Research

This article engages with Mark Moffett’s proposed definition of society, which emphasizes shared group identification over social interaction, and argues for a different, problem-driven approach to definition in evolutionary social science. Rather than treating definitions as primary, I situate them within the broader research agenda aimed at explaining the Great Holocene Transformation—the dramatic expansion of human societies in scale and complexity over the past 10,000 years. My central analytical focus is on cooperation: the capacity of individuals to coordinate actions toward collective goals despite incentives to free-ride. Accordingly, society is defined here as a collective of individuals engaged in sustained cooperation, with interest group proposed as a more flexible and analytically useful term. Within this framework, polities represent a key subtype of interest groups, characterized by their role as independent political units whose persistence depends on maintaining cooperative cohesion, particularly among elites. Other commonly cited features of societies—such as group identification, territoriality, and intergenerational continuity—are treated as secondary mechanisms that support cooperation rather than defining properties. Ultimately, I argue that flexible, operational definitions are most valuable at intermediate stages of scientific inquiry, where they facilitate the construction and empirical testing of theoretical models.

During the Holocene the scale and complexity of human societies increased dramatically. Generations of scholars have proposed different theories explaining this evolution, which range from functionalist explanations, focusing on the provision of public goods, to conflict theories, emphasizing the role of internal class struggle or external warfare.…

How do large-scale human societies maintain functional integration? Over the last 12,000 years, polities grew over six orders of magnitude in population, far exceeding the scale at which the interpersonal mechanisms that promote cooperation in small-scale societies can operate. Here we test the hypothesis that cooperation and functional integration in large-scale societies is promoted by social (and, specifically, institutional) complexity. We model large-scale societies as territorial social networks subject to energetic, cognitive, and competitive constraints. We demonstrate that agricultural intensification generates increasing population density, raising the per capita rate of social interaction beyond what small-scale mechanisms can sustain and requiring institutional rules to substitute for interpersonal knowledge. The model predicts that territory and social complexity scale with polity population with exponents of 5/6 and 1/6. Tests against data describing hundreds of polities spanning the Holocene support these predictions, providing quantitative evidence that social complexity is a central integrative mechanism enabling large-scale human cooperation.

Cultural macroevolution (CME) is a subfield of cultural evolution that studies large-scale changes in the cultural traits of whole groups. A central question in CME is how and why characteristics of polities (such as chiefdoms, states, and empires) evolve over time. Multilevel selection, and especially its application to cultural evolution, provides a very useful theoretical framework for CME because many polity-level characteristics evolve under selection pressures that act in opposite directions at different levels of hierarchical organization. In this chapter I discuss the conceptual framework that multilevel selection provides for studying CME and summarize the main empirical results from my recent book (Turchin 2025). I conclude that major predictions of cultural multilevel selection theory enjoy substantial empirical support.

Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve more than 90% accuracy on popular benchmarks such as Measuring Massive Multitask Language Understanding1, limiting informed measurement of state-of-the-art LLM capabilities. Here, in response, we introduce Humanity’s Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be an expert-level closed-ended academic benchmark with broad subject coverage. HLE consists of 2,500 questions across dozens of subjects, including mathematics, humanities and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable but cannot be quickly answered by internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a marked gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai.

Is it possible to forecast the dynamics of societal resilience and its obverse, sociopolitical unrest or even breakdown? This is the goal of Structural-Demographic Theory (SDT), which integrates mechanism-based models with data and focuses on the dynamics of structural drivers for instability over the long-term (thus, requiring a historical approach). Several recent studies utilizing the SDT framework have proven adept at predicting (or “retrodicting”) sociopolitical instability in c.20 past societies. It was also used in 2010 to successfully forecast the outbreak of US instability for 10 years in the future (in 2020). This study applies the SDT to explore the dynamics of another contemporary society. Our aim is to empirically test SDT in a most rigorous way, using it to forecast future dynamics of sociopolitical instability in Japan. Our research questions are: (1) how accurately (if at all) does the SDT framework predict future levels of sociopolitical instability; (2) What are the relative contributions of possible drivers of instability, including those proposed by SDT, as well as other theories, in explaining instability levels; and (3) are there key ‘leverage points’ that might help mitigate the negative consequences of instability? This article explains how we develop quantitative indices for SDT drivers of instability: mass immiseration, elite overproduction, and state fiscal distress. We then construct a Political Stress Index to track the evolution of these pressures from the 1990s to the present and implement a multipath forecasting model to project future trends through 2050, using a variety of intervention (or non-intervention) scenarios. Our plan is to revisit these predictions ten years in the future with the goal of assessing their accuracy.

