在对抗性环境中运用联合分析与机器学习优选候选者画像

Selecting Optimal Candidate Profiles in Adversarial Environments Using Conjoint Analysis and Machine Learning

April 26, 2025

作者: Connor T. Jerzak, Priyanshi Chandra, Rishi Hazra

cs.AI

摘要

联合分析作为因子实验设计的一种应用，是社会科学研究中用于探讨多维偏好的常用工具。在政治分析背景下的此类实验中，受访者需在两位具有随机选取特征（可能包括党派归属、政策立场、性别及种族）的假设政治候选人之间做出选择。我们探讨了识别最优候选人特征组合的问题。由于独特特征组合的数量远超典型联合实验中的观测总数，精确确定最优组合实属不可能。为应对这一识别难题，我们推导出一种最优随机干预策略，该策略表现为旨在实现最有利平均结果的各种属性概率分布。我们首先考察单一政党优化其候选人选择的情境，随后转向更为现实的双政党同时且相互对立地优化各自候选人选择的情形。我们将所提出的方法应用于一项关于美国总统选举投票选择的现有候选人选择联合实验。研究发现，与非对抗性方法相比，对抗性机制下的预期结果落在历史选举结果范围内，且该方法建议的最优策略相较于非对抗性方法得出的策略，更可能与实际观察到的候选人特征相符。这些发现表明，将对抗性动态纳入联合分析，可能为从实验中获取的社会科学数据提供独特洞见。

English

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.