Animal play and evolution: Seven timely research issues about enigmatic phenomena

The nature of play in animals has been long debated, but progress is being made in characterizing play and its variants, documenting its distribution across vertebrate and invertebrate taxa, describing its mechanisms and development, and proposing testable theories about its origins, evolution, and adaptive functions. To achieve a deeper understanding of the functions and evolution of play, integrative and conceptual advances are needed in neuroscience, computer modeling, phylogenetics, experimental techniques, behavior development, and inter- and intra-specific variation. The special issue contains papers documenting many of these advances. Here, we describe seven timely areas where further research is needed to understand this still enigmatic class of phenomena more fully. Growing empirical and theoretical evidence reveals that play has been crucial in the evolution of behavior and psychology but has been underestimated, if not ignored, in both empirical and theoretical areas of evolutionary biology and neuroscience. Play research has important ramifications for understanding the evolution of cognition, emotion, and culture, and research on animals can be both informative and transformative.

Noise and opinion dynamics: how ambiguity promotes pro-majority consensus in the presence of confirmation bias

Opinion dynamics are affected by cognitive biases and noise. While mathematical models have focused extensively on biases, we still know surprisingly little about how noise shapes opinion patterns. Here, we use an agent-based opinion dynamics model to investigate the interplay between confirmation bias-represented as bounded confidence-and different types of noise. After analysing where noise can enter social interaction, we propose a type of noise that has not been discussed so far, ambiguity noise. While previously considered types of noise acted on agents either before, after or independent of social interaction, ambiguity noise acts on communicated messages, assuming that socially transmitted opinions are inherently noisy. We find that noise can induce agreement when confirmation bias is moderate, but different types of noise require quite different conditions for this effect to occur. An application of our model to the climate change debate shows that at just the right mix of confirmation bias and ambiguity noise, opinions tend to converge to high levels of climate change concern. This result is not observed with the other types. Our findings highlight the importance of considering and distinguishing between the various types of noise and the unique role of ambiguity in opinion formation.

Maintaining Transient Diversity Is a General Principle for Improving Collective Problem Solving

Humans regularly solve complex problems in cooperative teams. A wide range of mechanisms have been identified that improve the quality of solutions achieved by those teams on reaching consensus. We argue that many of these mechanisms work via increasing the transient diversity of solutions while the group attempts to reach a consensus. These mechanisms can operate at the level of individual psychology (e.g., behavioral inertia), interpersonal communication (e.g., transmission noise), or group structure (e.g., sparse social networks). Transient diversity can be increased by widening the search space of possible solutions or by slowing the diffusion of information and delaying consensus. All of these mechanisms increase the quality of the solution at the cost of increased time to reach it. We review specific mechanisms that facilitate transient diversity and synthesize evidence from both empirical studies and diverse formal models-including multiarmed bandits, NK landscapes, cumulative-innovation models, and evolutionary-transmission models. Apparent exceptions to this principle occur primarily when problems are sufficiently simple that they can be solved by mere trial and error or when the incentives of team members are insufficiently aligned. This work has implications for our understanding of collective intelligence, problem solving, innovation, and cumulative cultural evolution.

The miss of the framework

The authors rightly critique existing social sciences approaches. However, they are too quick to dismiss the criticism that their proposed paradigm is atheoretical. Social and cognitive theories are indeed incommensurate, often due to the lack of a unifying framework. Without proper integration with theoretical frameworks, their proposal may merely produce a resource-intensive veneer of thoroughness without substantive improvements to understanding.

Reducing global inequality increases local cooperation: a simple model of group selection with a global externality

Group-structured models often explain the evolution of prosocial activities in terms of selection acting at both individual and group levels. Such models do not typically consider how individuals' behaviours may have consequences beyond the boundaries of their groups. However, many behaviours affect global environmental variables, including climate change and ecosystem fragility. Against this background, we propose a simple model of multi-level selection in the presence of global externalities. In our model, group members can cooperate in a social dilemma with the potential for group-level benefits. The actions of cooperators also have global consequences, which can be positive (a global good) or negative (a global bad). We use simulations to consider scenarios in which the effects of the global externality either are evenly distributed, or have stronger influences on either the rich or the poor. We find that the global externality promotes the evolution of cooperation only if it either disproportionately benefits the poor or disproportionately reduces the payoffs of the rich. If the global externality primarily harms the poor, it undermines the evolution of prosocial behaviour. Understanding this effect is important given concerns that poorer households are more vulnerable to climate change impacts. This article is part of the theme issue 'Evolution and sustainability: gathering the strands for an Anthropocene synthesis'.

