At the heart of this volume lies a critical inquiry into an age-old question: how have diverse societies historically conceptualized the supernatural regulation of human behavior through notions of divine punishment and reward? From the well-known frameworks of Abrahamic religions that emphasize an omnipotent deity judging human actions to karmic traditions in Asia that posit a recursive moral cause-and-effect across lifetimes, the book meticulously tracks the varied manifestations of moralizing agents in religious systems worldwide. The editors—Jennifer Larson, Jenny Reddish, and Peter Turchin—bring together leading archaeologists, historians, and anthropologists to examine this phenomenon not simply from theological or doctrinal perspectives but within the broader context of social and political development.

One particularly compelling insight from the collection is the challenge it poses to simplistic models that assert moralizing gods as initial catalysts for social complexity. Instead, the volume provides robust evidence suggesting that moralizing supernatural punishment typically emerged after societies had already attained significant levels of organizational sophistication. This is exemplified in the analysis of ancient Egypt, where inscriptions from the Old Kingdom period (c. 2575–2150 BCE) exhibit early concepts of divine justice through the principle of ma’at, symbolizing cosmic order and moral balance. These beliefs matured by the New Kingdom era (c. 1540–1070 BCE) into elaborate afterlife judgments governing the fate of the soul—yet similar religious formulations only appeared in many Eurasian societies during the Axial Age, centuries later.

The book moves beyond traditional Abrahamic and Asian paradigms to bring to light the remarkable diversity of moralizing religions and their underpinning supernatural frameworks. For instance, it explores the ancient Indo-European polytheistic systems, where gods often functioned with self-interested motivations rather than universal moral supervision, and where concepts such as oaths and treaties played an outsized role in social cohesion. These sacred promises, witnessed prominently in Greek mercantile and judicial contexts, illustrate how deities were invoked to enforce social norms and interpersonal morality pragmatically, rather than embodying abstract moral principles.

Perhaps most striking is the treatment of non-doctrinal and indigenous religious systems within the Americas. Revisiting colonial-era accounts critically, archaeologist R. Alan Covey challenges long-held assumptions about widespread belief in moralizing gods among precontact Native American societies. His rigorous examination suggests these beliefs were either absent or considerably less developed, complicating the narrative that moralizing supernatural agents were universal drivers of sociopolitical complexity. This nuanced approach underscores the importance of contextual and culturally specific perspectives rather than applying a one-size-fits-all model of religious evolution.

The contributions also address doctrinal religions such as Buddhism, Hinduism, Islam, and Manichaeism, highlighting internal diversities and temporal shifts within their moralizing frameworks. Contrary to simplistic views of divine punishment as the sole mechanism enforcing ethical behavior, the book demonstrates that ritual practices, institutional frameworks, and shared community experiences have often played equally critical roles in shaping and sustaining moral order. The veneration of bodhisattvas in Mahayana Buddhism, daily prayers in Islam, and martyr festivals within Manichaeism serve as vivid examples of the ritualistic underpinnings that reinforce moral norms in conjunction with supernatural beliefs.

Another groundbreaking aspect of the volume is its use of data-driven methods integrated with expert knowledge. Leveraging the extensive metadata and longitudinal datasets from the Seshat Databank, the editors employ quantitative and qualitative analyses to trace patterns in the emergence, transformation, and influence of moralizing religions. This interdisciplinary approach not only enhances the granularity of historical understanding but also enables testing of scientific hypotheses about cultural evolution, challenging and refining previous theoretical models like those presented in Norenzayan’s Big Gods and Johnson’s God Is Watching You.

A key takeaway is the temporal disjunction between the rise of social complexity and the appearance of fully articulated moralizing supernatural systems. The book contends that while morally prescriptive religions do not appear to have triggered the initial formation of large-scale societies, once established, their institutional and ideological frameworks exerted substantial influence on imperial expansion, statecraft, and the stabilization of social hierarchies. This suggests a dynamic feedback loop where religion both shapes and is shaped by political and economic forces over millennia.

