Computational Techniques for Studying Everyday Multiattribute Choice

日常选择对象，如食品、电影、衣服和消费品，可以被视为具有不同的属性或特征。虽然日常物品之间的选择涉及到关注和评估这些属性，但属性本身可能很复杂，研究人员不容易观察到。该项目试图开发计算技术，通过将机器学习和统计学的见解与决策研究中的现有理论相结合，来揭示日常选择中涉及的属性。它提供了利用机器学习和数据科学发展的可能性，以推进我们对日常决策的理论理解，并在此过程中，通过告知和改善重要决策的实施和政策，如与医疗保健或退休计划有关的决策，产生更广泛的影响。第一个是计算性的，涉及使用统计技术从大规模用户生成的互联网数据中恢复（否则无法观察）真实世界选择对象的属性表示。第二个主要组成部分是经验的，并涉及使用这些恢复的属性，结合现有的多属性决策规则，研究多属性选择之间的各种现实世界的对象。总体而言，该项目适用于三个领域所提出的方法：电影的选择，书籍的选择，和食物的选择，并为这些领域中的每一个，试图预测选择概率，决策时间，并判断属性的重要性，在自然决策问题，涉及电影，书籍，和食品，在实验室的参与者。以类似的方式，该项目使用这些域来测试是否行为效应，如选择集依赖和参考依赖，建立使用类型的程式化实验流行的多属性研究，也持有时，所考虑的对象是自然的，并没有使用明确的属性对象矩阵描述。最后，这个项目使用这些域来研究决策，其中选择集本身存储在内存中，并没有明确地呈现给决策者。

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.