Selecting Optimal Candidate Profiles in Adversarial Environments Using Conjoint Analysis and Machine Learning
Connor T. Jerzak, Priyanshi Chandra, Rishi Hazra
2025-05-01
Summary
This paper talks about using a mix of conjoint analysis and machine learning to figure out the best political candidate profiles, especially when different parties are competing against each other.
What's the problem?
It's really hard to know exactly what combination of candidate traits will be most successful because there are so many possible combinations, and typical experiments don't have enough data to cover them all. Plus, in real elections, parties are always trying to outsmart each other, which makes the problem even trickier.
What's the solution?
The researchers developed a method that uses probability and machine learning to estimate which candidate profiles are likely to do best, even when the data is limited and both parties are trying to pick their strongest candidates at the same time. They tested their approach using real survey data about US presidential elections and found that their method's results matched what actually happened in past elections better than older methods.
Why it matters?
This matters because it gives political parties and researchers a smarter way to predict which kinds of candidates will appeal most to voters in a real-world, competitive setting, helping them make better decisions and understand elections more deeply.
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, 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.