Unravelling Expressive Delegations: Complexity and Normative Analysis

Giannis Tyrovolas, Andrei Constantinescu, Edith Elkind

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We consider binary group decision-making under a rich model of liquid democracy: agents submit ranked delegation options, where each option may be a function of multiple agents' votes; e.g., “I vote yes if a majority of my friends vote yes.” Such ballots are unravelled into a profile of direct votes by selecting one entry from each ballot so as not to introduce cyclic dependencies. We study delegation via monotonic Boolean functions, and two unravelling procedures: MINSUM, which minimises the sum of the ranks of the chosen entries, and its egalitarian counterpart, MINMAX. We provide complete computational dichotomies: MINSUM is hard to compute (and approximate) as soon as any nontrivial functions are permitted, and polynomial otherwise; for MINMAX the easiness results extend to arbitrary-arity logical ORs and ANDs taken in isolation, but not beyond. For the classic model of delegating to individual agents, we give asymptotically near-tight algorithms for carrying out the two procedures, and efficient algorithms for finding optimal unravellings with the highest vote count for a given alternative. These algorithms inspire novel tie-breaking rules for the setup of voting to change a status quo. We then introduce a new axiom, which can be viewed as a variant of the participation axiom, and use algorthmic techniques developed earlier in the paper to show that it is satisfied by MINSUM and a lexicographic refinement of MINMAX (but not MINMAX itself).

Original languageEnglish (US)
Title of host publicationTechnical Tracks 14
EditorsMichael Wooldridge, Jennifer Dy, Sriraam Natarajan
PublisherAssociation for the Advancement of Artificial Intelligence
Pages9918-9925
Number of pages8
Edition9
ISBN (Electronic)1577358872, 1577358872, 1577358872, 1577358872, 1577358872, 1577358872, 1577358872, 1577358872, 1577358872, 1577358872, 1577358872, 1577358872, 1577358872, 1577358872, 1577358872, 1577358872, 1577358872, 1577358872, 1577358872, 1577358872, 1577358872, 9781577358879, 9781577358879, 9781577358879, 9781577358879, 9781577358879, 9781577358879, 9781577358879, 9781577358879, 9781577358879, 9781577358879, 9781577358879, 9781577358879, 9781577358879, 9781577358879, 9781577358879, 9781577358879, 9781577358879, 9781577358879, 9781577358879, 9781577358879, 9781577358879
DOIs
StatePublished - Mar 25 2024
Event38th AAAI Conference on Artificial Intelligence, AAAI 2024 - Vancouver, Canada
Duration: Feb 20 2024Feb 27 2024

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
Number9
Volume38
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468

Conference

Conference38th AAAI Conference on Artificial Intelligence, AAAI 2024
Country/TerritoryCanada
CityVancouver
Period2/20/242/27/24

ASJC Scopus subject areas

  • Artificial Intelligence

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