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2023/10/12 【Oct. 13】Selecting Optimal Candidate Profiles in Adversarial Environments Using Conjoint Analysis

Dr. Connor Jerzak, University of Texas at Austin
Date: Friday, October 13 - 10:00AM~13:00PM (Taiwan Time, GMT+8); Thursday, October 12 - 21:00PM~00:00AM (US Central Time, GMT-6).
Topic: Selecting Optimal Candidate Profiles in Adversarial Environments Using Conjoint Analysis 
Abstract: Conjoint analysis, an application of factorial experimental design, is a popular tool in social science research for studying multidimensional preferences.  In such experiments in the political analysis context, respondents are asked to choose between two hypothetical political candidates with randomly selected features, which can include partisanship, policy positions, gender and race.  We consider the problem of identifying optimal candidate profiles. Because the number of unique feature combinations far exceeds the total number of observations in a typical conjoint experiment, it is impossible to determine the optimal profile exactly. To address this identification challenge, we derive an optimal stochastic intervention that represents a probability distribution of various attributes aimed at achieving the most favorable average outcome. We first consider an environment where one political party optimizes their candidate selection.  We then move to the more realistic case where two political parties optimize their own candidate selection simultaneously and in opposition to each other. We apply the proposed methodology to an existing candidate choice conjoint experiment concerning vote choice for US president. We find that, in contrast to the non-adversarial approach, expected outcomes in the adversarial regime fall within range of historical electoral outcomes.  In addition, optimal strategies in the adversarial case yield comparatively higher observed data likelihoods in an analysis of the 2016 presidential primaries. These findings indicate that incorporating adversarial dynamics into conjoint analysis may yield more realistic insights into candidate selection and optimization.
Bio: Connor Jerzak received a Master’s in Statistics and Ph.D. in Government from Harvard University, where he was advised by Kosuke Imai, Gary King, and Xiang Zhou. During graduate school, he also worked as an intern at Adobe Research in San Jose, California with Nikos Vlassis. In 2021-2022, Connor did a one-year postdoc with Adel Daoud and the AI and Global Development Lab at Linköping University in Sweden while serving as a Visiting Scholar in the Program on Governance and Local Development (GLD) at the University of Gothenburg. Since 2022, he has served at the University of Texas at Austin as an Assistant Professor in the Department of Government. His research has appeared or is forthcoming in peer-reviewed machine learning, economics, and political science venues. During spring of 2024, Connor will be teaching at Harvard University.