Lie on the Fly: Strategic Voting in an Iterative Preference Elicitation Process

A voting center is in charge of collecting and aggregating voter preferences. In an iterative process, the center sends comparison queries to voters, requesting them to submit their preference between two items. Voters might discuss the candidates among themselves, figuring out during the elicitation process which candidates stand a chance of winning and which do not. Consequently, strategic voters might attempt to manipulate by deviating from their true preferences and instead submit a different response in order to attempt to maximize their profit. We provide a practical algorithm for strategic voters which computes the best manipulative vote and maximizes the voter’s selfish outcome when such a vote exists. We also provide a careful voting center which is aware of the possible manipulations and avoids manipulative queries when possible. In an empirical study on four real world domains, we show that in practice manipulation occurs in a low percentage of settings and has a low impact on the final outcome. The careful voting center reduces manipulation even further, thus allowing for a non-distorted group decision process to take place.We thus provide a core technology study of a voting process that can be adopted in opinion or information aggregation systems and in crowdsourcing applications, e.g., peer grading in massive open online courses.

[1]  Luis C. Dias,et al.  Supporting groups in sorting decisions: Methodology and use of a multi-criteria aggregation/disaggregation DSS , 2007, Decis. Support Syst..

[2]  Simina Brânzei,et al.  How Bad Is Selfish Voting? , 2013, AAAI.

[3]  Miguel A. Costa-Gomes,et al.  Structural Models of Nonequilibrium Strategic Thinking: Theory, Evidence, and Applications , 2013 .

[4]  Nikolai S. Kukushkin,et al.  Acyclicity of improvements in finite game forms , 2011, Int. J. Game Theory.

[5]  David C. Parkes,et al.  Strategic Voting Behavior in Doodle Polls , 2015, CSCW.

[6]  Nicola Capuano,et al.  A Fuzzy Group Decision Making Model for Ordinal Peer Assessment , 2017, IEEE Transactions on Learning Technologies.

[7]  Sumit Chopra,et al.  Two of a Kind or the Ratings Game? Adaptive Pairwise Preferences and Latent Factor Models , 2010, ICDM.

[8]  Toby Walsh,et al.  Restricted Manipulation in Iterative Voting: Condorcet Efficiency and Borda Score , 2013, ADT.

[9]  Jean-Jacques Laffont,et al.  Incentives and the allocation of public goods , 1987 .

[10]  Svetlana Obraztsova,et al.  Between Fairness and a Mistrial : Consensus Under a Deadline , 2016 .

[11]  Vincent Conitzer,et al.  Strategic Voting and Strategic Candidacy , 2015, AAAI.

[12]  Vincent Conitzer,et al.  Communication complexity of common voting rules , 2005, EC '05.

[13]  Jérôme Lang,et al.  Voting procedures with incomplete preferences , 2005 .

[14]  Omer Lev,et al.  Convergence of iterative voting , 2012, AAMAS.

[15]  Joseph Y. Halpern,et al.  Knowledge and common knowledge in a distributed environment , 1984, JACM.

[16]  Vincent Conitzer,et al.  Strategic sequential voting in multi-issue domains and multiple-election paradoxes , 2011, EC '11.

[17]  Felix A. Fischer,et al.  Possible and necessary winners of partial tournaments , 2012, AAMAS.

[18]  Meir Kalech,et al.  Preference Elicitation for Group Decisions Using the Borda Voting Rule , 2015, Group Decision and Negotiation.

[19]  Joseph Y. Halpern,et al.  A Guide to the Modal Logics of Knowledge and Belief: Preliminary Draft , 1985, IJCAI.

[20]  Thomas Pfeiffer,et al.  Adaptive Polling for Information Aggregation , 2012, AAAI.

[21]  D. Monderer,et al.  Approximating common knowledge with common beliefs , 1989 .

[22]  Meir Kalech,et al.  Preference Elicitation for Group Decisions , 2014, GDN.

[23]  Francisco Herrera,et al.  Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power , 2010, Inf. Sci..

[24]  Piotr Faliszewski,et al.  Large-Scale Election Campaigns: Combinatorial Shift Bribery , 2015, AAMAS.

[25]  Toby Walsh,et al.  Uncertainty in Preference Elicitation and Aggregation , 2007, AAAI.

[26]  Omer Lev,et al.  Non-Myopic Voting Dynamics: An Optimistic Approach , 2016 .

[27]  Meir Kalech,et al.  Reducing preference elicitation in group decision making , 2016, Expert Syst. Appl..

[28]  Toby Walsh,et al.  Incompleteness and Incomparability in Preference Aggregation , 2007, IJCAI.

[29]  Gerhard Brewka,et al.  Nonmonotonic Reasoning: Logical Foundations of Commonsense By Gerhard Brewka (Cambridge University Press, 1991) , 1991, SGAR.

[30]  Ulrich Endriss,et al.  Voter response to iterated poll information , 2012, AAMAS.

[31]  Edith Elkind,et al.  Cognitive Hierarchy and Voting Manipulation , 2017, ArXiv.

[32]  Meir Kalech,et al.  Reaching a joint decision with minimal elicitation of voter preferences , 2014, Inf. Sci..

[33]  M. Kendall A NEW MEASURE OF RANK CORRELATION , 1938 .

