity and have less healthful diets than non-Hispanic whites. We present baseline characteristics from a three-arm randomized controlled trial that randomized 185 obese (BMI ≥ 30 kg/m 2 and ≤ 50 kg/m 2 ) healthy older adults (55-85 years of age) to: 1) Typical Diet Control (TDC); 2) MedDiet alone (MedDiet-A) intervention; or 3) MedDiet caloric restricted intervention to promote weight loss (MedDiet-WL). The majority of the sample was African American (91.4%) and female (85.9%). The two active interventions (MedDiet-A and MedDiet-WL) met once weekly for 8 months, and the TDC received weekly general health newsletters. Baseline data were collected between January 2017 and July 2019 in Chicago, IL. In our sample, closer adherence to a MedDiet pattern was associated with higher attention and information processing (AIP) and higher executive functioning (EF). Consistent with the literature, we saw that older participants performed more poorly on the cognitive assessments than younger participants, and women outperformed men across verbally mediated tasks, especially ones related to learning and memory., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Previous systematic reviews have examined the efficacy of obesity prevention interventions within early childcare/education settings. Often lacking in these reviews is reporting on external validity, which continues to be underemphasized compared to internal validity. More attention to external validity would help better translate evidence-based interventions to real-world settings. This systematic review aimed to determine the availability of data on both internal and external validity across dimensions of the Reach, Efficacy/Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework in studies reporting on obesity prevention interventions in early childcare/education settings. Inclusion criteria included: randomized controlled trials, early childcare/education setting, targeted children 2-6 years old, addressed both diet and physical activity, collected measures of weight status and diet and/or physical activity, and published within the last 10 years. Searches were conducted in ERIC, PsychInfo, and PubMed; 23 studies met inclusion criteria. A validated RE-AIM abstraction tool was used to code studies. Most commonly reported dimensions were Reach (62.3%), Implementation (53.5%), and Efficacy/Effectiveness (48.7%). Adoption (21.7%) and Maintenance (11.6%) were less often reported. All studies reported on primary outcomes, but few reported on RE-AIM indicators of characteristics of participation and adoption, quality of life, methods used to identify staff, staff inclusion/exclusion criteria and adoption rates, implementation fidelity, measures of cost to start-up and deliver the intervention, and indicators of maintenance. This systematic review underscores the need for more focus on external validity to inform replication, dissemination, and implementation so that evidence-based early childcare/education obesity interventions can be generalized to real-world settings., (© Society of Behavioral Medicine 2020. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Armed conflict data display features consistent with scaling and universal dynamics in both social and physical properties like fatalities and geographic extent. We propose a randomly branching armed conflict model to relate the multiple properties to one another. The model incorporates a fractal lattice on which conflict spreads, uniform dynamics driving conflict growth, and regional virulence that modulates local conflict intensity. The quantitative constraints on scaling and universal dynamics we use to develop our minimal model serve more generally as a set of constraints for other models for armed conflict dynamics. We show how this approach akin to thermodynamics imparts mechanistic intuition and unifies multiple conflict properties, giving insight into causation, prediction, and intervention timing.

Recent work suggests that collective computation of social structure can minimize uncertainty about the social and physical environment, facilitating adaptation. We explore these ideas by studying how fission-fusion social structure arises in spider monkey ( Ateles geoffroyi ) groups, exploring whether monkeys use social knowledge to collectively compute subgroup size distributions adaptive for foraging in variable environments. We assess whether individual decisions to stay in or leave subgroups are conditioned on strategies based on the presence or absence of others. We search for this evidence in a time series of subgroup membership. We find that individuals have multiple strategies, suggesting that the social knowledge of different individuals is important. These stay-leave strategies provide microscopic inputs to a stochastic model of collective computation encoded in a family of circuits. Each circuit represents an hypothesis for how collectives combine strategies to make decisions, and how these produce various subgroup size distributions. By running these circuits forward in simulation we generate new subgroup size distributions and measure how well they match food abundance in the environment using transfer entropies. We find that spider monkeys decide to stay or go using information from multiple individuals and that they can collectively compute a distribution of subgroup size that makes efficient use of ephemeral sources of nutrition. We are able to artificially tune circuits with subgroup size distributions that are a better fit to the environment than the observed. This suggests that a combination of measurement error, constraint, and adaptive lag are diminishing the power of collective computation in this system. These results are relevant for a more general understanding of the emergence of ordered states in multi-scale social systems with adaptive properties-both natural and engineered., (Copyright © 2020 Ramos-Fernandez, Smith Aguilar, Krakauer and Flack.)

