Attributes: Selective Learning and Influence.
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 pairw...
| Published in: | Econometrica Vol. 92; no. 2; pp. 311 - 354 |
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| Format: | Article |
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Wiley-Blackwell
Mar2024
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=176119276&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 176119276 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00129682 ECN jtl: Econometrica issn: 00129682 maglogo: Y pubinfo: dt: Mar2024 vid: 92 iid: 2 pid: 480 pub: Wiley-Blackwell artinfo: ui: 176119276 10.3982/ECTA18355 ppf: 311 ppct: 43 formats: tig: atl: Attributes: Selective Learning and Influence. aug: au: Bardhi, Arjada affil: Department of Economics, New York University su: Distance education Gaussian processes Statics sug: subj: Distance education Administration of Education Programs All Other Miscellaneous Schools and Instruction Gaussian processes Statics keyword: Attribute covariance Gaussian sample paths nearest‐attribute property powered‐exponential covariances strategic sampling Attribute covariance Gaussian sample paths nearest‐attribute property powered‐exponential covariances strategic sampling ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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