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 gametheoretic 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 problems: 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