Editors Jenny Reddish and Peter Turchin emphasize the global scale of their inquiry, noting how it challenges existing Eurocentric and doctrinal-centric perspectives that have dominated scholarly discourse. By covering more than 30 world regions, the book highlights the tremendous heterogeneity of moralizing belief systems and their sociopolitical contexts, from ancient Hawaiian state gods to Aztec afterlife beliefs for warriors. This expansive scope invites readers to reconsider preconceived notions about how morality and religion intertwine with human history globally.

The Seshat History of Moralizing Religion also delves into the social mechanisms by which moralizing supernatural concepts have been embedded within broader societal practices. The detailed examination of oath-taking and treaty-making rituals reveals the sophistication with which religious ideas have been mobilized to mediate interpersonal and political relations. Observing how these practices persisted in various forms from antiquity through classical civilizations to contemporary societies underscores the enduring functional importance of religiously sanctioned moral frameworks.

Complementing this historical and cross-cultural exploration is an engagement with theoretical debates within anthropology and sociology concerning the origins and functions of religion. The collected essays collectively propose that moralizing religion is neither monolithic nor static but rather a flexible cultural adaptation that has co-evolved with shifts in societal scale, social complexity, and political economy. Such a perspective paves the way for new hypotheses about the contingencies of religious change and offers methodological innovations for future research.

In essence, The Seshat History of Moralizing Religion redefines the frontier of scholarship on the evolution of religion and morality by blending rigorous empirical research with broad theoretical vision. It transcends disciplinary boundaries to present a richly textured, historically informed narrative of how moralizing beliefs and practices have intersected with human social development. For anyone interested in understanding the deep roots of religion’s moral dimensions and their implications for human societies past and present, this volume provides an indispensable resource grounded in scientific inquiry and comprehensive historical analysis.

Subject of Research: The historical and comparative development of moralizing religion and its role in social complexity.

Article Title: The Seshat History of Moralizing Religion: A Comprehensive Exploration of Divine Morality Across Cultures

News Publication Date: July 21, 2025

Web References:

• The Seshat History of Moralizing Religion – Beresta Books
• Seshat: Global History Databank
• Complexity Science Hub (CSH)

References:

• Turchin, P., et al. (2022). “Moralizing Gods and Social Complexity.” Paper link
• Norenzayan, A. (2013). Big Gods. Princeton University Press.
• Johnson, D. D. P. (2015). God Is Watching You. Oxford University Press.

Image Credits: Beresta Books

With the advent of sophisticated AI tools, the research team found themselves grappling with a new question: could these machine learning models help historians and archaeologists uncover deeper insights into the past? To explore this possibility, they embarked on a rigorous assessment to gauge the understanding of historical content by these advanced AI systems, traditionally associated with a range of proficiency in various domains. This ambitious project positions itself as the first of its kind, aiming to set a benchmark for assessing the capacity of large language models (LLMs) to grapple with intricate historical knowledge.

The significance of this inquiry cannot be overstated, particularly in light of recent advancements in AI. As these models continue to permeate various fields—from law to media—the team was curious about the applicability of such technology to historical analysis. AI systems like ChatGPT have achieved remarkable success in specific contexts; however, Turchin pointed out their notable limitations when assessing societies beyond the confines of Western-centric narratives. This divergence raises questions about the underlying biases in training datasets that these AI technologies utilize, thereby impacting their interpretive frameworks.

The researchers presented their findings at the NeurIPS 2024 conference, a prestigious gathering focused on advancements in AI and machine learning. It was at this forum that they disclosed the results from their rigorous experiments, which revealed that even the most advanced language model, GPT-4 Turbo, managed only a 46% on a four-choice question test specifically designed for expert-level historical inquiry. This performance, though statistically better than random guessing, underscores a pervasive gap in AI’s understanding of nuanced historical context—a stark contrast to the model’s more robust performance in legally defined tasks or quantitative analysis.

