{"type":"ScholarlyArticle","title":"The Power of Context in Networks: Ideal Point Models with Social Interactions","authors":[{"name":"Mohammad T. Irfan","slug":"mohammad-t-irfan","given":"Mohammad T.","family":"Irfan","affiliation":"Department of Computer Science , Bowdoin College , Brunswick , Maine 04011"},{"name":"Tucker Gordon","slug":"tucker-gordon","given":"Tucker","family":"Gordon","affiliation":"Department of Computer Science , Bowdoin College , Brunswick , Maine 04011"}],"abstract":"Game theory has been widely used for modeling strategic behaviors in networked multiagent systems. However, the context within which these strategic behaviors take place has received limited attention. We present a model of strategic behavior in networks that incorporates the behavioral context, focusing on the contextual aspects of congressional voting. One salient predictive model in political science is the ideal point model, which assigns each senator and each bill a number on the real line of political spectrum. We extend the classical ideal point model with network-structured interactions among senators. In contrast to the ideal point model's prediction of individual voting behavior, we predict joint voting behaviors in a game-theoretic fashion. The consideration of context allows our model to outperform previous models that solely focus on the networked interactions with no contextual parameters. We focus on two fundamental questions: learning the model using real-world data and computing stable outcomes of the model with a view to predicting joint voting behaviors and identifying most influential senators. We demonstrate the effectiveness of our model through experiments using data from the 114th U.S. Congress.","doi":"10.24963/ijcai.2019/858","url":"https://ijcai.org/proceedings/2019/858","pdf":"https://ijcai.org/proceedings/2019/0858.pdf","markdown":"https://ijcai.org/proceedings/2019/858.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/858","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Best Sister Conferences","track":"Best Sister Conferences","number":858,"pages":{"first":6176,"last":6180},"keywords":["Agent-based and Multi-agent Systems: Algorithmic Game Theory","Machine Learning Applications: Networks","Agent-based and Multi-agent Systems: Noncooperative Games"],"videos":[],"references":[{"position":1,"raw":"[Canes-Wrone et al., 2002] Brandice Canes-Wrone, David W Brady, and John F Cogan. 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