Everyday choice objects, such as food items, movies, clothes, and consumer goods, can be seen as possessing different attributes or features. Although choices between everyday objects involve attending to and evaluating these attributes, the attributes themselves may be complex and not easily observed by researchers. This project attempts to develop computational techniques to uncover the attributes involved in everyday choice by combining insights from machine learning and statistics with existing theories in decision making research. It offers the possibility harnessing developments in machine learning and data science to advance our theoretical understanding of everyday decision making and, in the process, yield broader impacts by informing and improving implementation of and policy toward important decisions like those concerned with health care or retirement planning.
There are two major components to this project. The first is computational, and involves the use of statistical techniques to recover (otherwise unobservable) attribute representations for real-world choice objects from large-scale user-generated internet data. The second major component is empirical, and involves the use of these recovered attributes, combined with existing multi-attribute decision rules, to study multi-attribute choices between various real-world objects. Overall, the project applies the proposed approach to three domains: movie choice, book choice, and food choice, and for each of these domains, attempts to predict choice probabilities, decision times, and judgments of attribute importance in naturalistic decision problems involving movies, books, and food items, given to participants in the laboratory. In a similar manner, this project uses these domains to test whether behavioral effects such as choice set dependence and reference dependence, established using the types of stylized experiments popular in multi-attribute research, also hold when the objects under consideration are naturalistic and are not described using explicit attribute-by-object matrices. Finally this project uses these domains to study decisions in which the choice sets themselves are stored in memory, and are not explicitly presented to decision makers.