The Great Holocene Transformation: What Complexity Science Tells Us about the Evolution of Complex Societies Peter Turchin Beresta Books (2025) An ambitious analysis of a vast trove of data from 800 societies around the world concludes that there’s safety in numbers. When Spanish conquistador Hernán Cortés arrived in Mexico in 1519, he found monarchs,…

The Great Holocene Transformation What Complexity Science Tells Us about the Evolution of Complex Societies?

Historians and social scientists have long been preoccupied with understanding and documenting periods of crisis. Such emphasis is only growing, and becoming more pressing, as the world continues to face a number of interrelated stressors in the form of irreversible climate change, major ecological shocks and disease outbreaks, eruptions of military violence, economic disruptions and deepening inequalities, political polarization and unrest, the rise of authoritarian and nationalist regimes. Crises in these domains are not new, but have been recurrent features of past societies. Although these periods have typically led to massive loss of life, the failure of critical institutions, and even complete societal collapse, there are instances in the historical record of societies managing to turn the tide of crisis even as violence and social turmoil grow. Here, we focus on four such cases of crises mitigated with structural reforms revealed from our previous historical analyses: early Republican Rome, mid-19th century England and Russia, USA during the the late 20th to early 21st centuries. Utilizing structural demographic theory as a lens to explore these cases, we seek to expose the pressures that built up leading to crisis and the early signs of violent confrontation revealed by these societies, as well as to examine the conditions and key decisions made in the midst of this unrest that allowed these societies to turn the tide and enact significant structural adaptations. Our findings have clear relevance to understanding and navigating similar crises in contemporary societies.

The scientific understanding of the complex dynamics of global history – from the rise and spread of states to their declines and falls, from their peaceful interactions with economic or diplomatic exchanges to violent confrontations – requires, at its core, a consistent and explicit encoding of historical political entities, their locations, extents and durations. Numerous attempts have been made to produce digital geographical compendia of polities with different time depths and resolutions. Most have been limited in scope and many of the more comprehensive geospatial datasets must either be licensed or are stored in proprietary formats, making access for scholarly analysis difficult. To address these issues we have developed Cliopatria, a comprehensive open-source geospatial dataset of worldwide states from 3400BCE to 2024CE. Presently it comprises over 1600 political entities sampled at varying timesteps and spatial scales. Here, we discuss its construction, its scope, and its current limitations.

Soil fertility depletion presents a negative feedback mechanism that could have impacted early adopters of agriculture. We consider whether such feedback can lead to population cycles among early agriculturalists, such as the boom-and-bust patterns suggested by an increasing amount of evidence for Neolithic Europe. Using general mathematical arguments, we show that this is unlikely, due to the interplay of two factors. First, there is an important mathematical difference between biotic (i.e., logistic) and abiotic resource replenishment; soil nutrients are better modeled by the abiotic case, which leads to more stable dynamics. Second, under realistic conditions, the resource replenishment process operates on fast time scales compared to attainable population growth rates, reinforcing the tendency towards stable dynamics. Both these factors are relevant for early agricultural societies and imply that nutrient depletion is likely not the main contributing factor to boom-and-bust cycles observed in the archaeological record.

The impact of inter-group conflict on population dynamics has long been debated, especially for prehistoric and non-state societies. In this work, we consider that beyond direct battle casualties, conflicts can also create a ‘landscape of fear’ in which many non-combatants near theatres of conflict abandon their homes and migrate away. This process causes population decline in the abandoned regions and increased stress on local resources in better-protected areas that are targeted by refugees. By applying analytical and computational modelling, we demonstrate that these indirect effects of conflict are sufficient to produce substantial, long-term population boom-and-bust patterns in non-state societies, such as the case of Mid-Holocene Europe. We also demonstrate that greater availability of defensible locations act to protect and maintain the supply of combatants, increasing the permanence of the landscape of fear and the likelihood of endemic warfare.