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Sequential Deliberation for Social Choice

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Web and Internet Economics (WINE 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10660))

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Abstract

Social choice is a normative study of designing protocols for collective decision making. However, in instances where the underlying decision space is too large or complex for ordinal voting, standard voting methods may be impractical. How then can we design a protocol - preferably decentralized, simple, scalable, and not requiring any special knowledge of the decision space - to reach consensus? We propose sequential deliberation as a natural solution to this problem. In this iterative method, successive pairs of agents bargain over the decision space using the previous decision as a disagreement alternative. We show that sequential deliberation finds a 1.208-approximation to the optimal social cost when the space of preferences define a median graph, coming very close to this value with only a small constant number of agents sampled from the population. We also give lower bounds on simpler classes of mechanisms to justify our design choices. We further show that sequential deliberation is ex-post Pareto efficient and has truthful reporting as an equilibrium of the induced extensive form game. Finally, we prove that for general metric spaces, the first and second moment of the distribution of social cost of the outcomes produced by sequential deliberation are also bounded by constants.

B. Fain—Supported by NSF grants CCF-1637397 and IIS-1447554.

A. Goel—Supported by the Army Research Office Grant No. 116388, the Office of Naval Research Grant No. 11904718, by NSF grant CCF-1637418, and by the Stanford Cyber Initiative.

K. Munagala—Supported by NSF grants CCF-1408784, CCF-1637397, and IIS-1447554.

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Notes

  1. 1.

    See also recent work by [38] that considers minimizing the variance of randomized truthful mechanisms.

  2. 2.

    The motivation for considering Squared-Distortion instead of the standard deviation is that the latter might prefer a more deterministic mechanism with a worse social cost, a problem that the Squared-Distortion avoids.

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Authors and Affiliations

  1. Computer Science Department, Duke University, Durham, NC, USA

    Brandon Fain & Kamesh Munagala

  2. Management Science and Engineering Department, Stanford University, Stanford, CA, USA

    Ashish Goel & Sukolsak Sakshuwong

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  1. Brandon Fain
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  2. Ashish Goel
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Correspondence to Brandon Fain .

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  1. Microsoft Research, Redmond, Washington, USA

    Nikhil R. Devanur

  2. Shanghai University of Finance and Economics, Shanghai, China

    Pinyan Lu

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Fain, B., Goel, A., Munagala, K., Sakshuwong, S. (2017). Sequential Deliberation for Social Choice. In: R. Devanur, N., Lu, P. (eds) Web and Internet Economics. WINE 2017. Lecture Notes in Computer Science(), vol 10660. Springer, Cham. https://doi.org/10.1007/978-3-319-71924-5_13

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  • DOI: https://doi.org/10.1007/978-3-319-71924-5_13

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