Incentives for expressing opinions in online polls

Prediction markets efficiently extract and aggregate the private information held by individuals about events and facts that can be publicly verified. However, facts such as the effects of raising or lowering interest rates can never be publicly verified, since only one option will be implemented. Online opinion polls can still be used to extract and aggregate private information about such questions. This paper addresses incentives for truthful reporting in online opinion polls. The challenge lies in designing reward schemes that do not require a-priori knowledge of the participants' beliefs. We survey existing solutions, analyze their practicality and propose a new mechanism that extracts accurate information from rational participants.

[1]  Paul Resnick,et al.  Eliciting Informative Feedback: The Peer-Prediction Method , 2005, Manag. Sci..

[2]  Richard P. McLean,et al.  Optimal Selling Strategies under Uncertainty for a Discriminating Monopolist When Demands Are Interdependent , 1985 .

[3]  Thomas A. Rietz,et al.  Wishes, expectations and actions: a survey on price formation in election stock markets , 1999 .

[4]  Bernardo A. Huberman,et al.  Forecasting uncertain events with small groups , 2001, EC '01.

[5]  Makoto Yokoo,et al.  The effect of false-name bids in combinatorial auctions: new fraud in internet auctions , 2004, Games Econ. Behav..

[6]  Stephen Figlewski Subjective Information and Market Efficiency in a Betting Market , 1979, Journal of Political Economy.

[7]  David M. Pennock,et al.  Prediction Markets: Does Money Matter? , 2004, Electron. Mark..

[8]  Boi Faltings,et al.  Collusion-resistant, incentive-compatible feedback payments , 2007, EC '07.

[9]  Vincent Conitzer,et al.  Complexity of Mechanism Design , 2002, UAI.

[10]  Vincent Conitzer,et al.  Limited verification of identities to induce false-name-proofness , 2007, TARK '07.

[11]  Koleman Strumpf,et al.  Manipulating political stock markets: A field experiment and a century of observational data , 2006 .

[12]  C. d'Aspremont,et al.  Incentives and incomplete information , 1979 .

[13]  Allen M. Poteshman Unusual Option Market Activity and the Terrorist Attacks of September 11, 2001* , 2006 .

[14]  Boi Faltings,et al.  Minimum payments that reward honest reputation feedback , 2006, EC '06.

[15]  Charles R. Plott,et al.  Information Aggregation Mechanisms: Concept, Design and Implementation for a Sales Forecasting Problem , 2002 .

[16]  Sandip Debnath,et al.  Modelling Information Incorporation in Markets, with Application to Detecting and Explaining Events , 2002, UAI.

[17]  Michihiro Kandori,et al.  Private Observation, Communication and Collusion , 1998 .

[18]  Boi Faltings,et al.  Robust Incentive-Compatible Feedback Payments , 2006, TADA/AMEC.

[19]  Nancy L. Stokey,et al.  Information, Trade, and Common Knowledge , 1982 .

[20]  David M. Pennock,et al.  Extracting collective probabilistic forecasts from web games , 2001, KDD '01.

[21]  Andrew Leigh,et al.  Three Tools for Forecasting Federal Elections: Lessons from 2001 , 2001 .

[22]  Colin Camerer Can Asset Markets Be Manipulated? A Field Experiment With Racetrack Betting , 1998, Journal of Political Economy.

[23]  D. Prelec A Bayesian Truth Serum for Subjective Data , 2004, Science.

[24]  R. Roll Orange Juice and Weather , 1984 .

[25]  R. Zeckhauser,et al.  Efficiency Despite Mutually Payoff-Relevant Private Information: The Finite Case , 1990 .

[26]  Robert T. Clemen,et al.  Incentive contrats and strictly proper scoring rules , 2002 .