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Truthful opinions from the crowds

Truthful opinions from the crowds Truthful Opinions from the Crowds RADU JURCA Google Inc. and BOI FALTINGS Arti cial Intelligence Lab, Ecole Polytechnique F´d´rale de Lausanne (EPFL) e e An increasing number of applications of arti cial intelligence extract knowledge from large groups of agents, also termed the wisdom of the crowds. One example are online feedback forums (also known as reputation mechanisms) for obtaining information about the products or services. The testimonies of previous buyers disclose hidden product attributes such as quality, reliability, ease of use, etc., that can only be observed after the purchase. This previously unavailable information allows the buyers to make more e ƒcient decisions, and eliminates some of the problems that would otherwise lead to the collapse of online markets [Akerlof 1970]. Recent studies, however, raise important questions regarding the ability of existing reputation mechanisms to re ‚ect the real quality of a product. First, the absence of clear incentives drives only some of the users to voice their opinions. For example, most Amazon ratings for a book or CD are either very good, or very bad, while controlled experiments on the same items reveal normally distributed opinions [Hu et al. 2006]. Second, some users intentionally lie to http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM SIGecom Exchanges Association for Computing Machinery

Truthful opinions from the crowds

ACM SIGecom Exchanges , Volume 7 (2) – Jun 1, 2008

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2008 by ACM Inc.
ISSN
1551-9031
DOI
10.1145/1399589.1399592
Publisher site
See Article on Publisher Site

Abstract

Truthful Opinions from the Crowds RADU JURCA Google Inc. and BOI FALTINGS Arti cial Intelligence Lab, Ecole Polytechnique F´d´rale de Lausanne (EPFL) e e An increasing number of applications of arti cial intelligence extract knowledge from large groups of agents, also termed the wisdom of the crowds. One example are online feedback forums (also known as reputation mechanisms) for obtaining information about the products or services. The testimonies of previous buyers disclose hidden product attributes such as quality, reliability, ease of use, etc., that can only be observed after the purchase. This previously unavailable information allows the buyers to make more e ƒcient decisions, and eliminates some of the problems that would otherwise lead to the collapse of online markets [Akerlof 1970]. Recent studies, however, raise important questions regarding the ability of existing reputation mechanisms to re ‚ect the real quality of a product. First, the absence of clear incentives drives only some of the users to voice their opinions. For example, most Amazon ratings for a book or CD are either very good, or very bad, while controlled experiments on the same items reveal normally distributed opinions [Hu et al. 2006]. Second, some users intentionally lie to

Journal

ACM SIGecom ExchangesAssociation for Computing Machinery

Published: Jun 1, 2008

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