Despite the near universal assumption of individuality in biology, there is little agreement about what individuals are and few rigorous quantitative methods for their identification. Here, we propose that individuals are aggregates that preserve a measure of temporal integrity, i.e., "propagate" information from their past into their futures. We formalize this idea using information theory and graphical models. This mathematical formulation yields three principled and distinct forms of individuality-an organismal, a colonial, and a driven form-each of which varies in the degree of environmental dependence and inherited information. This approach can be thought of as a Gestalt approach to evolution where selection makes figure-ground (agent-environment) distinctions using suitable information-theoretic lenses. A benefit of the approach is that it expands the scope of allowable individuals to include adaptive aggregations in systems that are multi-scale, highly distributed, and do not necessarily have physical boundaries such as cell walls or clonal somatic tissue. Such individuals might be visible to selection but hard to detect by observers without suitable measurement principles. The information theory of individuality allows for the identification of individuals at all levels of organization from molecular to cultural and provides a basis for testing assumptions about the natural scales of a system and argues for the importance of uncertainty reduction through coarse-graining in adaptive systems.

This study examined fruit and vegetable intake by food store type shopped among US Hispanics. Using National Health and Nutrition Examination Survey 2011-2012 data, T test and chi-square tests examined differences between Hispanic consumers by food store type. Negative binomial regression analyses estimated associations between fruit and vegetable intake and food store type. Hispanics who only purchased fruits and vegetables from convenience stores were younger and more likely US born. They reported lower intakes of fruit and vegetables than individuals who purchased these foods from supermarket/grocery stores. Those who primarily purchased fruits and vegetables from supermarkets/grocery stores consumed 0.92 (p < .001) greater fruit cup equivalents and 0.26 (p = .001) greater vegetable cup equivalents than those who only purchased from convenience stores. Research on the influence of shopping in multiple food store types is needed to develop targeted in-store intervention strategies to encourage healthier food purchases. Results provide support for policy-level research such as minimum stocking requirements for healthy foods in convenience stores.

In many biological systems, the functional behavior of a group is collectively computed by the system's individual components. An example is the brain's ability to make decisions via the activity of billions of neurons. A long-standing puzzle is how the components' decisions combine to produce beneficial group-level outputs, despite conflicts of interest and imperfect information. We derive a theoretical model of collective computation from mechanistic first principles, using results from previous work on the computation of power structure in a primate model system. Collective computation has two phases: an information accumulation phase, in which (in this study) pairs of individuals gather information about their fighting abilities and make decisions about their dominance relationships, and an information aggregation phase, in which these decisions are combined to produce a collective computation. To model information accumulation, we extend a stochastic decision-making model-the leaky integrator model used to study neural decision-making-to a multiagent game-theoretic framework. We then test alternative algorithms for aggregating information-in this study, decisions about dominance resulting from the stochastic model-and measure the mutual information between the resultant power structure and the "true" fighting abilities. We find that conflicts of interest can improve accuracy to the benefit of all agents. We also find that the computation can be tuned to produce different power structures by changing the cost of waiting for a decision. The successful application of a similar stochastic decision-making model in neural and social contexts suggests general principles of collective computation across substrates and scales.

Downward causation is the controversial idea that 'higher' levels of organization can causally influence behaviour at 'lower' levels of organization. Here I propose that we can gain traction on downward causation by being operational and examining how adaptive systems identify regularities in evolutionary or learning time and use these regularities to guide behaviour. I suggest that in many adaptive systems components collectively compute their macroscopic worlds through coarse-graining. I further suggest we move from simple feedback to downward causation when components tune behaviour in response to estimates of collectively computed macroscopic properties. I introduce a weak and strong notion of downward causation and discuss the role the strong form plays in the origins of new organizational levels. I illustrate these points with examples from the study of biological and social systems and deep neural networks.This article is part of the themed issue 'Reconceptualizing the origins of life'., (© 2017 The Authors.)

