Scholars have written extensively about hierarchical international order, on the one hand, and war on the other, but surprisingly little work systematically explores the connection between the two. This disconnect is all the more striking given that empirical studies have found a strong relationship between the two. We provide a generative computational network model that explains hierarchy and war as two elements of a larger recursive process: The threat of war drives the formation of hierarchy, which in turn shapes states’ incentives for war. Grounded in canonical theories of hierarchy and war, the model explains an array of known regularities about hierarchical order and conflict. Surprisingly, we also find that many traditional results of the IR literature—including institutional persistence, balancing behavior, and systemic self-regulation—emerge from the interplay between hierarchy and war.

This model examines language dynamics within a social network using simulation techniques to represent the interplay of language adoption, social influence, economic incentives, and language policies. The agent-based model (ABM) focuses on interactions between agents endowed with specific linguistic attributes, who engage in communication based on predefined rules. A key feature of our model is the incorporation of network analysis, structuring agent relationships as a dynamic network and leveraging network metrics to capture the evolving inter-agent connections over time. This integrative approach provides nuanced insights into emergent behaviors and system dynamics, offering an analytical framework that extends beyond traditional modeling approaches. By combining agent-based modeling with network analysis, the model sheds light on the underlying mechanisms governing complex language systems and can be effectively paired with sociolinguistic observational data.

An empirically calibrated agent-based model of cooperation among 14 EU member states. Adaptive state-agents update their cooperation propensity through behavioural inertia, influence along the observed intra-EU trade network (IMF bilateral flows), and repeated-game payoff indicators built from verified Eurostat, Eurobarometer and IMF data (2021-2024). An anchored logistic mapping makes the observed configuration stationary in the absence of shocks, so outcomes read as deviations from the empirical baseline. The model stress-tests European cooperation to 2040 under five scenarios of increasing severity, from a baseline to a Taiwan Strait crisis counterfactual, with 1,000 Monte Carlo replications and a full sensitivity suite (one-factor-at-a-time, joint parameter sampling, breaking-point analysis, alternative functional form). Documented with the ODD protocol; self-testing and fully reproducible under fixed seeds.

chitwanabm is a spatially explicit agent-based model of population and land use in the Chitwan Valley, Nepal, designed to explore feedbacks between population and environment, with a heavy focus on community context and individual-level variation.

This simulation model is to simulate the emergence of technological innovation processes from the hypercycles perspective.

This model simulates a bank - firm credit network.

This model (CharRec) creates simulated charcoal records, based on differing natural and anthropogenic patterns of ignitions, charcoal dispersion, and deposition.

We build a computational model to investigate, in an evolutionary setting, a series of questions pertaining to happiness.

The code contains four experiments for well-being based IMRL reward features.

CEDSS is an agent-based model of domestic energy demand at the level of a small community.