Incentives for Truthful Information Elicitation of Continuous Signals

We consider settings where a collective intelligence is formed by aggregating information contributed from many independent agents, such as product reviews, community sensing, or opinion polls. We propose a novel mechanism that elicits both private signals and beliefs. The mechanism extends the previous versions of the Bayesian Truth Serum (the original BTS, the RBTS, and the multi-valued BTS), by allowing small populations and non-binary private signals, while not requiring additional assumptions on the belief updating process. For priors that are sufficiently smooth, such as Gaussians, the mechanism allows signals to be continuous.

[1]  L. J. Savage.  Elicitation of Personal Probabilities and Expectations , 1971 .

[2]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[3]  Yoav Shoham,et al.  Truthful Surveys , 2008, WINE.

[4]  Lothar Thiele,et al.  OpenSense: open community driven sensing of environment , 2010, IWGS '10.

[5]  Robin Hanson,et al.  Combinatorial Information Market Design , 2003, Inf. Syst. Frontiers.

[6]  Shunsuke Ihara,et al.  Information theory - for continuous systems , 1993 .

[7]  Jens Witkowski.  Robust peer prediction mechanisms , 2015 .

[8]  David C. Parkes,et al.  A Robust Bayesian Truth Serum for Small Populations , 2012, AAAI.

[9]  Aaron D. Shaw,et al.  Designing incentives for inexpert human raters , 2011, CSCW.

[10]  David M. Pennock,et al.  A Utility Framework for Bounded-Loss Market Makers , 2007, UAI.

[11]  A. Raftery,et al.  Strictly Proper Scoring Rules, Prediction, and Estimation , 2007 .

[12]  M. Kearns,et al.  An Algorithm That Finds Truth Even If Most People Are Wrong , 2007 .

[13]  Drazen Prelec,et al.  Creating Truth-Telling Incentives with the Bayesian Truth Serum , 2013 .

[14]  David M. Pennock,et al.  Collective revelation: a mechanism for self-verified, weighted, and truthful predictions , 2009, EC '09.

[15]  Boi Faltings,et al.  A Robust Bayesian Truth Serum for Non-Binary Signals , 2013, AAAI.

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

[17]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

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

[19]  Yiling Chen,et al.  39 Information Elicitation Sans Verification , 2013 .

[20]  Anirban Dasgupta,et al.  Crowdsourced judgement elicitation with endogenous proficiency , 2013, WWW.

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

[22]  David C. Parkes,et al.  Peer prediction without a common prior , 2012, EC '12.

[23]  Yoav Shoham,et al.  Eliciting truthful answers to multiple-choice questions , 2009, EC '09.

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

[25]  Boi Faltings,et al.  Incentives for Answering Hypothetical Questions , 2011 .

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