Social identity bias and communication network clustering interact to shape patterns of opinion dynamics

Social influence aligns people's opinions, but social identities and related in-group biases interfere with this alignment. For instance, the recent rise of young climate activists (e.g. 'Fridays for Future' or 'Last Generation') has highlighted the importance of generational identities in the climate change debate. It is unclear how social identities affect the emergence of opinion patterns, such as consensus or disagreement, in a society. Here, we present an agent-based model to explore this question. Agents communicate in a network and form opinions through social influence. The agents have fixed social identities which involve homophily in their interaction preferences and in-group bias in their perception of others. We find that the in-group bias has opposing effects depending on the network topology. The bias impedes consensus in highly random networks by promoting the formation of echo chambers within social identity groups. By contrast, the bias facilitates consensus in highly clustered networks by aligning dispersed in-group agents across the network and, thereby, preventing the formation of isolated echo chambers. Our model uncovers the mechanisms underpinning these opposing effects of the in-group bias and highlights the importance of the communication network topology for shaping opinion dynamics.

Innovation-facilitating networks create inequality

Theories of innovation often balance contrasting views that either smart people create smart things or smartly constructed institutions create smart things. While population models have shown factors including population size, connectivity and agent behaviour as crucial for innovation, few have taken the individual-central approach seriously by examining the role individuals play within their groups. To explore how network structures influence not only population-level innovation but also performance among individuals, we studied an agent-based model of the Potions Task, a paradigm developed to test how structure affects a group's ability to solve a difficult exploration task. We explore how size, connectivity and rates of information sharing in a network influence innovation and how these have an impact on the emergence of inequality in terms of agent contributions. We find, in line with prior work, that population size has a positive effect on innovation, but also find that large and small populations perform similarly per capita; that many small groups outperform fewer large groups; that random changes to structure have few effects on innovation in the task; and that the highest performing agents tend to occupy more central positions in the network. Moreover, we show that every network factor which improves innovation leads to a proportional increase in inequality of performance in the network, creating 'genius effects' among otherwise 'dumb' agents in both idealized and real-world networks.

Smaldino and Richard McElreath

[This corrects the article DOI: 10.1098/rsos.160384.][This corrects the article DOI: 10.1098/rsos.160384.].

Shifting the Level of Selection in Science

Criteria for recognizing and rewarding scientists primarily focus on individual contributions. This creates a conflict between what is best for scientists' careers and what is best for science. In this article, we show how the theory of multilevel selection provides conceptual tools for modifying incentives to better align individual and collective interests. A core principle is the need to account for indirect effects by shifting the level at which selection operates from individuals to the groups in which individuals are embedded. This principle is used in several fields to improve collective outcomes, including animal husbandry, team sports, and professional organizations. Shifting the level of selection has the potential to ameliorate several problems in contemporary science, including accounting for scientists' diverse contributions to knowledge generation, reducing individual-level competition, and promoting specialization and team science. We discuss the difficulties associated with shifting the level of selection and outline directions for future development in this domain.

Hashtags as signals of political identity: #BlackLivesMatter and #AllLivesMatter

We investigate perceptions of tweets marked with the #BlackLivesMatter and #AllLivesMatter hashtags, as well as how the presence or absence of those hashtags changed the meaning and subsequent interpretation of tweets in U.S. participants. We found a strong effect of partisanship on perceptions of the tweets, such that participants on the political left were more likely to view #AllLivesMatter tweets as racist and offensive, while participants on the political right were more likely to view #BlackLivesMatter tweets as racist and offensive. Moreover, we found that political identity explained evaluation results far better than other measured demographics. Additionally, to assess the influence of hashtags themselves, we removed them from tweets in which they originally appeared and added them to selected neutral tweets. Our results have implications for our understanding of how social identity, and particularly political identity, shapes how individuals perceive and engage with the world.

