Reconstructing Interstate Conflict Networks - An Agent-Based Model Anchored in HIIK Data

Interstate conflict networks are frequently represented as mappings of dyadic feature combinations, which can be represented as graph-based networks. Such graphs encode structured macro-patterns, including but not limited to positions, clusters, and polarization, that require mechanistic interpretations and explanation. The research underlying this abstract develops an agent-based model (ABM) to reconstruct annual interstate conflict networks derived from the Conflict Barometer of the Heidelberg Institute for International Conflict Research (HIIK) (HIIK, various years). Rather than treating these networks as graphs and illustrations, we utilize them as empirical macro-targets for simulation-based explanation in the tradition of generative social science (Epstein, 2006).

Between 2014 and 2024, and on a yearly basis, the HIIK Conflict Barometer systematically coded political conflicts worldwide using a qualitative, measure-based methodology and an ordinal intensity scale (1-5), distinguishing disputes, crises, and wars. The resulting annual interstate networks can be used to represent states as nodes and conflict-intensity as weighted, undirected edges. These graphs, in turn, reveal recurring structural features, such as high-betweenness states, state clustering, and varying intensity distributions. However, these graphs cannot provide insights into the micro-processes that generate them. Research in international conflict networks emphasizes that dyadic conflict behavior is embedded in broader relational structures characterized by diffusion and bloc formation (Maoz, 2010), reinforcing the need for dynamic modeling.

To address this gap and support the future prediction of dyadic state combinations with the potential to escalate, we construct an ABM at monthly granularity in which state agents are endowed with heterogeneous capabilities, institutional characteristics, and endogenous stress variables. In our model, dyadic conflict intensity evolves through probabilistic escalation and de-escalation processes driven by baseline tension and retaliation, cost sensitivity and fatigue, network spillover or conflict transmissibility, as well as alignment pressure via triadic closure and alliances. Third-party mediation mechanisms further enable mediation effects to emerge endogenously. Annual networks are obtained by aggregating simulated monthly intensities. They are then evaluated and calibrated through comparison with HIIK data, including the empirical distribution of dyadic intensity levels, rank stability of high-betweenness states, network density and component structure, as well as modular clustering patterns. Our validation relies on out-of-sample yearly comparisons and tests.

Our preliminary results indicate that a parsimonious combination of dyadic retaliation and network-mediated spillovers can already be used to reproduce mediation and polarization patterns observed in HIIK networks, while alliance-related parameters affect cluster rigidity rather than aggregate conflict prevalence. Our findings thus suggest that interstate conflict networks are best understood as macro-representations of micro-interactions rather than static representations of antagonism. Our work further demonstrates how conflict data can anchor ABM calibration. Substantively, it clarifies structural drivers of mediation and alliance formation in interstate conflicts and provides a framework for counterfactual and experimental analyses.

References

Epstein, J. M. (2006). Generative Social Science. Princeton University Press. Heidelberg Institute for International Conflict Research (HIIK). (Various years). Conflict Barometer. Heidelberg. Maoz, Z. (2010). Networks of Nations. Cambridge University Press.