In recent decades, historiography has undergone a profound shift away from monocausal explanations of past events toward more nuanced, multifactorial, and interactive models. The recognition that historical outcomes are not the product of singular forces—be they economic, political, or cultural—but rather emerge from the interplay of multiple, often heterogeneous elements, has opened new methodological avenues.

In this perspective, historical social systems can be legitimately conceptualized as complex dynamical systems. They exhibit strong path dependence: current states are not merely influenced by the past but deeply embedded in historical trajectories. Moreover, the behaviour of such systems is not easily predictable by examining individual components in isolation. As P.W. Anderson famously stated, “More is different”—a phrase that aptly captures the symmetry-breaking that occurs when micro-level interactions of individual human beings give rise to macro-level phenomena such as revolutions, institutional transformations, or societal collapses. The study of history, then, is not merely the accumulation of facts about the past, but a search for structure in systems where interaction effects dominate and linear intuition fails.

Within this context, mathematical modelling and computational approaches to historical phenomena have begun to gain traction. These methods represent a relatively recent development, propelled by advances in data availability, computational power, and the maturation of complex systems science. Modelling the past is not only a retrospective exercise; it offers a deeper understanding of the mechanisms through which societies function, adapt, and break down—questions that are as relevant today as they were centuries ago, especially given that economic collapses, political upheavals, and cultural fragmentation are not unique to specific periods but appear across time and geographies. By modelling such phenomena, scholars may identify latent regularities or causal mechanisms that would otherwise remain hidden in narrative analysis: is not a substitute for historical interpretation, but a complementary tool that can sharpen its explanatory power.

This Research Topic aims to consolidate current research at the intersection of historical inquiry and complex systems science, bringing together contributions focused on analytical and computational modelling, as well as the exploitation of novel data sources. The objective is to highlight how methodological advances and interdisciplinarity could lead to generation of novel knowledge, otherwise inaccessible through conventional methods.

The papers considered include, but are not limited to, the following topics:
• Mathematical models of historical phenomena
• Computational models of historical phenomena
• Application of network analysis to the study of historical phenomena
• Cliodynamics
• Presentation of new systems, techniques, and datasets for studying historical phenomena
• Utilization of Large Language Models (LLMs) to study and interpret historical phenomena

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Complex Systems Approaches to Historical Dynamics: Mathematical and Computational Models in Historiography