In biological systems, prolonged conflict is costly, whereas contained conflict permits strategic innovation and refinement. Causes of variation in conflict size and duration are not well understood. We use a well-studied primate society model system to study how conflicts grow. We find conflict duration is a 'first to fight' growth process that scales superlinearly, with the number of possible pairwise interactions. This is in contrast with a 'first to fail' process that characterizes peaceful durations. Rescaling conflict distributions reveals a universal curve, showing that the typical time scale of correlated interactions exceeds nearly all individual fights. This temporal correlation implies collective memory across pairwise interactions beyond those assumed in standard models of contagion growth or iterated evolutionary games. By accounting for memory, we make quantitative predictions for interventions that mitigate or enhance the spread of conflict. Managing conflict involves balancing the efficient use of limited resources with an intervention strategy that allows for conflict while keeping it contained and controlled., (© 2017 The Author(s).)

A central question in cognitive neuroscience is how unitary, coherent decisions at the whole organism level can arise from the distributed behavior of a large population of neurons with only partially overlapping information. We address this issue by studying neural spiking behavior recorded from a multielectrode array with 169 channels during a visual motion direction discrimination task. It is well known that in this task there are two distinct phases in neural spiking behavior. Here we show Phase I is a distributed or incompressible phase in which uncertainty about the decision is substantially reduced by pooling information from many cells. Phase II is a redundant or compressible phase in which numerous single cells contain all the information present at the population level in Phase I, such that the firing behavior of a single cell is enough to predict the subject's decision. Using an empirically grounded dynamical modeling framework, we show that in Phase I large cell populations with low redundancy produce a slow timescale of information aggregation through critical slowing down near a symmetry-breaking transition. Our model indicates that increasing collective amplification in Phase II leads naturally to a faster timescale of information pooling and consensus formation. Based on our results and others in the literature, we propose that a general feature of collective computation is a "coding duality" in which there are accumulation and consensus formation processes distinguished by different timescales.

Many adaptive systems sit near a tipping or critical point. For systems near a critical point small changes to component behaviour can induce large-scale changes in aggregate structure and function. Criticality can be adaptive when the environment is changing, but entails reduced robustness through sensitivity. This tradeoff can be resolved when criticality can be tuned. We address the control of finite measures of criticality using data on fight sizes from an animal society model system (Macaca nemestrina, n=48). We find that a heterogeneous, socially organized system, like homogeneous, spatial systems (flocks and schools), sits near a critical point; the contributions individuals make to collective phenomena can be quantified; there is heterogeneity in these contributions; and distance from the critical point (DFC) can be controlled through biologically plausible mechanisms exploiting heterogeneity. We propose two alternative hypotheses for why a system decreases the distance from the critical point.

In biological function emerges from the interactions of components with only partially aligned interests. An example is the brain-a large aggregation of neurons capable of producing unitary, coherent output. A theory for how such aggregations produce coherent output remains elusive. A first question we might ask is how collective is the behavior of the components? Here we introduce two properties of collectivity and illustrate how these properties can be quantified using approaches from information theory and statistical physics. First, amplification quantifies the sensitivity of the large scale to information at the small scale and is related to the notion of criticality in statistical physics. Second, decomposability reveals the extent to which aggregate behavior is reducible to individual contributions or is the result of synergistic interactions among components forming larger subgroups. These measures facilitate identification of causally important components and subgroups that might be experimentally manipulated to study the evolution and controllability of biological circuits and their outputs., (Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.)

To inform the design of a multilevel in-store intervention, this qualitative study utilized in-depth semistructured interviews with 28 managers and 10 employees of small-to-medium-sized Latino food stores (tiendas) in San Diego, California, to identify factors within the tienda that may influence Latino customers' grocery-shopping experiences and behaviors. Qualitative data analysis, guided by grounded theory, was performed using open coding. Results suggest that future interventions should focus on the physical (i.e., built structures) and social (i.e., economic and sociocultural) dimensions of store environments, including areas where the two dimensions interact, to promote the purchase of healthy food among customers.

