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BatchVote: Voting Rules Designed for Auditability

  • Ronald L. RivestEmail author
  • Philip B. Stark
  • Zara Perumal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10323)

Abstract

We propose a family of novel social choice functions. Our goal is to explore social choice functions for which ease of auditing is a primary design goal, instead of being ignored or left as a puzzle to solve later.

Our proposal, “BatchVote,” creates a social choice function f from an arbitrary “inner” social choice function g, such as instant-runoff voting (IRV), and an integer B, the number of batches.

We aim to preserve flexibility by allowing g to be arbitrary, while providing the ease of auditing of a plurality election.

To compute the winner of an election of n votes, the social choice function f partitions the votes into B batches of roughly the same size, pseudorandomly. The social choice function g is applied to each batch. The election winner, according to f, is the weighted plurality winner for the B outcomes, where the weight of each batch is the number of votes it contains. The social choice function f may be viewed as an “interpolation” between plurality (which is easily auditable) and g (which need not be).

Auditing is simple by design: we can view f as being a (weighted) plurality election by Bsupervoters,” where the bth supervoter’s vote is determined by applying g to the votes in batch b, and the weight of her vote is the number of votes in her batch. Since plurality elections are easy to audit, the election output can be audited by checking a random sample of “supervotes” against the corresponding paper records.

Notes

Acknowledgments

Ronald L. Rivest gratefully acknowledges support for his work on this project received from the Center for Science of Information (CSoI), an NSF Science and Technology Center, under grant agreement CCF-0939370, and from the Department of Statistics, University of California, Berkeley, which hosted his sabbatical visit when this work began.

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Copyright information

© International Financial Cryptography Association 2017

Authors and Affiliations

  • Ronald L. Rivest
    • 1
    Email author
  • Philip B. Stark
    • 2
  • Zara Perumal
    • 3
  1. 1.MIT CSAILCambridgeUSA
  2. 2.Department of StatisticsUniversity of California BerkeleyBerkeleyUSA
  3. 3.MITCambridgeUSA

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