Modeling Peoples Voting Behavior with Poll Information

TL;DR

This paper compares various models of strategic voting, introduces a new heuristic model called AU, and demonstrates its superior predictive power using experimental data, challenging existing theoretical assumptions.

Contribution

It introduces the Attainability-Utility heuristic model, which better predicts voting behavior and is cognitively plausible compared to existing models.

Findings

AU model predicts voting behavior better than other models

AU model performs comparably to machine learning algorithms

Results challenge traditional assumptions in social choice theory

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 experiments, 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 peoples 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.