Punctuated equilibrium at 50: Anything there for evolutionary anthropology? Yes; definitely.

The theory of punctuated equilibrium (PE) was developed a little over 50 years ago to explain long-term, large-scale appearance and disappearance of species in the fossil record. A theory designed specifically for that purpose cannot be expected, out of the box, to be directly applicable to biocultural evolution, but in revised form, PE offers a promising approach to incorporating not only a wealth of recent empirical research on genetic, linguistic, and technological evolution but also large databases that document human biological and cultural diversity across time and space. Here we isolate the fundamental components of PE and propose which pieces, when reassembled or renamed, can be highly useful in evolutionary anthropology, especially as humanity faces abrupt ecological challenges on an increasingly larger scale.

A Simple Model of the Rise and Fall of Civilizations.

The literature on the fall of civilizations spans from the archaeology of early state societies to the history of the 20th century. Explanations for the fall of civilizations abound, from general extrinsic causes (drought, warfare) to general intrinsic causes (intergroup competition, socioeconomic inequality, collapse of trade networks) and combinations of these, to case-specific explanations for the specific demise of early state societies. Here, we focus on ancient civilizations, which archaeologists typically define by a set of characteristics including hierarchical organization, standardization of specialized knowledge, occupation and technologies, and hierarchical exchange networks and settlements. We take a general approach, with a model suggesting that state societies arise and dissolve through the same processes of innovation. Drawing on the field of cumulative cultural evolution, we demonstrate a model that replicates the essence of a civilization's rise and fall, in which agents at various scales-individuals, households, specialist communities, polities-copy each other in an unbiased manner but with varying degrees of institutional memory, invention rate, and propensity to copy locally versus globally. The results, which produce an increasingly extreme hierarchy of success among agents, suggest that civilizations become increasingly vulnerable to even small increases in propensity to copy locally.

Agency and Identity in the Collective Self.

Contemporary research on human sociality is heavily influenced by the social identity approach, positioning social categorization as the primary mechanism governing social life. Building on the distinction between agency and identity in the individual self ("I" vs. "Me"), we emphasize the analogous importance of distinguishing collective agency from collective identity ("We" vs. "Us"). While collective identity is anchored in the unique characteristics of group members, collective agency involves the adoption of a shared subjectivity that is directed toward some object of our attention, desire, emotion, belief, or action. These distinct components of the collective self are differentiated in terms of their mental representations, neurocognitive underpinnings, conditions of emergence, mechanisms of social convergence, and functional consequences. Overall, we show that collective agency provides a useful complement to the social categorization approach, with unique implications for multiple domains of human social life, including collective action, responsibility, dignity, violence, dominance, ritual, and morality.

Observations and conversations: how communities learn about infection risk can impact the success of non-pharmaceutical interventions against epidemics.

BACKGROUND: Individual behavioural decisions are responses to a person's perceived social norms that could be shaped by both their physical and social environment. In the context of the COVID-19 pandemic, these environments correspond to epidemiological risk from contacts and the social construction of risk by communication within networks of friends. Understanding the circumstances under which the influence of these different social networks can promote the acceptance of non-pharmaceutical interventions and consequently the adoption of protective behaviours is critical for guiding useful, practical public health messaging. METHODS: We explore how information from both physical contact and social communication layers of a multiplex network can contribute to flattening the epidemic curve in a community. Connections in the physical contact layer represent opportunities for transmission, while connections in the communication layer represent social interactions through which individuals may gain information, e.g. messaging friends. RESULTS: We show that maintaining focus on awareness of risk among each individual's physical contacts promotes the greatest reduction in disease spread, but only when an individual is aware of the symptoms of a non-trivial proportion of their physical contacts (~ ≥ 20%). Information from the social communication layer without was less useful when these connections matched less well with physical contacts and contributed little in combination with accurate information from physical contacts. CONCLUSIONS: We conclude that maintaining social focus on local outbreak status will allow individuals to structure their perceived social norms appropriately and respond more rapidly when risk increases. Finding ways to relay accurate local information from trusted community leaders could improve mitigation even where more intrusive/costly strategies, such as contact-tracing, are not possible.

Genes, culture, and the human niche: An overview.