On the Problems Solved by Cognitive Processes

Cognitive scientists have focused too narrowly on the acquisition of data and on the methods to extract patterns from those data. We argue that a successful science of the mind requires widening our focus to include the problems being solved by cognitive processes. Frameworks that characterize cognitive processes in terms of instrumental problem-solving, such as those within the evolutionary social sciences, become necessary if we wish to discover more accurate descriptions of those processes.

The form of uncertainty affects selection for social learning

Social learning is a critical adaptation for dealing with different forms of variability. Uncertainty is a severe form of variability where the space of possible decisions or probabilities of associated outcomes are unknown. We identified four theoretically important sources of uncertainty: temporal environmental variability; payoff ambiguity; selection-set size; and effective lifespan. When these combine, it is nearly impossible to fully learn about the environment. We develop an evolutionary agent-based model to test how each form of uncertainty affects the evolution of social learning. Agents perform one of several behaviours, modelled as a multi-armed bandit, to acquire payoffs. All agents learn about behavioural payoffs individually through an adaptive behaviour-choice model that uses a softmax decision rule. Use of vertical and oblique payoff-biased social learning evolved to serve as a scaffold for adaptive individual learning - they are not opposite strategies. Different types of uncertainty had varying effects. Temporal environmental variability suppressed social learning, whereas larger selection-set size promoted social learning, even when the environment changed frequently. Payoff ambiguity and lifespan interacted with other uncertainty parameters. This study begins to explain how social learning can predominate despite highly variable real-world environments when effective individual learning helps individuals recover from learning outdated social information.

Beyond collective intelligence: Collective adaptation

We develop a conceptual framework for studying collective adaptation in complex socio-cognitive systems, driven by dynamic interactions of social integration strategies, social environments and problem structures. Going beyond searching for 'intelligent' collectives, we integrate research from different disciplines and outline modelling approaches that can be used to begin answering questions such as why collectives sometimes fail to reach seemingly obvious solutions, how they change their strategies and network structures in response to different problems and how we can anticipate and perhaps change future harmful societal trajectories. We discuss the importance of considering path dependence, lack of optimization and collective myopia to understand the sometimes counterintuitive outcomes of collective adaptation. We call for a transdisciplinary, quantitative and societally useful social science that can help us to understand our rapidly changing and ever more complex societies, avoid collective disasters and reach the full potential of our ability to organize in adaptive collectives.

Replication of the natural selection of bad science

This study reports an independent replication of the findings presented by Smaldino and McElreath (Smaldino, McElreath 2016 R. Soc. Open Sci. 3, 160384 (doi:10.1098/rsos.160384)). The replication was successful with one exception. We find that selection acting on scientist's propensity for replication frequency caused a brief period of exuberant replication not observed in the original paper due to a coding error. This difference does not, however, change the authors' original conclusions. We call for more replication studies for simulations as unique contributions to scientific quality assurance.

The Emergence of Cultural Attractors: How Dynamic Populations of Learners Achieve Collective Cognitive Alignment

When a population exhibits collective cognitive alignment, such that group members tend to perceive, remember, and reproduce information in similar ways, the features of socially transmitted variants (i.e., artifacts, behaviors) may converge over time towards culture-specific equilibria points, often called cultural attractors. Because cognition may be plastic, shaped through experience with the cultural products of others, collective cognitive alignment and stable cultural attractors cannot always be taken for granted, but little is known about how these patterns first emerge and stabilize in initially uncoordinated populations. We propose that stable cultural attractors can emerge from general principles of human categorization and communication. We present a model of cultural attractor dynamics, which extends a model of unsupervised category learning in individuals to a multiagent setting wherein learners provide the training input to each other. Agents in our populations spontaneously align their cognitive category structures, producing emergent cultural attractor points. We highlight three interesting behaviors exhibited by our model: (1) noise enhances the stability of cultural category structures; (2) short 'critical' periods of learning early in life enhance stability; and (3) larger populations produce more stable but less complex attractor landscapes, and cliquish network structure can mitigate the latter effect. These results may shed light on how collective cognitive alignment is achieved in the absence of shared, innate cognitive attractors, which we suggest is important to the capacity for cumulative cultural evolution.

