Modeling people's voting behavior with poll information

Roy Fairstein, Adam Lauz, Reshef Meir, Kobi Gal

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Despite the prevalence of voting systems in the real world there is no consensus among researchers of how people vote strategically, even in simple voting settings. This paper addresses this gap by comparing different approaches that have been used to model strategic voting, including expected utility maximization, heuristic decisionmaking, and bounded rationality models. The models are applied to data collected from hundreds of people in controlled voting ex-periments, where people vote after observing non-binding poll information. We introduce a new voting model, the Attainability-Utility (AU) heuristic, which weighs the popularity of a candidate according to the poll, with the utility of the candidate to the voter. We argue that the AU model is cognitively plausible, and show that it is able to predict people's voting behavior significantly better than other models from the literature. It was almost at par with (and sometimes better than) a machine learning algorithm that uses substantially more information. Our results provide new insights into the strategic considerations of voters, that undermine the prevalent assumptions of much theoretical work in social choice.

Original languageAmerican English
Title of host publication18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019
Pages1422-1430
Number of pages9
ISBN (Electronic)9781510892002
StatePublished - 1 Jan 2019
Event18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019 - Montreal, Canada
Duration: 13 May 201917 May 2019
https://dl.acm.org/doi/proceedings/10.5555/3306127

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume3

Conference

Conference18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019
Country/TerritoryCanada
CityMontreal
Period13/05/1917/05/19
Internet address

Keywords

  • [Agent societies and societal issues] coordination and control models for multiagent systems
  • [Economic paradigms] behavioral game theory
  • [Economic paradigms] social choice theory

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering

Fingerprint

Dive into the research topics of 'Modeling people's voting behavior with poll information'. Together they form a unique fingerprint.

Cite this