Attributes: Selective Learning and Influence

Arjada Bardhi

    Research output: Contribution to journalArticlepeer-review


    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.

    Original languageEnglish (US)
    Pages (from-to)311-353
    Number of pages43
    Issue number2
    StatePublished - Mar 2024


    • Attribute covariance
    • Gaussian sample paths
    • nearest-attribute property
    • powered-exponential covariances
    • strategic sampling

    ASJC Scopus subject areas

    • Economics and Econometrics


    Dive into the research topics of 'Attributes: Selective Learning and Influence'. Together they form a unique fingerprint.

    Cite this