Organizational Development as Generative Entrenchment

A critical task for organizations is how to best structure themselves to efficiently allocate information and resources to individuals tasked with solving sub-components of the organization’s central problems. Despite this criticality, the processes by which organizational structures form remain largely opaque within organizational theory, with most approaches focused on how structure is influenced by individual managerial heuristics, normative cultural perceptions, and trial-and-error. Here, we propose that a broad understanding of organizational formation can be aided by appealing to generative entrenchment, a theory from developmental biology that helps explain why phylogenetically diverse animals appear similar as embryos. Drawing inferences from generative entrenchment and applying it to organizational differentiation, we argue that the reason many organizations appear structurally similar is due to core informational restraints on individual actors beginning at the top and descending to the bottom of these informational hierarchies, which reinforces these structures via feedback between separate levels. We further argue that such processes can lead to the emergence of a variety of group-level traits, an important but undertheorized class of phenomena in cultural evolution.

Models of Identity Signaling

Identity signals inform receivers of a signaler’s membership in a subset of individuals, and in doing so shape cooperation, conflict, and social learning. Understanding the use and consequences of identity signaling is therefore critical for a complete science of collective human behavior. As is true for all complex social systems, this understanding is aided by the use of formal mathematical and computational models. Here I review some formal models of identity signaling. I divide these models into two categories. First, I discuss models used to study how identity functions as a signal, with a focus on public-health-related behavior and disease transmission. Second, I discuss models used to understand how identity signals operate strategically in different social environments, with a focus on covert, or encrypted, communication.

Individual exploration and selective social learning: balancing exploration-exploitation trade-offs in collective foraging

Search requires balancing exploring for more options and exploiting the ones previously found. Individuals foraging in a group face another trade-off: whether to engage in social learning to exploit the solutions found by others or to solitarily search for unexplored solutions. Social learning can better exploit learned information and decrease the costs of finding new resources, but excessive social learning can lead to over-exploitation and too little exploration for new solutions. We study how these two trade-offs interact to influence search efficiency in a model of collective foraging under conditions of varying resource abundance, resource density and group size. We modelled individual search strategies as Lévy walks, where a power-law exponent (μ) controlled the trade-off between exploitative and explorative movements in individual search. We modulated the trade-off between individual search and social learning using a selectivity parameter that determined how agents responded to social cues in terms of distance and likely opportunity costs. Our results show that social learning is favoured in rich and clustered environments, but also that the benefits of exploiting social information are maximized by engaging in high levels of individual exploration. We show that selective use of social information can modulate the disadvantages of excessive social learning, especially in larger groups and when individual exploration is limited. Finally, we found that the optimal combination of individual exploration and social learning gave rise to trajectories with μ ≈ 2 and provide support for the general optimality of such patterns in search. Our work sheds light on the interplay between individual search and social learning, and has broader implications for collective search and problem-solving.

Covert signaling is an adaptive communication strategy in diverse populations

Identity signals are those common components of communication transmissions that inform receivers of the signaler's membership in a categorizable subset of individuals. Such signals may be overt, broadcast to all possible receivers, or covert, encrypted so that only similar receivers are likely to perceive their identity-relevant meaning. Here we present an instrumental theory of covert signaling, based on the function of identity signals in social assortment. We argue that covert signaling is favored when signalers are generous toward strangers, when costs of being discovered as dissimilar are high, and when the ability to assort only with preferred partners is restricted. We further argue that covert signaling should be more common among members of "invisible" minorities, who are less likely to encounter similar individuals by chance. We formalize this theory with an evolutionary model to more rigorously explore the consequences of our assumptions. Our results have implications for our understanding of numerous aspects of social life, including communication, cooperation, social identity, humor, pragmatics, politics, hate speech, and the maintenance of diversity. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Analogy as a Catalyst for Cumulative Cultural Evolution

Analogies, broadly defined, map novel concepts onto familiar concepts, making them essential for perception, reasoning, and communication. We argue that analogy-building served a critical role in the evolution of cumulative culture by allowing humans to learn and transmit complex behavioural sequences that would otherwise be too cognitively demanding or opaque to acquire. The emergence of a protolanguage consisting of simple labels would have provided early humans with the cognitive tools to build explicit analogies and to communicate them to others. This focus on analogy-building can shed new light on the coevolution of cognition and culture and addresses recent calls for better integration of the field of cultural evolution with cognitive science.

