在對抗性環境中使用聯合分析與機器學習選擇最佳候選人檔案

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

聯合分析作為因子實驗設計的一種應用，是社會科學研究中研究多維偏好的常用工具。在政治分析背景下的此類實驗中，受訪者被要求從具有隨機選取特徵的兩位假想政治候選人中做出選擇，這些特徵可能包括黨派立場、政策主張、性別和種族。我們考慮識別最優候選人特徵組合的問題。由於獨特特徵組合的數量遠超典型聯合實驗中的總觀測數，因此無法精確確定最優特徵組合。為應對這一識別挑戰，我們推導出一種最優隨機干預，它代表著旨在實現最有利平均結果的各種屬性的概率分佈。我們首先考慮一個政黨優化其候選人選擇的環境。然後轉向更為現實的情況，即兩個政黨同時且相互對立地優化各自的候選人選擇。我們將所提出的方法應用於一項關於美國總統投票選擇的現有候選人選擇聯合實驗中。我們發現，與非對抗性方法相比，對抗性制度下的預期結果落在歷史選舉結果的範圍內，且該方法建議的最優策略比非對抗性方法得出的策略更有可能與實際觀察到的候選人相匹配。這些發現表明，將對抗性動態納入聯合分析，可能從實驗中獲取社會科學數據的獨特洞見。

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.