Cited By
View all- Procaccia A(2019)Axioms Should Explain SolutionsThe Future of Economic Design10.1007/978-3-030-18050-8_27(195-199)Online publication date: 16-Nov-2019
Making Right Decisions Based on Wrong Opinions
Pages 267 - 284
Abstract
We revisit the classic problem of designing voting rules that aggregate objective opinions, in a setting where voters have noisy estimates of a true ranking of the alternatives. Previous work has replaced structural assumptions on the noise with a worst-case approach that aims to choose an outcome that minimizes the maximum error with respect to any feasible true ranking. This approach underlies algorithms that have recently been deployed on the social choice website RoboVote.org. We take a less conservative viewpoint by minimizing the average error with respect to the set of feasible ground truth rankings. We derive (mostly sharp) analytical bounds on the expected error and establish the practical benefits of our approach through experiments.
Supplementary Material
MP4 File (04b_01benade.mp4)
- Download
- 372.05 MB
References
[1]
H. Azari Soufiani, D. C. Parkes, and L. Xia. 2012. Random Utility Theory for Social Choice. In Proceedings of the 26th Annual Conference on Neural Information Processing Systems (NIPS). 126--134.
[2]
H. Azari Soufiani, D. C. Parkes, and L. Xia. 2013. Preference Elicitation For General Random Utility Models. In Proceedings of the 29th Annual Conference on Uncertainty in Artificial Intelligence (UAI). 596--605.
[3]
H. Azari Soufiani, D. C. Parkes, and L. Xia. 2014. Computing Parametric Ranking Models via Rank-Breaking. In Proceedings of the 31st International Conference on Machine Learning (ICML). 360--368.
[4]
F. Brandt, V. Conitzer, U. Endriss, J. Lang, and A. D. Procaccia (Eds.). 2016. Handbook of Computational Social Choice. Cambridge University Press.
[5]
I. Caragiannis, S. Nath, A. D. Procaccia, and N. Shah. 2017. Subset Selection Via Implicit Utilitarian Voting. Journal of Artificial Intelligence Research 58 (2017), 123--152.
[6]
I. Caragiannis, A. D. Procaccia, and N. Shah. 2014. Modal Ranking: A Uniquely Robust Voting Rule. In Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI). 616--622.
[7]
I. Caragiannis, A. D. Procaccia, and N. Shah. 2016. When Do Noisy Votes Reveal the Truth? ACM Transactions on Economics and Computation 4, 3 (2016), article 15.
[8]
J. R. Chamberlin and P. N. Courant. 1983. Representative Deliberations and Representative Decisions: Proportional Representation and the Borda Rule. American Political Science Review 77, 3 (1983), 718--733.
[9]
Y. Chen, A. Ghosh, M. Kearns, T. Roughgarden, and J. Wortman Vaughan. 2016. Mathematical Foundations for Social Computing. Communications of the ACM 59, 12 (2016), 102--108.
[10]
V. Conitzer, M. Rognlie, and L. Xia. 2009. Preference Functions that Score Rankings and Maximum Likelihood Estimation. In Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI). 109--115.
[11]
V. Conitzer and T. Sandholm. 2005. Common Voting Rules as Maximum Likelihood Estimators. In Proceedings of the 21st Annual Conference on Uncertainty in Artificial Intelligence (UAI). 145--152.
[12]
P. Diaconis and R. L. Graham. 1977. Spearman's footrule as a measure of disarray. Journal of the Royal Statistical Society: Series B 39, 2 (1977), 262--268.
[13]
E. Elkind, P. Faliszewski, and A. Slinko. 2010. Good Rationalizations of Voting Rules. In Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI). 774--779.
[14]
E. Elkind and N. Shah. 2014. Electing the Most Probable Without Eliminating the Irrational: Voting Over Intransitive Domains. In Proceedings of the 30th Annual Conference on Uncertainty in Artificial Intelligence (UAI). 182--191.
[15]
T. Lu and C. Boutilier. 2011. Budgeted Social Choice: From Consensus to Personalized Decision Making. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI). 280--286.
[16]
T. Lu and C. Boutilier. 2011. Robust Approximation and Incremental Elicitation in Voting Protocols. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI). 287--293.
