NDLI: The maximum likelihood approach to voting on social networks

Content Provider	IEEE Xplore Digital Library
Author	Conitzer, V.
Copyright Year	2013
Description	Author affiliation: Depts. of Comput. Sci. & Econ., Duke Univ., Durham, NC, USA (Conitzer, V.)
Abstract	One view of voting is that voters have inherently different preferences - de gustibus non est disputandum - and that voting is merely a method for reaching a reasonable compromise solution. An alternative view is that some of the alternatives really are better in an objective sense, and by voting over the alternatives we hope to be more likely to reach the correct outcome. In this latter view, we can see the votes as noisy estimates of the truth. Specifying a probabilistic noise model gives us a natural “optimal” voting rule for determining the outcome based on the votes, namely, the function that takes the votes as input and produces the outcome that maximizes the likelihood of these votes as output. We will first review some of the work on the maximum likelihood approach to voting. Most of this work supposes that, conditional on the correct outcome, votes are independent. In reality, however, voters are clearly influenced by the opinions of those close to them. How should we model the effects of the social network, and what does this imply for the maximum likelihood approach? We will first review an earlier result [1] that states that, under certain assumptions, the social network structure should not affect the voting rule. We then consider a new model under which this is not true, and prove that computing the probability of the votes given the correct outcome is #P-hard under this model. On the other hand, if the goal is to simultaneously also give a point estimate of the hidden variables in the model, then the optimization problem can be solved in polynomial time.
Sponsorship	IEEE Cont. Syst. Soc.
Starting Page	1482
Ending Page	1487
File Size	649513
Page Count	6
File Format	PDF
e-ISBN	9781479934102
DOI	10.1109/Allerton.2013.6736702
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-10-02
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Maximum likelihood estimation Social network services Computational modeling Noise Polynomials Bipartite graph Noise measurement
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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