The sharp distinction between biological traits and culturally based traits, which had long been standard in evolutionary approaches to behavior, was blurred in the early 1980s by mathematical models that allowed a co-dependent evolution of genetic transmission and cultural information. Niche-construction theory has since added another contrast to standard evolutionary theory, in that it views niche construction as a cause of evolutionary change rather than simply a product of selection. While offering a new understanding of the coevolution of genes, culture, and human behavior, niche-construction models also invoke multivariate causality, which require multiple time series to resolve. The empirical challenge lies in obtaining time-series data on causal pathways involved in the coevolution of genes, culture, and behavior. This is a significant issue in archeology, where time series are often sparse and causal behaviors are represented only by proxies in the material record.

A long view of cumulative technological culture.

We agree that the emergence of cumulative technological culture was tied to nonsocial cognitive skills, namely, technical-reasoning skills, which allowed humans to constantly acquire and improve information. Our concern is with a reading of the history of cumulative technological culture that is based largely on modern experiments in simulated settings and less on phenomena crucial to the long-term dynamics of cultural evolution.

Mapping multiple drivers of human obesity.

The insurance hypothesis is a reasonable explanation for the current obesity epidemic. One alternative explanation is that the marketing of high-sugar foods, especially sugar-sweetened beverages, drives the rise in obesity. Another prominent hypothesis is that obesity spreads through social influence. We offer a framework for estimating the extent to which these different models explain the rise in obesity.

Fitness landscapes among many options under social influence.

Cultural learning represents a novel problem in that an optimal decision depends not only on intrinsic utility of the decision/behavior but also on transparency of costs and benefits, the degree of social versus individual learning, and the relative popularity of each possible choice in a population. In terms of a fitness-landscape function, this recursive relationship means that multiple equilibria can exist. Here we use discrete-choice theory to construct a fitness-landscape function for a bi-axial decision-making map that plots the magnitude of social influence in the learning process against the costs and payoffs of decisions. Specifically, we use econometric and statistical methods to estimate not only the fitness function but also movements along the map axes. To search for these equilibria, we employ a hill-climbing algorithm that leads to the expected values of optimal decisions, which we define as peaks on the fitness landscape. We illustrate how estimation of a measure of transparency, a measure of social influence, and the associated fitness landscape can be accomplished using panel data sets.

Collective behaviour, uncertainty and environmental change.

A central aspect of cultural evolutionary theory concerns how human groups respond to environmental change. Although we are painting with a broad brush, it is fair to say that prior to the twenty-first century, adaptation often happened gradually over multiple human generations, through a combination of individual and social learning, cumulative cultural evolution and demographic shifts. The result was a generally resilient and sustainable population. In the twenty-first century, however, considerable change happens within small portions of a human generation, on a vastly larger range of geographical and population scales and involving a greater degree of horizontal learning. As a way of gauging the complexity of societal response to environmental change in a globalized future, we discuss several theoretical tools for understanding how human groups adapt to uncertainty. We use our analysis to estimate the limits of predictability of future societal change, in the belief that knowing when to hedge bets is better than relying on a false sense of predictability.

Using isotopic evidence to assess the impact of migration and the two-layer hypothesis in prehistoric Northeast Thailand.

OBJECTIVES: The nature of the agricultural transition in Southeast Asia has been a topic of some debate for archaeologists over the past decades. A prominent model, known as the two-layer hypothesis, states that indigenous hunter-gatherers were subsumed by the expansion of exotic Neolithic farmers into the area around 2000 BC. These farmers had ultimate origins in East Asia and brought rice and millet agriculture. Ban Non Wat is one of the few archaeological sites in Southeast Asia where this model can potentially be tested. The site is located in the Mun River valley of Northeast Thailand, and divided into 12 phases that span over 2,000 years, from about 1750 BC to the end of the Iron Age (ca. 500 AD). These phases exhibit successive cultural changes, and current interpretation of the site is of an early hunter-gatherer population, with agriculturalists immigrating into the later phases. METHODS: We analyzed strontium, oxygen, and carbon isotopes in tooth enamel from over 150 individuals, dating from the Neolithic to Iron Age, to assess extrinsic origins and differences in diet between early and later phases. RESULTS: We find evidence of dietary and cultural differences between groups at Ban Non Wat during its early occupation, but little evidence for immigration from distinct environments beyond the Khorat Plateau of Northeast Thailand. CONCLUSIONS: The lack of consistent isotopic differences between early and later Neolithic occupants at Ban Non Wat means that the site does not conclusively support the two-layer hypothesis.

Community differentiation and kinship among Europe's first farmers.

