Skip to main content

Iterative local voting for collective decision-making in continuous spaces

Publication ,  Journal Article
Garg, N; Kamble, V; Goel, A; Marn, D; Munagala, K
Published in: Journal of Artificial Intelligence Research
February 1, 2019

Many societal decision problems lie in high-dimensional continuous spaces not amenable to the voting techniques common for their discrete or single-dimensional counterparts. These problems are typically discretized before running an election or decided upon through negotiation by representatives. We propose a algorithm called Iterative Local Voting for collective decision-making in this setting. In this algorithm, voters are sequentially sampled and asked to modify a candidate solution within some local neighborhood of its current value, as dened by a ball in some chosen norm, with the size of the ball shrinking at a specied rate. We rst prove the convergence of this algorithm under appropriate choices of neighborhoods to Pareto optimal solutions with desirable fairness properties in certain natural settings: when the voters' utilities can be expressed in terms of some form of distance from their ideal solution, and when these utilities are additively decomposable across dimensions. In many of these cases, we obtain convergence to the societal welfare maximizing solution. We then describe an experiment in which we test our algorithm for the decision of the U.S. Federal Budget on Mechanical Turk with over 2,000 workers, employing neighborhoods dened by various L-Norm balls. We make several observations that inform future implementations of such a procedure.

Duke Scholars

Altmetric Attention Stats
Dimensions Citation Stats

Published In

Journal of Artificial Intelligence Research

DOI

ISSN

1076-9757

Publication Date

February 1, 2019

Volume

64

Start / End Page

315 / 355

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4611 Machine learning
  • 4603 Computer vision and multimedia computation
  • 4602 Artificial intelligence
  • 1702 Cognitive Sciences
  • 0801 Artificial Intelligence and Image Processing
  • 0102 Applied Mathematics
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Garg, N., Kamble, V., Goel, A., Marn, D., & Munagala, K. (2019). Iterative local voting for collective decision-making in continuous spaces. Journal of Artificial Intelligence Research, 64, 315–355. https://doi.org/10.1613/jair.1.11358
Garg, N., V. Kamble, A. Goel, D. Marn, and K. Munagala. “Iterative local voting for collective decision-making in continuous spaces.” Journal of Artificial Intelligence Research 64 (February 1, 2019): 315–55. https://doi.org/10.1613/jair.1.11358.
Garg N, Kamble V, Goel A, Marn D, Munagala K. Iterative local voting for collective decision-making in continuous spaces. Journal of Artificial Intelligence Research. 2019 Feb 1;64:315–55.
Garg, N., et al. “Iterative local voting for collective decision-making in continuous spaces.” Journal of Artificial Intelligence Research, vol. 64, Feb. 2019, pp. 315–55. Scopus, doi:10.1613/jair.1.11358.
Garg N, Kamble V, Goel A, Marn D, Munagala K. Iterative local voting for collective decision-making in continuous spaces. Journal of Artificial Intelligence Research. 2019 Feb 1;64:315–355.

Published In

Journal of Artificial Intelligence Research

DOI

ISSN

1076-9757

Publication Date

February 1, 2019

Volume

64

Start / End Page

315 / 355

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4611 Machine learning
  • 4603 Computer vision and multimedia computation
  • 4602 Artificial intelligence
  • 1702 Cognitive Sciences
  • 0801 Artificial Intelligence and Image Processing
  • 0102 Applied Mathematics