A network approach to measuring state preferences
Gallop, Max and Minhas, Shahryar (2021) A network approach to measuring state preferences. Network Science. ISSN 2050-1250 (https://doi.org/10.1017/nws.2020.44)
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Abstract
State preferences play an important role in international politics. Unfortunately, actually observing and measuring these preferences are impossible. In general, scholars have tried to infer preferences using either UN voting or alliance behavior. The two most notable measures of state preferences that have flowed from this research area are ideal points (Bailey et al.2017) and S-scores (Signorino & Ritter, 1999). The basis of both these models is a spatial weighting scheme that has proven useful but discounts higher-order effects that might be present in relational data structures such as UN voting and alliances. We begin by arguing that both alliances and UN voting are simply examples of the multiple layers upon which states interact with one another. To estimate a measure of state preferences, we utilize a tensor decomposition model that provides a reduced-rank approximation of the main patterns across the layers. Our new measure of preferences plausibly describes important state relations and yields important insights on the relationship between preferences, democracy, and international conflict. Additionally, we show that a model of conflict using this measure of state preferences decisively outperforms models using extant measures when it comes to predicting conflict in an out-of-sample context.
ORCID iDs
Gallop, Max ORCID: https://orcid.org/0000-0001-6352-4301 and Minhas, Shahryar;-
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Item type: Article ID code: 75036 Dates: DateEvent20 January 2021Published20 January 2021Published Online2 November 2020AcceptedSubjects: Political Science Department: Faculty of Humanities and Social Sciences (HaSS) > Government and Public Policy > Politics Depositing user: Pure Administrator Date deposited: 12 Jan 2021 16:17 Last modified: 29 Nov 2024 01:16 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/75036