An Enriched Model of Strategic Voting under Uncertainty

TL;DR

This paper introduces a novel strategic voting model using probability sets and belief functions to better represent uncertainty, unifying and extending existing models with broader theoretical and practical implications.

Contribution

It presents a comprehensive model that incorporates uncertainty representations like belief functions, unifying various existing models and extending convergence results.

Findings

Model includes many existing probability and set-based models

Generalizes convergence results to broader uncertainty representations

Captures more realistic scenarios for practical applications

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.