2020/11/01 by Zhanna Terechshenko · 19 citations
Psychology · Social Sciences · #Artificial intelligence #Bayesian probability #Benchmark (surveying) #Cognitive psychology #Computer science #Construct (python library) #Data mining #Econometrics #Economics #Event (particle physics) #Hostility #International Relations and Foreign Policy #International conflict #Latent variable #Measure (data warehouse) #Peacebuilding and International Security #Poison control #Political Conflict and Governance #Political science #Politics #Psychology #Reciprocity (cultural anthropology) #Social psychology
paper · doi:10.1177/0022343320962546
published in Journal of Peace Research 57(6), 764-776 (SAGE Publishing)
openalex publication_date 2020/11/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/25
Abstract The majority of studies on international conflict escalation use a variety of measures of hostility including the use of force, reciprocity, and the number of fatalities. The use of different measures, however, leads to different empirical results and creates difficulties when testing existing theories of interstate conflict. Furthermore, hostility measures currently used in the conflict literature are ill suited to the task of identifying consistent predictors of international conflict escalation. This article presents a new dyadic latent measure of interstate hostility, created using a Bayesian item-response theory model and conflict data from the Militarized Interstate Dispute (MID) and Phoenix political event datasets. This model (1) provides a more granular, conceptually precise, and validated measure of hostility, which incorporates the uncertainty inherent in the latent variable; and (2) solves the problem of temporal variation in event data using a varying-intercept structure and human-coded data as a benchmark against which biases in machine-coded data are corrected. In addition, this measurement model allows for the systematic evaluation of how existing measures relate to the construct of hostility. The presented model will therefore enhance the ability of researchers to understand factors affecting conflict dynamics, including escalation and de-escalation processes.