Assessing Simulations of Imperial Dynamics and Conflict in the Ancient World

TL;DR

This paper evaluates a model of imperial dynamics in human history by comparing its predictions to historical data, revealing strengths in population density correlation but limitations in reproducing individual polity shapes, and suggests future improvements.

Contribution

It implements and assesses a model of imperial density, providing reproducible code and data, and explores how modifications affect its accuracy in simulating historical patterns.

Findings

Good correlation with population density ($R^2 < 0.75$).

Moderate correlation with historical conflict ($R^2 < 0.42$).

Greedy attack behavior degrades model performance.

Abstract

The development of models to capture large-scale dynamics in human history is one of the core contributions of cliodynamics. Most often, these models are assessed by their predictive capability on some macro-scale and aggregated measure and compared to manually curated historical data. In this report, we consider the model from Turchin et al. (2013), where the evaluation is done on the prediction of "imperial density": the relative frequency with which a geographical area belonged to large-scale polities over a certain time window. We implement the model and release both code and data for reproducibility. We then assess its behaviour against three historical data sets: the relative size of simulated polities vs historical ones; the spatial correlation of simulated imperial density with historical population density; the spatial correlation of simulated conflict vs historical conflict. At the global level, we show good agreement with population density ($R^2 < 0.75$), and some agreement with historical conflict in Europe ($R^2 < 0.42$). The model instead fails to reproduce the historical shape of individual polities. Finally, we tweak the model to behave greedily by having polities preferentially attacking weaker neighbours. Results significantly degrade, suggesting that random attacks are a key trait of the original model. We conclude by proposing a way forward by matching the probabilistic imperial strength from simulations to inferred networked communities from real settlement data.