Pandering in a Flexible Representative Democracy

TL;DR

This paper introduces a formal model of pandering in representative democracies, analyzing its impact on voting systems over multiple election cycles using theoretical and reinforcement learning methods.

Contribution

It presents a novel formal model of pandering, compares the resilience of RD and FRD systems, and applies reinforcement learning to study strategic candidate behavior across rounds.

Findings

Pandering strategies vary between single and multiple election cycles.

Reinforcement learning reveals how candidates adapt their pandering over time.

The complexity of pandering is characterized for single election cycles.

Abstract

In representative democracies, the election of new representatives in regular election cycles is meant to prevent corruption and other misbehavior by elected officials and to keep them accountable in service of the ``will of the people." This democratic ideal can be undermined when candidates are dishonest when campaigning for election over these multiple cycles or rounds of voting. Much of the work on COMSOC to date has investigated strategic actions in only a single round. We introduce a novel formal model of \emph{pandering}, or strategic preference reporting by candidates seeking to be elected, and examine the resilience of two democratic voting systems to pandering within a single round and across multiple rounds. The two voting systems we compare are Representative Democracy (RD) and Flexible Representative Democracy (FRD). For each voting system, 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 cycle, formulate our problem for multiple cycles as a Markov Decision Process, and use reinforcement learning to study the effects of pandering by both single candidates and groups of candidates across a number of rounds.