Community differentiation is a fundamental topic of the social sciences, and its prehistoric origins in Europe are typically assumed to lie among the complex, densely populated societies that developed millennia after their Neolithic predecessors. Here we present the earliest, statistically significant evidence for such differentiation among the first farmers of Neolithic Europe. By using strontium isotopic data from more than 300 early Neolithic human skeletons, we find significantly less variance in geographic signatures among males than we find among females, and less variance among burials with ground stone adzes than burials without such adzes. From this, in context with other available evidence, we infer differential land use in early Neolithic central Europe within a patrilocal kinship system.

Mapping collective behavior in the big-data era.

The behavioral sciences have flourished by studying how traditional and/or rational behavior has been governed throughout most of human history by relatively well-informed individual and social learning. In the online age, however, social phenomena can occur with unprecedented scale and unpredictability, and individuals have access to social connections never before possible. Similarly, behavioral scientists now have access to "big data" sets - those from Twitter and Facebook, for example - that did not exist a few years ago. Studies of human dynamics based on these data sets are novel and exciting but, if not placed in context, can foster the misconception that mass-scale online behavior is all we need to understand, for example, how humans make decisions. To overcome that misconception, we draw on the field of discrete-choice theory to create a multiscale comparative "map" that, like a principal-components representation, captures the essence of decision making along two axes: (1) an east-west dimension that represents the degree to which an agent makes a decision independently versus one that is socially influenced, and (2) a north-south dimension that represents the degree to which there is transparency in the payoffs and risks associated with the decisions agents make. We divide the map into quadrants, each of which features a signature behavioral pattern. When taken together, the map and its signatures provide an easily understood empirical framework for evaluating how modern collective behavior may be changing in the digital age, including whether behavior is becoming more individualistic, as people seek out exactly what they want, or more social, as people become more inextricably linked, even "herdlike," in their decision making. We believe the map will lead to many new testable hypotheses concerning human behavior as well as to similar applications throughout the social sciences.

Authors' response. more on maps, terrains, and behaviors.

In a recent New York Times column (April 15, 2013), David Brooks discussed how the big-data agenda lacks a coherent framework of social theory ­ a deficiency that the Bentley, O'Brien, and Brock (henceforth BOB) model was meant to overcome. Or, stated less pretentiously, the model was meant as a first step in that direction ­ a map that hopefully would serve as a minimal, practical, and accessible framework that behavioral scientists could use to analyze big data. Rather than treating big data as a record of, and also a predictor of, where and when certain behaviors might take place, the BOB model is interested in what big data reveal about how decisions are being made, how collective behavior evolves from daily to decadal time scales, and how this varies across communities.

On the multiscale dynamics of punctuated evolution.

For five decades, paleontologists, paleobiologists, and ecologists have investigated patterns of punctuated equilibria in biology. Here, we step outside those fields and summarize recent advances in the theory of and evidence for punctuated equilibria, gathered from contemporary observations in geology, molecular biology, genetics, anthropology, and sociotechnology. Taken in the aggregate, these observations lead to a more general theory that we refer to as punctuated evolution. The quality of recent datasets is beginning to illustrate the mechanics of punctuated evolution in a way that can be modeled across a vast range of phenomena, from mass extinctions hundreds of millions of years ago to the possible future ahead in the Anthropocene. We expect the study of punctuated evolution to be applicable beyond biological scenarios.

How social learning shapes the efficacy of preventative health behaviors in an outbreak.

The global pandemic of COVID-19 revealed the dynamic heterogeneity in how individuals respond to infection risks, government orders, and community-specific social norms. Here we demonstrate how both individual observation and social learning are likely to shape behavioral, and therefore epidemiological, dynamics over time. Efforts to delay and reduce infections can compromise their own success, especially when disease risk and social learning interact within sub-populations, as when people observe others who are (a) infected and/or (b) socially distancing to protect themselves from infection. Simulating socially-learning agents who observe effects of a contagious virus, our modelling results are consistent with with 2020 data on mask-wearing in the U.S. and also concur with general observations of cohort induced differences in reactions to public health recommendations. We show how shifting reliance on types of learning affect the course of an outbreak, and could therefore factor into policy-based interventions incorporating age-based cohort differences in response behavior.

Inequality between identity groups and social unrest.