A hallmark of human communication is vocal turn taking. Until recently, turn taking was thought to be unique to humans but new data indicate that marmosets, a new world monkey, take turns when vocalizing too., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Biological and social networks are composed of heterogeneous nodes that contribute differentially to network structure and function. A number of algorithms have been developed to measure this variation. These algorithms have proven useful for applications that require assigning scores to individual nodes-from ranking websites to determining critical species in ecosystems-yet the mechanistic basis for why they produce good rankings remains poorly understood. We show that a unifying property of these algorithms is that they quantify consensus in the network about a node's state or capacity to perform a function. The algorithms capture consensus by either taking into account the number of a target node's direct connections, and, when the edges are weighted, the uniformity of its weighted in-degree distribution (breadth), or by measuring net flow into a target node (depth). Using data from communication, social, and biological networks we find that that how an algorithm measures consensus-through breadth or depth- impacts its ability to correctly score nodes. We also observe variation in sensitivity to source biases in interaction/adjacency matrices: errors arising from systematic error at the node level or direct manipulation of network connectivity by nodes. Our results indicate that the breadth algorithms, which are derived from information theory, correctly score nodes (assessed using independent data) and are robust to errors. However, in cases where nodes "form opinions" about other nodes using indirect information, like reputation, depth algorithms, like Eigenvector Centrality, are required. One caveat is that Eigenvector Centrality is not robust to error unless the network is transitive or assortative. In these cases the network structure allows the depth algorithms to effectively capture breadth as well as depth. Finally, we discuss the algorithms' cognitive and computational demands. This is an important consideration in systems in which individuals use the collective opinions of others to make decisions.

Animals living in groups collectively produce social structure. In this context individuals make strategic decisions about when to cooperate and compete. This requires that individuals can perceive patterns in collective dynamics, but how this pattern extraction occurs is unclear. Our goal is to identify a model that extracts meaningful social patterns from a behavioral time series while remaining cognitively parsimonious by making the fewest demands on memory. Using fine-grained conflict data from macaques, we show that sparse coding, an important principle of neural compression, is an effective method for compressing collective behavior. The sparse code is shown to be efficient, predictive, and socially meaningful. In our monkey society, the sparse code of conflict is composed of related individuals, the policers, and the alpha female. Our results suggest that sparse coding is a natural technique for pattern extraction when cognitive constraints and small sample sizes limit the complexity of inferential models. Our approach highlights the need for cognitive experiments addressing how individuals perceive collective features of social organization.

To build a theory of social complexity, we need to understand how aggregate social properties arise from individual interaction rules. Here, I review a body of work on the developmental dynamics of pigtailed macaque social organization and conflict management that provides insight into the mechanistic causes of multi-scale social systems. In this model system coarse-grained, statistical representations of collective dynamics are more predictive of the future state of the system than the constantly in-flux behavioural patterns at the individual level. The data suggest that individuals can perceive and use these representations for strategical decision-making. As an interaction history accumulates the coarse-grained representations consolidate. This constrains individual behaviour and provides the foundations for new levels of organization. The time-scales on which these representations change impact whether the consolidating higher-levels can be modified by individuals and collectively. The time-scales appear to be a function of the 'coarseness' of the representations and the character of the collective dynamics over which they are averages. The data suggest that an advantage of multiple timescales is that they allow social systems to balance tradeoffs between predictability and adaptability. I briefly discuss the implications of these findings for cognition, social niche construction and the evolution of new levels of organization in biological systems.