[17]
C. L. Mallows. 1957. Non-null ranking models. Biometrika 44 (1957), 114--130.
[18]
A. Mao, A. D. Procaccia, and Y. Chen. 2013. Better Human Computation through Principled Voting. In Proceedings of the 27th AAAI Conference on Artificial Intelligence (AAAI). 1142--1148.
[19]
B. L. Monroe. 1995. Fully Proportional Representation. American Political Science Review 89, 4 (1995), 925--940.
[20]
G. Pritchard and M. Wilson. 2009. Asymptotics of the minimum manipulating coalition size for positional voting rules under impartial culture behaviour. Mathematical Social Sciences 58, 1 (2009), 35--57.
[21]
A. D. Procaccia. 2016. Science Can Restore America's Faith in Democracy. (2016). https://www.wired.com/2016/12/science-can-restore-americas-faith-democracy/
[22]
A. D. Procaccia, S. J. Reddi, and N. Shah. 2012. A Maximum Likelihood Approach For Selecting Sets of Alternatives. In Proceedings of the 28th Annual Conference on Uncertainty in Artificial Intelligence (UAI). 695--704.
[23]
A. D. Procaccia, J. S. Rosenschein, and A. Zohar. 2008. On the Complexity of Achieveing Proportional Representation. Social Choice and Welfare 30, 3 (2008), 353--362.
[24]
A. D. Procaccia, N. Shah, and Y. Zick. 2016. Voting rules as error-correcting codes. Artificial Intelligence 231 (2016), 1--16.
[25]
I. Tsetlin, M. Regenwetter, and B. Grofman. 2003. The impartial culture maximizes the probability of majority cycles. Social Choice and Welfare 21 (2003), 387--398.
[26]
L. Xia. 2016. Bayesian Estimators As Voting Rules. In Proceedings of the 32nd Annual Conference on Uncertainty in Artificial Intelligence (UAI).
[27]
L. Xia and V. Conitzer. 2011. A maximum likelihood approach towards aggregating partial orders. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI). 446--451.
[28]
L. Xia, V. Conitzer, and J. Lang. 2010. Aggregating preferences in multi-issue domains by using maximum likelihood estimators. In Proceedings of the 9th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS). 399--408.
[29]
A. C. Yao. 1977. Probabilistic Computations: Towards a Unified Measure of Complexity. In Proceedings of the 17th Symposium on Foundations of Computer Science (FOCS). 222--227.
[30]
H. P. Young. 1988. Condorcet's theory of voting. The American Political Science Review 82, 4 (1988), 1231--1244.
Index Terms
- Making Right Decisions Based on Wrong Opinions
Recommendations
Multi-decisions decision-making: In addition to wheeling and dealing, our national political bodies need a formal approach for prioritization
Individuals, corporations and governments constantly face the extremely complex problem of ordering and prioritizing their numerous decisions according to urgency and importance. They need to sequence expenditures and allocate scarce resources to ...
Weighting Individual Opinions in Group Decision Making
MDAI '07: Proceedings of the 4th international conference on Modeling Decisions for Artificial IntelligenceIn this paper we introduce a multi-stage decision making procedure where decision makers sort the alternatives by means of a fixed set of linguistic categories, each one has associated a numerical score. First we average the scores obtained by each ...
Comments
Information & Contributors
Information
Published In
June 2017
740 pages
ISBN:9781450345279
DOI:10.1145/3033274
- General Chair:
- Constantinos Daskalakis,
- Program Chairs:
- Moshe Babaioff,
- Hervé Moulin
Copyright © 2017 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 20 June 2017
Check for updates
Author Tag
Qualifiers
- Research-article
Conference
EC '17
Sponsor:
Acceptance Rates
EC '17 Paper Acceptance Rate 75 of 257 submissions, 29%;
Overall Acceptance Rate 664 of 2,389 submissions, 28%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 160Total Downloads
- Downloads (Last 12 months)5
- Downloads (Last 6 weeks)0
Reflects downloads up to 22 Sep 2024
Other Metrics
Citations
Cited By
View all- Procaccia A(2019)Axioms Should Explain SolutionsThe Future of Economic Design10.1007/978-3-030-18050-8_27(195-199)Online publication date: 16-Nov-2019
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in