Mechanisms for Making Crowds Truthful

We consider schemes for obtaining truthful reports on a common but hidden signal from large groups of rational, self-interested agents. One example are online feedback mechanisms, where users provide observations about the quality of a product or service so that other users can have an accurate idea of what quality they can expect. However, (i) providing such feedback is costly, and (ii) there are many motivations for providing incorrect feedback. Both problems can be addressed by reward schemes which (i) cover the cost of obtaining and reporting feedback, and (ii) maximize the expected reward of a rational agent who reports truthfully. We address the design of such incentive-compatible rewards for feedback generated in environments with pure adverse selection. Here, the correlation between the true knowledge of an agent and her beliefs regarding the likelihoods of reports of other agents can be exploited to make honest reporting a Nash equilibrium. In this paper we extend existing methods for designing incentive-compatible rewards by also considering collusion. We analyze different scenarios, where, for example, some or all of the agents collude. For each scenario we investigate whether a collusion-resistant, incentive-compatible reward scheme exists, and use automated mechanism design to specify an algorithm for deriving an efficient reward mechanism.

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

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

[3]  T. Sandholm,et al.  Applications of Automated Mechanism Design , 2003 .

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

[5]  Alice Cheng,et al.  Sybilproof reputation mechanisms , 2005, P2PECON '05.

[6]  Vincent Conitzer,et al.  Worst-case optimal redistribution of VCG payments , 2007, EC '07.

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

[8]  Tuomas Sandholm,et al.  Incentive compatible mechanism for trust revelation , 2002, AAMAS '02.

[9]  Chrysanthos Dellarocas,et al.  Strategic Manipulation of Internet Opinion Forums: Implications for Consumers and Firms , 2004, Manag. Sci..

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

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

[12]  Roberto Serrano,et al.  A Characterization of Virtual Bayesian Implementation , 2002 .

[13]  Vincent Conitzer,et al.  Incremental Mechanism Design , 2007, IJCAI.

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

[15]  Boi Faltings,et al.  Reliable QoS monitoring based on client feedback , 2007, WWW '07.

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

[17]  Roberto Serrano,et al.  A Characterization of Virtual Bayesian Implementation , 2002, Games Econ. Behav..

[18]  Boi Faltings,et al.  Enforcing Truthful Strategies in Incentive Compatible Reputation Mechanisms , 2005, WINE.

[19]  Vincent Conitzer,et al.  Automated Mechanism Design with a Structured Outcome Space , 2003 .

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

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

[22]  Arunava Sen,et al.  VIRTUAL IMPLEMENTATION IN NASH EQUILIBRIUM , 1991 .

[23]  Hitoshi Matsushima.  A new approach to the implementation problem , 1988 .

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

[25]  Hanif D. Sherali,et al.  Optimization with disjunctive constraints , 1980 .

[26]  Anat R. Admati,et al.  Research Paper Series Graduate School of Business Stanford University Noisytalk.com: Broadcasting Opinions in a Noisy Environment Broadcasting Opinions in a Noisy Environment , 2022 .

[27]  Thomas R. Palfrey,et al.  Nash Implementation Using Undominated Strategies , 1991 .

[28]  C. Ma.  Unique Implementation of Incentive Contracts with Many Agents , 1988 .

[29]  A. Gibbard.  Manipulation of Voting Schemes: A General Result , 1973 .

[30]  Paul A. Pavlou,et al.  Can online reviews reveal a product's true quality?: empirical findings and analytical modeling of Online word-of-mouth communication , 2006, EC '06.

[31]  Shu-Hsing Li,et al.  Collusion Proof Transfer Payment Schemes with Multiple Agents , 2000 .

[32]  Boi Faltings,et al.  Understanding user behavior in online feedback reporting , 2007, EC '07.

[33]  Bengt Holmstrom,et al.  Moral Hazard in Teams , 1982 .

[34]  Laura A. Dabbish,et al.  Labeling images with a computer game , 2004, AAAI Spring Symposium: Knowledge Collection from Volunteer Contributors.

[35]  A. Parasuraman,et al.  A Conceptual Model of Service Quality and Its Implications for Future Research , 1985 .

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

[37]  E. Maskin.  Nash Equilibrium and Welfare Optimality , 1999 .

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

[39]  Chrysanthos Dellarocas,et al.  Goodwill Hunting: An Economically Efficient Online Feedback Mechanism for Environments with Variable Product Quality , 2002, AMEC.

[40]  Mohammad Taghi Hajiaghayi,et al.  Automated Online Mechanism Design and Prophet Inequalities , 2007, AAAI.

[41]  Vincent Conitzer,et al.  Automated Design of Multistage Mechanisms , 2007, IJCAI.

[42]  Vincent Conitzer,et al.  An algorithm for automatically designing deterministic mechanisms without payments , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..