We review an empirically grounded approach to studying the emergence of collective properties from individual interactions in social dynamics. When individual decision-making rules, strategies, can be extracted from the time-series data, these can be used to construct adaptive social circuits. Social circuits provide a compact description of collective effects by mapping rules at the individual level to statistical properties of aggregates. This defines a simple form of social computation. We consider the properties that complexity measures would need to have to best capture regularities at different level of analysis, from individual rules to circuits to population statistics. One obvious benefit of using the properties and structure of biological and social systems to guide the development of complexity measures is that it is more likely to produce measures that can be applied to data. Principled but pragmatic measures would allow for a rigorous investigation of the relationship between adaptive features at the micro, meso, and macro scales, a long standing goal of evolutionary theory. A second benefit is that empirically grounded complexity measures would facilitate quantitative comparisons of strategies, circuits, and aggregate properties across social systems.

Scientific theories seek to provide simple explanations for significant empirical regularities based on fundamental physical and mechanistic constraints. Biological theories have rarely reached a level of generality and predictive power comparable to physical theories. This discrepancy is explained through a combination of frozen accidents, environmental heterogeneity, and widespread non-linearities observed in adaptive processes. At the same time, model building has proven to be very successful when it comes to explaining and predicting the behavior of particular biological systems. In this respect biology resembles alternative model-rich frameworks, such as economics and engineering. In this paper we explore the prospects for general theories in biology, and suggest that these take inspiration not only from physics, but also from the information sciences. Future theoretical biology is likely to represent a hybrid of parsimonious reasoning and algorithmic or rule-based explanation. An open question is whether these new frameworks will remain transparent to human reason. In this context, we discuss the role of machine learning in the early stages of scientific discovery. We argue that evolutionary history is not only a source of uncertainty, but also provides the basis, through conserved traits, for very general explanations for biological regularities, and the prospect of unified theories of life., (Copyright © 2011. Published by Elsevier Ltd.)

Conflict destabilizes social interactions and impedes cooperation at multiple scales of biological organization. Of fundamental interest are the causes of turbulent periods of conflict. We analyze conflict dynamics in an monkey society model system. We develop a technique, Inductive Game Theory, to extract directly from time-series data the decision-making strategies used by individuals and groups. This technique uses Monte Carlo simulation to test alternative causal models of conflict dynamics. We find individuals base their decision to fight on memory of social factors, not on short timescale ecological resource competition. Furthermore, the social assessments on which these decisions are based are triadic (self in relation to another pair of individuals), not pairwise. We show that this triadic decision making causes long conflict cascades and that there is a high population cost of the large fights associated with these cascades. These results suggest that individual agency has been over-emphasized in the social evolution of complex aggregates, and that pair-wise formalisms are inadequate. An appreciation of the empirical foundations of the collective dynamics of conflict is a crucial step towards its effective management.

A central issue in the evolution of social complexity and the evolution of communication concerns the capacity to communicate about increasingly abstract objects and concepts. Many animals can communicate about immediate behavior, but to date, none have been reported to communicate about behavior during future interactions. In this study, we show that a special, unidirectional, cost-free dominance-related signal used by monkeys (pigtailed macaques: Macaca nemestrina) means submission (immediate behavior) or subordination (pattern of behavior) depending on the context of usage. We hypothesize that to decrease receiver uncertainty that the signal object is subordination, senders shift contextual usage from the conflict context, where the signal evolved, to a peaceful one, in which submission is unwarranted. We predict and find that deceasing receiver uncertainty through peaceful signal exchange facilitates the development of higher quality social relationships: Individuals exchanging the peaceful variant groom and reconcile more frequently and fight less frequently than individuals exchanging signals only in the conflict context or no signals. We rule out alternative hypotheses, including an underlying reciprocity rule, temperament, and proximity effects. Our results suggest that primates can communicate about behavioral patterns when these concern relationship rules. The invention of signals decreasing uncertainty about relationship state is likely to have been critical for the evolution of social complexity and to the emergence of robust power structures that feed down to influence rapidly changing individual behavior.

