Pandering in a Flexible Representative Democracy
- URL: http://arxiv.org/abs/2211.09986v1
- Date: Fri, 18 Nov 2022 02:19:28 GMT
- Title: Pandering in a Flexible Representative Democracy
- Authors: Xiaolin Sun, Jacob Masur, Ben Abramowitz, Nicholas Mattei, Zizhan
Zheng
- Abstract summary: We examine the resilience of two democratic voting systems to pandering within a single round and across multiple rounds.
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.
- Score: 24.462078390546246
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- 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.
Related papers
- ElectionSim: Massive Population Election Simulation Powered by Large Language Model Driven Agents [70.17229548653852]
We introduce ElectionSim, an innovative election simulation framework based on large language models.
We present a million-level voter pool sampled from social media platforms to support accurate individual simulation.
We also introduce PPE, a poll-based presidential election benchmark to assess the performance of our framework under the U.S. presidential election scenario.
arXiv Detail & Related papers (2024-10-28T05:25:50Z) - From Experts to the Public: Governing Multimodal Language Models in Politically Sensitive Video Analysis [48.14390493099495]
This paper examines the governance of large language models (MM-LLMs) through individual and collective deliberation.
We conducted a two-step study: first, interviews with 10 journalists established a baseline understanding of expert video interpretation; second, 114 individuals from the general public engaged in deliberation using Inclusive.AI.
arXiv Detail & Related papers (2024-09-15T03:17:38Z) - Representation Bias in Political Sample Simulations with Large Language Models [54.48283690603358]
This study seeks to identify and quantify biases in simulating political samples with Large Language Models.
Using the GPT-3.5-Turbo model, we leverage data from the American National Election Studies, German Longitudinal Election Study, Zuobiao dataset, and China Family Panel Studies.
arXiv Detail & Related papers (2024-07-16T05:52:26Z) - Learning to Manipulate under Limited Information [44.99833362998488]
We trained over 70,000 neural networks of 26 sizes to manipulate against 8 different voting methods.
We find that some voting methods, such as Borda, are highly manipulable by networks with limited information, while others, such as Instant Runoff, are not.
arXiv Detail & Related papers (2024-01-29T18:49:50Z) - Adaptively Weighted Audits of Instant-Runoff Voting Elections: AWAIRE [61.872917066847855]
Methods for auditing instant-runoff voting (IRV) elections are either not risk-limiting or require cast vote records (CVRs), the voting system's electronic record of the votes on each ballot.
We develop an RLA method that uses adaptively weighted averages of test supermartingales to efficiently audit IRV elections when CVRs are not available.
arXiv Detail & Related papers (2023-07-20T15:55:34Z) - Diverse Representation via Computational Participatory Elections --
Lessons from a Case Study [16.699381591572166]
We have designed a novel participatory electoral process coined the Representation Pact, implemented with the support of a computational system.
That process explicitly enables voters to decide on representation criteria in a first round, and then lets them vote for candidates in a second round.
After the two rounds, a counting method is applied, which selects the committee of candidates that maximizes the number of votes received in the second round.
arXiv Detail & Related papers (2022-05-30T19:22:38Z) - Exploring Fairness in District-based Multi-party Elections under
different Voting Rules using Stochastic Simulations [0.5076419064097732]
Many democratic societies use district-based elections, where the region under consideration is geographically divided into districts and a representative is chosen for each district based on the preferences of the electors who reside there.
We show that this can lead to situations where many electors are dissatisfied with the election results, which is not desirable in a democracy.
Inspired by current literature on fairness of Machine Learning algorithms, we define measures of fairness to quantify the satisfaction of electors, irrespective of their political choices.
arXiv Detail & Related papers (2022-02-25T18:03:03Z) - DiRe Committee : Diversity and Representation Constraints in Multiwinner
Elections [0.0]
We develop a model, DiRe Committee Winner Determination (DRCWD), which delineates candidate and voter attributes to select a committee.
We develop a feasibility-based algorithm, which finds the winning DiRe committee in under two minutes on 63% of the instances of synthetic datasets and on 100% of instances of real-world datasets.
Overall, DRCWD motivates that a study of multiwinner elections should consider both its actors, namely candidates and voters, as candidate-specific "fair" models can unknowingly harm voter populations, and vice versa.
arXiv Detail & Related papers (2021-07-15T14:32:56Z) - Bribery as a Measure of Candidate Success: Complexity Results for
Approval-Based Multiwinner Rules [58.8640284079665]
We study the problem of bribery in multiwinner elections, for the case where the voters cast approval ballots (i.e., sets of candidates they approve)
We consider a number of approval-based multiwinner rules (AV, SAV, GAV, RAV, approval-based Chamberlin--Courant, and PAV)
In general, our problems tend to be easier when we limit out bribery actions on increasing the number of approvals of the candidate that we want to be in a winning committee.
arXiv Detail & Related papers (2021-04-19T08:26:40Z) - Auditing Hamiltonian Elections [24.832413743954618]
We show how to conduct risk-limiting audits for delegate allocation elections using variants of the Hamilton method.
Experiments on real-world elections show that we can audit primary elections to high confidence (small risk limits) usually at low cost.
arXiv Detail & Related papers (2021-02-17T00:20:26Z) - Modeling Voters in Multi-Winner Approval Voting [24.002910959494923]
We study voting behavior in single-winner and multi-winner approval voting scenarios with varying degrees of uncertainty.
We find that people generally manipulate their vote to obtain a better outcome, but often do not identify the optimal manipulation.
We propose a novel model that takes into account the size of the winning set and human cognitive constraints.
arXiv Detail & Related papers (2020-12-04T19:24:28Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.