Economic, social and political inequality between different identity groups is an important contributor to violent conflicts within societies. To deepen our understanding of the underlying social dynamics, we develop a mathematical model describing cooperation and conflict in a society composed of multiple factions engaged in economic and political interactions. Our model predicts that growing economic and political inequality tends to lead to the collapse of cooperation between factions that were initially seeking to cooperate. Certain mechanisms can delay this process, including the decoupling of political and economic power through rule of law and allegiance to the state or dominant faction. Counterintuitively, anti-conformity (a social norm for independent action) can also stabilize society, by preventing initial defections from cooperation from cascading through society. However, the availability of certain material resources that can be acquired by the state without cooperation with other factions has the opposite effect. We test several of these predictions using a multivariate statistical analysis of data covering 75 countries worldwide. Using social unrest as a proxy for the breakdown of cooperation in society, we find support for many of the predictions from our theory.

Balancing timeliness of reporting with increasing testing probability for epidemic data.

Reporting of epidemiological data requires coordinated action by numerous agencies, across a multitude of logistical steps. Using collated and reported information to inform direct interventions can be challenging due to associated delays. Mitigation can, however, occur indirectly through the public generation of concern, which facilitates adherence to protective behaviors. We utilized a coupled-dynamic multiplex network model with a communication- and disease-layer to examine how variation in reporting delay and testing probability are likely to impact adherence to protective behaviors, such as reducing physical contact. Individual concern mediated adherence and was informed by new- or active-case reporting, at the population- or community-level. Individuals received information from the communication layer: direct connections that were sick or adherent to protective behaviors increased their concern, but absence of illness eroded concern. Models revealed that the relative benefit of timely reporting and a high probability of testing was contingent on how much information was already obtained. With low rates of testing, increasing testing probability was of greater mitigating value. With high rates of testing, maximizing timeliness was of greater value. Population-level reporting provided advanced warning of disease risk from nearby communities; but we explore the relative costs and benefits of delays due to scale against the assumption that people may prioritize community-level information. Our findings emphasize the interaction of testing accuracy and reporting timeliness for the indirect mitigation of disease in a complex social system.

How reported outbreak data can shape individual behavior in a social world.

Agencies reporting on disease outbreaks face many choices about what to report and the scale of its dissemination. Reporting impacts an epidemic by influencing individual decisions directly, and the social network in which they are made. We simulated a dynamic multiplex network model-with coupled infection and communication layers-to examine behavioral impacts from the nature and scale of epidemiological information reporting. We explored how adherence to protective behaviors (social distancing) can be facilitated through epidemiological reporting, social construction of perceived risk, and local monitoring of direct connections, but eroded via social reassurance. We varied reported information (total active cases, daily new cases, hospitalizations, hospital capacity exceeded, or deaths) at one of two scales (population level or community level). Total active and new case reporting at the population level were the most effective approaches, relative to the other reporting approaches. Case reporting, which synergizes with test-trace-and-isolate and vaccination policies, should remain a priority throughout an epidemic.

A cultural evolutionary theory that explains both gradual and punctuated change.

Cumulative cultural evolution (CCE) occurs among humans who may be presented with many similar options from which to choose, as well as many social influences and diverse environments. It is unknown what general principles underlie the wide range of CCE dynamics and whether they can all be explained by the same unified paradigm. Here, we present a scalable evolutionary model of discrete choice with social learning, based on a few behavioural science assumptions. This paradigm connects the degree of transparency in social learning to the human tendency to imitate others. Computer simulations and quantitative analysis show the interaction of three primary factors-information transparency, popularity bias and population size-drives the pace of CCE. The model predicts a stable rate of evolutionary change for modest degrees of popularity bias. As popularity bias grows, the transition from gradual to punctuated change occurs, with maladaptive subpopulations arising on their own. When the popularity bias gets too severe, CCE stops. This provides a consistent framework for explaining the rich and complex adaptive dynamics taking place in the real world, such as modern digital media.

Monitoring event-driven dynamics on Twitter: a case study in Belarus.

Analysts of social media differ in their emphasis on the effects of message content versus social network structure. The balance of these factors may change substantially across time. When a major event occurs, initial independent reactions may give way to more social diffusion of interpretations of the event among different communities, including those committed to disinformation. Here, we explore these dynamics through a case study analysis of the Russian-language Twitter content emerging from Belarus before and after its presidential election of August 9, 2020. From these Russian-language tweets, we extracted a set of topics that characterize the social media data and construct networks to represent the sharing of these topics before and after the election. The case study in Belarus reveals how misinformation can be re-invigorated in discourse through the novelty of a major event. More generally, it suggests how audience networks can shift from influentials dispensing information before an event to a de-centralized sharing of information after it. Supplementary Information: The online version contains supplementary material available at 10.1007/s43545-022-00330-x.