Coupled dynamics of behaviour and disease contagion among antagonistic groups

Disease transmission and behaviour change are both fundamentally social phenomena. Behaviour change can have profound consequences for disease transmission, and epidemic conditions can favour the more rapid adoption of behavioural innovations. We analyse a simple model of coupled behaviour change and infection in a structured population characterised by homophily and outgroup aversion. Outgroup aversion slows the rate of adoption and can lead to lower rates of adoption in the later-adopting group or even behavioural divergence between groups when outgroup aversion exceeds positive ingroup influence. When disease dynamics are coupled to the behaviour-adoption model, a wide variety of outcomes are possible. Homophily can either increase or decrease the final size of the epidemic depending on its relative strength in the two groups and on R0 for the infection. For example, if the first group is homophilous and the second is not, the second group will have a larger epidemic. Homophily and outgroup aversion can also produce dynamics suggestive of a 'second wave' in the first group that follows the peak of the epidemic in the second group. Our simple model reveals dynamics that are suggestive of the processes currently observed under pandemic conditions in culturally and/or politically polarised populations such as the USA.

How to Translate a Verbal Theory Into a Formal Model

Abstract. Turning verbal theories into formal models is an essential business of a mature science. Here I elaborate on taxonomies of models, provide ten lessons for translating a verbal theory into a formal model, and discuss the specific challenges involved in collaborations between modelers and non-modelers. It’s a start.

Niche diversity can explain cross-cultural differences in personality structure

The covariance structure of personality traits derived from statistical models (for example, Big Five) is often assumed to be a human universal. Cross-cultural studies have challenged this view, finding that less-complex societies exhibit stronger covariation among behavioural characteristics, resulting in fewer derived personality factors. To explain these results, we propose the niche diversity hypothesis, in which a greater diversity of social and ecological niches elicits a broader range of multivariate behavioural profiles and, hence, lower trait covariance in a population. We formalize this as a computational model, which reproduces empirical results from recent cross-cultural studies and also yields an additional prediction for which we find empirical support. This work provides a general explanation for population differences in personality structure in both humans and other animals and suggests a substantial reimagining of personality research: instead of reifying statistical descriptions of manifest personality structures, research should focus more on modelling their underlying causes.

A Modeling Approach that Integrates Individual Behavior, Social Networks, and Cross-Cultural Variation

How do psychological traits shape social networks? How does this relationship influence the spread of behavior? In a recent paper, Muthukrishna and Schaller (Pers. Soc. Psychol. Rev., 2019) use a modeling approach to explore these questions. In doing so, they illustrate the value of using a multilevel approach to study human behavior.

Correction to 'Open science and modified funding lotteries can impede the natural selection of bad science'

[This corrects the article DOI: 10.1098/rsos.190194.].

Open science and modified funding lotteries can impede the natural selection of bad science

Assessing scientists using exploitable metrics can lead to the degradation of research methods even without any strategic behaviour on the part of individuals, via 'the natural selection of bad science.' Institutional incentives to maximize metrics like publication quantity and impact drive this dynamic. Removing these incentives is necessary, but institutional change is slow. However, recent developments suggest possible solutions with more rapid onsets. These include what we call open science improvements, which can reduce publication bias and improve the efficacy of peer review. In addition, there have been increasing calls for funders to move away from prestige- or innovation-based approaches in favour of lotteries. We investigated whether such changes are likely to improve the reproducibility of science even in the presence of persistent incentives for publication quantity through computational modelling. We found that modified lotteries, which allocate funding randomly among proposals that pass a threshold for methodological rigour, effectively reduce the rate of false discoveries, particularly when paired with open science improvements that increase the publication of negative results and improve the quality of peer review. In the absence of funding that targets rigour, open science improvements can still reduce false discoveries in the published literature but are less likely to improve the overall culture of research practices that underlie those publications.

The evolution of two types of play

Play is an important and understudied class of phenomena that likely serves a critical role in the ontogeny and maintenance of fitness-enhancing behaviors. Many species exhibit little or no play. Among those animals that do play, some exhibit only very simple forms, while others engage in complex play both solitarily and socially. Likewise, some animals play only as juveniles, while others continue to play as adults. We propose a general framework to explain interspecies variation in the evolution and emergence of simple vs. complex forms of play, supported by both a review of the empirical evidence and a novel mathematical model. The emergence of play requires that initial investment returns benefits that sufficiently compensate the opportunity costs associated with simple play. The subsequent evolution of complex play depends upon the interplay of several life-history factors related to the benefits, costs, and time course of play investment. We conclude with implications for understanding the evolution of play across the animal kingdom.

