Classifying strategy use in multiattribute subjective choice: Application to conjoint experiments in political science

Conjoint experiments are extensively used across multiple disciplines to make inferences about preferences over multiattribute alternatives. Much previous research has analyzed the importance of attributes at the aggregate level. Here, we focus on understanding individual-level behavior and, specifically, on strategy use. We present a Bayesian cognitive model of strategy use that performs attribute weight inference (how important an attribute is), attribute level inference (which value of the attribute is favored), and latent-mixture inference to classify strategy use among well-established strategies in cognitive science. We then validate and apply our model to two conjoint experiments ( N = 3,762) focusing on three political domains (attitudes toward immigrants, political candidates, and climate policy). We find that the simplest heuristic strategy is the highest probability in all conditions and that approximately 67–76% of subjects are classified as using different strategies depending on the political domain. Our model additionally highlights how aggregate-level inference on the weight of an attribute can be meaningfully decomposed by individual differences in strategy use and preferences over attributes. In addition, our experiments include a set of experimental design manipulations that increase different dimensions of complexity and show their impact on strategy classification.