Attributes: Selective Learning and Influence

• 成果类型: Article
• 署名作者: Bardhi, Arjada
• 署名单位: New York University
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA18355
• 发表日期: 2024
• 页码: 311-353
• 关键词: MULTIPLE ATTRIBUTES; INFORMATION; search; experimentation

摘要

An agent selectively samples attributes of a complex project so as to influence the decision of a principal. The players disagree about the weighting, or relevance, of attributes. The correlation across attributes is modeled through a Gaussian process, the covariance function of which captures pairwise attribute similarity. The key trade-off in sampling is between the alignment of the players' posterior values for the project and the variability of the principal's decision. Under a natural property of the attribute correlation-the nearest-attribute property (NAP)-each optimal attribute is relevant for some player and at most two optimal attributes are relevant for only one player. We derive comparative statics in the strength of attribute correlation and examine the robustness of our findings to violations of NAP for a tractable class of distance-based covariances. The findings carry testable implications for attribute-based product evaluation and strategic selection of pilot sites.