title: Selecting Optimal Candidate Profiles in Adversarial Environments Using
    Conjoint Analysis and Machine Learning
summary: 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, with optimal strategies suggested by the method
  more likely to match the actual observed candidates compared to strategies
  derived from a non-adversarial approach. These findings indicate that
  incorporating adversarial dynamics into conjoint analysis may yield unique
  insight into social science data from experiments.
paper_authors._id: 6810d357a33b41022b549c94
paper_authors.name: Connor T. Jerzak
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-02T06:37:45.543Z
paper_authors._id: 642c374b8f90c557f742db66
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/642c374b8f90c557f742db66/7Hw_z7_nY9gr5gdzYyygs.jpeg
paper_authors.fullname: Connor T. Jerzak
paper_authors.type: user
paper_authors.user: cjerzak
paper_authors._id: 6810d357a33b41022b549c95
paper_authors.name: Priyanshi Chandra
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-05T08:04:54.111Z
paper_authors._id: 67e6ca233c9439d3b27bfc13
paper_authors.avatarUrl: /avatars/a46b00a704a3ae10b7f858d8271ed587.svg
paper_authors.fullname: Priyanshi Chandra
paper_authors.type: user
paper_authors.user: Priyanshi-28
paper_authors._id: 6810d357a33b41022b549c96
paper_authors.name: Rishi Hazra
paper_title: Selecting Optimal Candidate Profiles in Adversarial Environments Using
    Conjoint Analysis and Machine Learning
paper_summary: 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, with optimal strategies suggested by the method
  more likely to match the actual observed candidates compared to strategies
  derived from a non-adversarial approach. These findings indicate that
  incorporating adversarial dynamics into conjoint analysis may yield unique
  insight into social science data from experiments.