Sigmoidal Acquisition Curves Are Good Indicators of Conformist Transmission

The potential for behaviours to spread via cultural transmission has profound implications for our understanding of social dynamics and evolution. Several studies have provided empirical evidence that local traditions can be maintained in animal populations via conformist learning (i.e. copying the majority). A conformist bias can be characterized by a sigmoidal relationship between a behavior's prevalence in the population and an individual's propensity to adopt that behavior. For this reason, the presence of conformist learning in a population is often inferred from a sigmoidal acquisition curve in which the overall rate of adoption for the behavior is taken as the dependent variable. However, the validity of sigmoidal acquisition curves as evidence for conformist learning has recently been challenged by models suggesting that such curves can arise via alternative learning rules that do not involve conformity. We review these models, and find that the proposed alternative learning mechanisms either rely on faulty or unrealistic assumptions, or apply only in very specific cases. We therefore recommend that sigmoidal acquisition curves continue to be taken as evidence for conformist learning. Our paper also highlights the importance of understanding the generative processes of a model, rather than only focusing solely on the patterns produced. By studying these processes, our analysis suggests that current practices by empiricists have provided robust evidence for conformist transmission in both humans and non-human animals.Arising from: Acerbi, A. et al. Sci. Rep. 6, 36068 (2016); https://doi.org/10.1038/srep36068 .

Social Learning and Cultural Evolution in Artificial Life

We describe the questions and discussions raised at the First Workshop on Social Learning and Cultural Evolution held at theArtificial Life Conference 2016 in Cancún, Mexico in July 2016. The purpose of the workshop was to assemble artificial life researchers interested in social learning and cultural evolution into one group so that we could focus on recent work and interesting open questions. Our discussion related to both the mechanisms of social learning and cultural evolution and the consequences and influence of social learning and cultural evolution on living systems. We present the contributions of our workshop presenters and conclude with a discussion of the more important open questions in this area.

Social identity and cooperation in cultural evolution

I discuss the function of social identity signaling in facilitating cooperative group formation, and how the nature of that function changes with the structure of social organization. I propose that signals of social identity facilitate assortment for successful coordination in large-scale societies, and that the multidimensional, context-dependent nature of social identity is crucial for successful coordination when individuals have to cooperate in different contexts. Furthermore, the structure of social identity is tied to the structure of society, so that as societies grow larger and more interconnected, the landscape of social identities grows more heterogeneous. This discussion bears directly on the need to articulate the dynamics of emergent, ephemeral groups as a major factor in human cultural evolution.

The natural selection of bad science

Poor research design and data analysis encourage false-positive findings. Such poor methods persist despite perennial calls for improvement, suggesting that they result from something more than just misunderstanding. The persistence of poor methods results partly from incentives that favour them, leading to the natural selection of bad science. This dynamic requires no conscious strategizing-no deliberate cheating nor loafing-by scientists, only that publication is a principal factor for career advancement. Some normative methods of analysis have almost certainly been selected to further publication instead of discovery. In order to improve the culture of science, a shift must be made away from correcting misunderstandings and towards rewarding understanding. We support this argument with empirical evidence and computational modelling. We first present a 60-year meta-analysis of statistical power in the behavioural sciences and show that power has not improved despite repeated demonstrations of the necessity of increasing power. To demonstrate the logical consequences of structural incentives, we then present a dynamic model of scientific communities in which competing laboratories investigate novel or previously published hypotheses using culturally transmitted research methods. As in the real world, successful labs produce more 'progeny,' such that their methods are more often copied and their students are more likely to start labs of their own. Selection for high output leads to poorer methods and increasingly high false discovery rates. We additionally show that replication slows but does not stop the process of methodological deterioration. Improving the quality of research requires change at the institutional level.