One alarming discovery was the staggering domain specificity of artificial intelligence; while these models excelled in areas with clear baseline facts, they faltered when engaging with ambiguous or interpretative narratives inherent in historical studies. Del Rio-Chanona, a pivotal figure in this research, expressed her surprise at the AI’s performance, having anticipated a higher level of success based on its training in factual knowledge. This skepticism highlights the essential role of expert interpretation in the understanding of historical frameworks, suggesting that while AI can perform admirably at certain tasks, it lacks a comprehensive worldview required for deeper analysis.

The benchmark established by Turchin and his team set out to challenge these AI systems with graduate-level inquiries found throughout the Seshat Databank. By leveraging this extensive resource, which spans over 36,000 data points and 2,700 scholarly references, the researchers aimed to discern not just factual accuracy but also the models’ ability to infer relationships between events based on indirect evidence. This facet of inquiry is critical; historical narratives often depend on synthesizing disparate data points into a coherent understandings of past events.

Their results released a wealth of insights into the models’ performances across different temporal epochs and geographical regions. Significantly, the chatbots demonstrated the highest accuracy when addressing questions pertaining to ancient history, particularly in the era from 8,000 BCE to 3,000 BCE. This focus illustrates a clear advantage for AI models when dealing with foundational, established facts from well-documented periods. Yet, as the timeline advanced, especially with inquiries extending from 1,500 CE into contemporary times, the participants’ accuracy experienced a stark decline, showcasing a worrying trend in the models’ grasp of modern historical contexts.

Geographic disparities in performance were also pronounced; the machine learning models fared better in answering questions related to Latin America and the Caribbean than in the Sub-Saharan African region. Interestingly, OpenAI’s frameworks outperformed others in these areas while the Llama model excelled in coverage related to Northern America. The limitations in regions like Sub-Saharan Africa signal an ongoing issue with training data diversity, perpetuating historical narratives that may overshadow significant cultural and societal contributions from the Global South.

The nuances of performance also extended to specific categories such as legal systems and social structures, revealing a variance in the proficiency of the models depending on the theme of inquiry. While they navigated legal taxonomy with relative ease, their struggles with topics like discrimination and social mobility expose a fundamental gap in terms of understanding social complexity in human history. Notably, while these findings confirm LLMs’ impressive capabilities, they simultaneously highlight the need for deeper contextual understanding, particularly for advanced scholarly work beyond standard facts.

As the results of this study disseminate through academic and technology circles, Turchin and his team are not resting on their laurels. They have articulated a vision for advancing this research further, which includes expanding their dataset and refining the benchmark methodologies. Future endeavors aim to integrate more diverse perspectives and historical narratives, particularly those from underrepresented regions. Furthermore, anticipation is building toward testing even more advanced models, such as o3, to evaluate their potential to bridge existing knowledge gaps uncovered in this study.

The implications of this research extend beyond the realm of academic inquiry; it offers valuable insights for both historians striving for accuracy and AI developers working to enhance the models’ effectiveness. Understanding the strengths and limitations of AI in the historical domain could shape how scholars approach research methodologies in the future. It proposes a collaborative future wherein human historians and sophisticated AI coalesce to enrich our comprehension of the rich and multifaceted narratives that define human civilization.

In summation, the intersection of AI and historical scholarship has been launched into new territory through this pioneering research. By demonstrating the AI models’ current proficiency in handling expert-level historical inquiries while exposing critical gaps in understanding and interpretation, this study serves as both a call to action and a foundation for ongoing development. As we move forward into an age where AI continues to evolve, the symbiotic relationship between complex historical narratives and artificial intelligence invites a reimagined future for historical research methodologies.

Subject of Research: Historical knowledge evaluation of Artificial Intelligence models.
Article Title: Large Language Models’ Expert-level Global History Knowledge Benchmark (HiST-LLM).
News Publication Date: January 21, 2025.
Web References: Seshat Global History Databank, Complexity Science Hub.
References: NeurIPS 2024 Conference Poster.
Image Credits: Complexity Science Hub.
Keywords: Artificial intelligence, historical knowledge, language models, complexity science, Seshat Databank, AI performance assessment.