Selecting Optimal Candidate Profiles in Adversarial Environments Using
  Conjoint Analysis and Machine Learning
Authors: name: Connor T. Jerzak, name: Priyanshi Chandra, name: Rishi Hazra
Organization: Jerzak Labs

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.