Evolution of fairness in the dictator game by multilevel selection

The most perplexing experimental results on fairness come from the dictator game where one of two players, the dictator, decides how to divide a resource with an anonymous player. The dictator, acting self-interestedly, should offer nothing to the anonymous second player, but in experimental studies, dictators offer much more than nothing. We developed a multilevel selection model to explain why people offer more than nothing in the dictator game. We show that fairness can evolve when population structure emerges from the aggregation and limited dispersal of offspring. We begin with an analytical model that shows how fair behavior can benefit groups by minimizing within-group variance in resources and thereby increasing group fitness. To investigate the generality of this result, we developed an agent-based model with agents that have no information about other agents. We allowed agents to aggregate into groups and evolve different levels of fairness by playing the dictator game for resources to reproduce. This allowed multilevel selection to emerge from the spatiotemporal properties of individual agents. We found that the population structure that emerged under low population densities was most conducive to the evolution of fairness, which is consistent with group selection as a major evolutionary force. We also found that fairness only evolves if resources are not too scarce relative to the lifespan of agents. We conclude that the evolution of fairness could evolve under multilevel selection. Thus, our model provides a novel explanation for the results of dictator game experiments, in which participants often fairly split a resource rather than keeping it all for themselves.

Theory development with agent-based models

Many social phenomena do not result solely from intentional actions by isolated individuals, but rather emerge as the result of repeated interactions among multiple individuals over time. However, such phenomena are often poorly captured by traditional empirical techniques. Moreover, complex adaptive systems are insufficiently described by verbal models. In this paper, we discuss how organizational psychologists and group dynamics researchers may benefit from the adoption of formal modeling, particularly agent-based modeling, for developing and testing richer theories. Agent-based modeling is well suited to capture multilevel dynamic processes and offers superior precision to verbal models. As an example, we present a model of social identity dynamics used to test the predictions of Brewer’s (1991) optimal distinctiveness theory, and discuss how the model extends the theory and produces novel research questions. We close with a general discussion on theory development using agent-based models.

Social conformity despite individual preferences for distinctiveness

We demonstrate that individual behaviours directed at the attainment of distinctiveness can in fact produce complete social conformity. We thus offer an unexpected generative mechanism for this central social phenomenon. Specifically, we establish that agents who have fixed needs to be distinct and adapt their positions to achieve distinctiveness goals, can nevertheless self-organize to a limiting state of absolute conformity. This seemingly paradoxical result is deduced formally from a small number of natural assumptions and is then explored at length computationally. Interesting departures from this conformity equilibrium are also possible, including divergence in positions. The effect of extremist minorities on these dynamics is discussed. A simple extension is then introduced, which allows the model to generate and maintain social diversity, including multimodal distinctiveness distributions. The paper contributes formal definitions, analytical deductions and counterintuitive findings to the literature on individual distinctiveness and social conformity.

Institutions and cooperation in an ecology of games

Social dilemmas have long been studied formally as cooperation games that pit individual gains against those of the group. In the real world, individuals face an ecology of games where they play many such games simultaneously, often with overlapping co-players. Here, we study an agent-based model of an ecology of public goods games and compare the effectiveness of two institutional mechanisms for promoting cooperation: a simple institution of limited group size (capacity constraints) and a reputational institution based on observed behavior. Reputation is shown to allow much higher relative payoffs for cooperators than do capacity constraints, but only if (1) the rate of reputational information flow is fast enough relative to the rate of social mobility, and (2) cooperators are relatively common in the population. When these conditions are not met, capacity constraints are more effective at protecting the interests of cooperators. Because of the simplicity of the limited-group-size rule, capacity constraints can also generate social organization, which promotes cooperation much more quickly than can reputation. Our results are discussed in terms of both normative prescriptions and evolutionary theory regarding institutions that regulate cooperation. More broadly, the ecology-of-games approach developed here provides an adaptable modeling framework for studying a wide variety of problems in the social sciences.

Simulating the evolution of the human family: cooperative breeding increases in harsh environments

Verbal and mathematical models that consider the costs and benefits of behavioral strategies have been useful in explaining animal behavior and are often used as the basis of evolutionary explanations of human behavior. In most cases, however, these models do not account for the effects that group structure and cultural traditions within a human population have on the costs and benefits of its members' decisions. Nor do they consider the likelihood that cultural as well as genetic traits will be subject to natural selection. In this paper, we present an agent-based model that incorporates some key aspects of human social structure and life history. We investigate the evolution of a population under conditions of different environmental harshness and in which selection can occur at the level of the group as well as the level of the individual. We focus on the evolution of a socially learned characteristic related to individuals' willingness to contribute to raising the offspring of others within their family group. We find that environmental harshness increases the frequency of individuals who make such contributions. However, under the conditions we stipulate, we also find that environmental variability can allow groups to survive with lower frequencies of helpers. The model presented here is inevitably a simplified representation of a human population, but it provides a basis for future modeling work toward evolutionary explanations of human behavior that consider the influence of both genetic and cultural transmission of behavior.