In animal societies, conflicts can be resolved by combatants or through third-party intervention. In gregarious species, conflicts among pairs can spread to involve multiple individuals. In the case of large conflicts, containment and termination of aggression by third parties is important. Successful intervention relies on consensus among combatants about the intervener's capacity to use force. We refer to this consensus as power. We measure it and study how it arises, using as our model system a pigtailed macaque (Macaca nemestrina) society. In macaques, the degree to which one individual perceives another as capable of using force is communicated using a special dominance signal. Group consensus about an individual's capacity to use force arises from the network of signaling interactions. We derive a formalism to quantify consensus in the network. We find that the power distribution is fat tailed and power is a strong predictor of social variables including request for support, intervention cost, and intensity. We develop models to show how dominance-signaling strategies promote robust power distributions despite individual signaling errors. We suggest that when considering correlated interactions among many individuals it can be more useful to emphasize coarse-grained information stored at the group level--behavioral macrostates--over detailed information at the individual level.

All organisms interact with their environment, and in doing so shape it, modifying resource availability. Termed niche construction, this process has been studied primarily at the ecological level with an emphasis on the consequences of construction across generations. We focus on the behavioural process of construction within a single generation, identifying the role a robustness mechanism--conflict management--has in promoting interactions that build social resource networks or social niches. Using 'knockout' experiments on a large, captive group of pigtailed macaques (Macaca nemestrina), we show that a policing function, performed infrequently by a small subset of individuals, significantly contributes to maintaining stable resource networks in the face of chronic perturbations that arise through conflict. When policing is absent, social niches destabilize, with group members building smaller, less diverse, and less integrated grooming, play, proximity and contact-sitting networks. Instability is quantified in terms of reduced mean degree, increased clustering, reduced reach, and increased assortativity. Policing not only controls conflict, we find it significantly influences the structure of networks that constitute essential social resources in gregarious primate societies. The structure of such networks plays a critical role in infant survivorship, emergence and spread of cooperative behaviour, social learning and cultural traditions.

Conflict management mechanisms have a direct, critical effect on system robustness because they mitigate conflict intensity and help repair damaged relationships. However, robustness mechanisms can also have indirect effects on system integrity by facilitating interactions among components. We explore the indirect role that conflict management mechanisms play in the maintenance of social system robustness, using a perturbation technique to 'knockout' components responsible for effective conflict management. We explore the effects of knockout on pigtailed macaque (Macaca nemestrina) social organization, using a captive group of 84 individuals. This system is ideal in addressing this question because there is heterogeneity in performance of conflict management. Consequently, conflict managers can be easily removed without disrupting other control structures. We find that powerful conflict managers are essential in maintaining social order for the benefit of all members of society. We show that knockout of components responsible for conflict management results in system destabilization by significantly increasing mean levels of conflict and aggression, decreasing socio-positive interaction and decreasing the operation of repair mechanisms.

Conflict management is one of the primary requirements for social complexity. Of the many forms of conflict management, one of the rarest and most interesting is third-party policing, or intervening impartially to control conflict. Third-party policing should be hard to evolve because policers personally pay a cost for intervening, while the benefits are diffused over the whole group. In this study we investigate the incidence and costs of policing in a primate society. We report quantitative evidence of non-kin policing in the nonhuman primate, the pigtailed macaque. We find that policing is effective at reducing the intensity of or terminating conflict when performed by the most powerful individuals. We define a measure, social power consensus, that predicts effective low-cost interventions by powerful individuals and ineffective, relatively costly interventions by low-power individuals. Finally, we develop a simple probabilistic model to explore whether the degree to which policing can effectively reduce the societal cost of conflict is dependent on variance in the distribution of power. Our data and simple model suggest that third-party policing effectiveness and cost are dependent on power structure and might emerge only in societies with high variance in power.

Prescriptive social rules are enforced statistical regularities. The authors investigated whether juvenile chimpanzees (Pan troglodytes) recognize and use enforced statistical regularities to guide dyadic play behavior. They hypothesized (a) that proximity of adults, especially mothers of younger play partners, to play bouts will increase the play signaling of older partners and (b) that when juvenile-juvenile play bouts occur in proximity to adults, older partners will play at a lower intensity than when no adults are present. They found that older and younger partners increase their play signaling in the presence of the mothers of younger partners, particularly as the intensity of play bouts increases. In contrast to their hypothesis, older partners played more roughly when the mothers of younger partners were in proximity. These results suggest that juvenile chimpanzees increase play signaling to prevent termination of the play bouts by mothers of younger partners.