[34]  Kwong-Sak Leung,et al.  A Survey of Crowdsourcing Systems , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[35]  Nicholas R. Jennings,et al.  Convergence to Equilibria in Plurality Voting , 2010, AAAI.

[36]  Bo An,et al.  Context-Aware Reliable Crowdsourcing in Social Networks , 2020, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

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

[38]  Omer Lev,et al.  A local-dominance theory of voting equilibria , 2014, EC.

[39]  Meir Kalech,et al.  Practical voting rules with partial information , 2010, Autonomous Agents and Multi-Agent Systems.

[40]  Vincent Conitzer,et al.  Dominating Manipulations in Voting with Partial Information , 2011, AAAI.

[41]  Andrew Beng Jin Teoh,et al.  Online Heterogeneous Face Recognition Based on Total-Error-Rate Minimization , 2020, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[42]  Bing Chen,et al.  Toward Efficient Team Formation for Crowdsourcing in Noncooperative Social Networks , 2017, IEEE Transactions on Cybernetics.

[43]  Yen-Liang Chen,et al.  Mining consensus preference graphs from users' ranking data , 2013, Decis. Support Syst..

[44]  Craig Boutilier,et al.  Multi-Winner Social Choice with Incomplete Preferences , 2013, IJCAI.

[45]  R. Aumann,et al.  Unraveling in Guessing Games : An Experimental Study , 2007 .

[46]  Toby Walsh,et al.  Where are the hard manipulation problems? , 2010, J. Artif. Intell. Res..

[47]  Mark C. Wilson,et al.  Best Reply Dynamics for Scoring Rules , 2012, ECAI.

[48]  Jürgen Dix,et al.  Nonmonotonic Reasoning: An Overview , 1997, CSLI Lecture Notes.

[49]  Omer Lev,et al.  Strategyproof Peer Selection: Mechanisms, Analyses, and Experiments , 2016, AAAI.

[50]  Craig Boutilier,et al.  Robust Approximation and Incremental Elicitation in Voting Protocols , 2011, IJCAI.

[51]  Nicholas R. Jennings,et al.  On the Convergence of Iterative Voting: How Restrictive Should Restricted Dynamics Be? , 2015, AAAI.

[52]  Ning Ding,et al.  Voting with partial information: what questions to ask? , 2013, AAMAS.

[53]  Shotaro Akaho,et al.  Supervised ordering - an empirical survey , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[54]  Toby Walsh,et al.  The Computational Impact of Partial Votes on Strategic Voting , 2014, ECAI.

[55]  Vincent Conitzer,et al.  Determining Possible and Necessary Winners under Common Voting Rules Given Partial Orders , 2008, AAAI.

[56]  Tilman Börgers,et al.  Weak Dominance and Approximate Common Knowledge , 1994 .

[57]  Svetlana Obraztsova,et al.  Strategic Voting with Incomplete Information , 2016, IJCAI.

[58]  Omer Lev,et al.  Analysis of Equilibria in Iterative Voting Schemes , 2015, AAAI.

[59]  Robin Farquharson,et al.  Theory of voting , 1969 .

[60]  Edith Elkind,et al.  Equilibria of plurality voting with abstentions , 2010, EC '10.

[61]  G. A. Miller THE PSYCHOLOGICAL REVIEW THE MAGICAL NUMBER SEVEN, PLUS OR MINUS TWO: SOME LIMITS ON OUR CAPACITY FOR PROCESSING INFORMATION 1 , 1956 .

[62]  Meir Kalech,et al.  Lie on the Fly: Iterative Voting Center with Manipulative Voters , 2015, IJCAI.

[63]  M. Satterthwaite Strategy-proofness and Arrow's conditions: Existence and correspondence theorems for voting procedures and social welfare functions , 1975 .

[64]  Svetlana Obraztsova,et al.  Strategic Candidacy Games with Lazy Candidates , 2015, IJCAI.

[65]  D. Stahl,et al.  Experimental evidence on players' models of other players , 1994 .

[66]  José Luis García Lapresta,et al.  Positional voting rules generated by aggregation functions and the role of duplication , 2017 .

[67]  Tim Roughgarden,et al.  Online Prediction with Selfish Experts , 2017, NIPS.

[68]  Omer Lev,et al.  Convergence of Iterative Scoring Rules , 2016, J. Artif. Intell. Res..

[69]  P. J. Gmytrasiewicz,et al.  A Framework for Sequential Planning in Multi-Agent Settings , 2005, AI&M.

[70]  Kevin Leyton-Brown,et al.  Beyond equilibrium: predicting human behaviour in normal form games , 2010, AAAI.

[71]  Rolf Niedermeier,et al.  A logic for causal reasoning , 2003, IJCAI 2003.

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

[73]  Vincent Conitzer,et al.  How hard is it to control sequential elections via the agenda , 2009, IJCAI 2009.

[74]  Svetlana Obraztsova,et al.  Faustian Dynamics in Sarkar's Social Cycle , 2014, ECAI.

[75]  P.-C.-F. Daunou,et al.  Mémoire sur les élections au scrutin , 1803 .

[76]  Nicholas R. Jennings,et al.  Convergence to Equilibria in Strategic Candidacy , 2015, IJCAI.

[77]  Vincent Conitzer,et al.  A Maximum Likelihood Approach towards Aggregating Partial Orders , 2011, IJCAI.