HoloSim. Modelling Dynamics in the Past
The HoloSim project includes a number of efforts to develop spatially-explicit agent-based models of historical processes. Theories about historical processes have long been formulated as primarily verbal models, offering compelling narratives, but with the inherent limitation that it is hard to argue about the importance and scale of specific effects and mechanisms. Translating such verbal theories into dynamic agent-based models offers a promising direction to uncover causal mechanisms approaches.
We are building a number of flexible agent-based models that can represent diverse and competing hypotheses about historical processes, including social, political and environmental factors. We utilize a range of data sources to evaluate model outcomes, directly testing key hypotheses against each other, for instance concerning the emergence and rise of state societies that became dominant in recent millennia, the establishment and spread of hierarchically-organized social formations, and the key mechanisms driving the rise-and-fall of large, complex societies across millennia.
HoloSim Research Projects
Modelling boom-and-bust patterns in the mid-Holocene. The first phase of this project targets Neolithic Europe and aims to explore the drivers of repeated population booms and busts that have been reported recently, in a range of studies. We have developed a spatially explicit model that incorporates the role of climate and conflict in this scenario. Given the limited set of quantitative data available for this period, a challenge is the reliable treatment of possible population proxies and assessing ways that meaningful patterns can be evaluated. In another model, we aim to investigate the processes that eventually give rise to early states via a process of conquest and consolidation. We have started by implementing a model of the formation and conflict among complex chiefdoms and a set of simple rules that result in an evolutionary process that shows the gradual emergence of polities of increasing size, on the timescale of centuries to millennia.
Modeling the Rise, Spread, and Fall of Large Complex States. Prior investigations suggest that increasing agricultural productivity and military prowess are sufficient to explain the increase in various polity (state) scales, such as their territory, population, and various political hierarchies. These investigations also point to adoption of critical technologies in certain regions and times that radically change the scale of polities in a punctuated manner and that their subsequent adoption and spread help explain the spatial and temporal distribution of states.
This research project develops dynamic agent-based models and compares output against large empirical datasets to explore the fundamental question: What is the minimum set of factors that can plausibly explain observed polity dynamics?