Cooperation in Harsh Environments and the Emergence of Spatial Patterns

This paper concerns the confluence of two important areas of research in mathematical biology: spatial pattern formation and cooperative dilemmas. Mechanisms through which social organisms form spatial patterns are not fully understood. Prior work connecting cooperation and pattern formation has often included unrealistic assumptions that shed doubt on the applicability of those models toward understanding real biological patterns. I investigated a more biologically realistic model of cooperation among social actors. The environment is harsh, so that interactions with cooperators are strictly needed to survive. Harshness is implemented via a constant energy deduction. I show that this model can generate spatial patterns similar to those seen in many naturally-occuring systems. Moreover, for each payoff matrix there is an associated critical value of the energy deduction that separates two distinct dynamical processes. In low-harshness environments, the growth of cooperator clusters is impeded by defectors, but these clusters gradually expand to form dense dendritic patterns. In very harsh environments, cooperators expand rapidly but defectors can subsequently make inroads to form reticulated patterns. The resulting web-like patterns are reminiscent of transportation networks observed in slime mold colonies and other biological systems.

Movement patterns, social dynamics, and the evolution of cooperation

The structure of social interactions influences many aspects of social life, including the spread of information and behavior, and the evolution of social phenotypes. After dispersal, organisms move around throughout their lives, and the patterns of their movement influence their social encounters over the course of their lifespan. Though both space and mobility are known to influence social evolution, there is little analysis of the influence of specific movement patterns on evolutionary dynamics. We explored the effects of random movement strategies on the evolution of cooperation using an agent-based prisoner's dilemma model with mobile agents. This is the first systematic analysis of a model in which cooperators and defectors can use different random movement strategies, which we chose to fall on a spectrum between highly exploratory and highly restricted in their search tendencies. Because limited dispersal and restrictions to local neighborhood size are known to influence the ability of cooperators to effectively assort, we also assessed the robustness of our findings with respect to dispersal and local capacity constraints. We show that differences in patterns of movement can dramatically influence the likelihood of cooperator success, and that the effects of different movement patterns are sensitive to environmental assumptions about offspring dispersal and local space constraints. Since local interactions implicitly generate dynamic social interaction networks, we also measured the average number of unique and total interactions over a lifetime and considered how these emergent network dynamics helped explain the results. This work extends what is known about mobility and the evolution of cooperation, and also has general implications for social models with randomly moving agents.

The origins of options

Most research on decision making has focused on how human or animal decision makers choose between two or more options, posed in advance by the researchers. The mechanisms by which options are generated for most decisions, however, are not well understood. Models of sequential search have examined the trade-off between continued exploration and choosing one’s current best option, but still cannot explain the processes by which new options are generated. We argue that understanding the origins of options is a crucial but untapped area for decision making research. We explore a number of factors which influence the generation of options, which fall broadly into two categories: psycho-biological and socio-cultural. The former category includes factors such as perceptual biases and associative memory networks. The latter category relies on the incredible human capacity for culture and social learning, which doubtless shape not only our choices but the options available for choice. Our intention is to start a discussion that brings us closer toward understanding the origins of options.

An institutional mechanism for assortment in an ecology of games

Recent research has revived Long's "ecology of games" model to analyze how social actors cooperate in the context of multiple political and social games. However, there is still a paucity of theoretical work that considers the mechanisms by which large-scale cooperation can be promoted in a dynamic institutional landscape, in which actors can join new games and leave old ones. This paper develops an agent-based model of an ecology of games where agents participate in multiple public goods games. In addition to contribution decisions, the agents can leave and join different games, and these processes are de-coupled. We show that the payoff for cooperation is greater than for defection when limits to the number of actors per game ("capacity constraints") structure the population in ways that allow cooperators to cluster, independent of any complex individual-level mechanisms such as reputation or punishment. Our model suggests that capacity constraints are one effective mechanism for producing positive assortment and increasing cooperation in an ecology of games. The results suggest an important trade-off between the inclusiveness of policy processes and cooperation: Fully inclusive policy processes reduce the chances of cooperation.