An Enriched Model of Strategic Voting under Uncertainty

2026-05-15

AI Summary

This work addresses the limited expressiveness of existing strategic voting models under preference uncertainty. It introduces a probabilistic set representation to capture such uncertainty and integrates lower and upper expected utilities into strategic decision-making. Building upon belief functions, the paper develops a unified modeling framework that subsumes current approaches based on probabilities, sets, and incomplete preferences. This framework extends classical convergence results to a broader class of uncertainty representations. The proposed model substantially enhances expressive power and real-world applicability while uncovering novel theoretical challenges inherent in reasoning under generalized uncertainty.

Abstract

We present a new strategic voting model where we use uncertainty representation to model preferences. Specifically, we use probability sets as uncertainty representations, together with lower and upper expected utility gains to take strategic decisions. Focusing on belief functions in particular, we demonstrate that this very expressive model includes in one sweep many existing models based on probabilities, sets or incomplete preferences. Additionally, we generalize several well-known convergence results from the literature to this broader representational setting. Furthermore, we illustrate how this model can capture more realistic scenarios for practical applications but also raises theoretical challenges.

Problem

• strategic voting
• uncertainty representation
• preference modeling
• belief functions
• expected utility

Innovation

• strategic voting
• uncertainty representation
• belief functions
• expected utility
• convergence results