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This content will become publicly available on August 1, 2024

Title: Pandering in a (flexible) representative democracy
In representative democracies, regular election cycles are supposed to prevent misbehavior by elected officials, hold them accountable, and subject them to the “will of the people." Pandering, or dishonest preference reporting by candidates campaigning for election, undermines this democratic idea. Much of the work on Computational Social Choice to date has investigated strategic actions in only a single election. We introduce a novel formal model of pandering and examine the resilience of two voting systems, Representative Democracy (RD) and Flexible Representative Democracy (FRD), to pandering within a single election and across multiple rounds of elections. For both voting systems, our analysis centers on the types of strategies candidates employ and how voters update their views of candidates based on how the candidates have pandered in the past. We provide theoretical results on the complexity of pandering in our setting for a single election, formulate our problem for multiple cycles as a Markov Decision Process, and use reinforcement learning to study the effects of pandering by single candidates and groups of candidates over many rounds.  more » « less
Award ID(s):
2007955
NSF-PAR ID:
10480841
Author(s) / Creator(s):
; ; ; ; ;
Publisher / Repository:
Proceedings of Machine Learning Research
Date Published:
Journal Name:
Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence
Format(s):
Medium: X
Location:
Pittsburgh, PA, USA
Sponsoring Org:
National Science Foundation
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