Related papers: As Time Goes By: Adding a Temporal Dimension Towards Resolving Delegations in Liquid Democracy

As Time Goes By: Adding a Temporal Dimension Towards Resolving Delegations in Liquid Democracy

URL: http://arxiv.org/abs/2307.12898v1
Date: Mon, 24 Jul 2023 15:46:45 GMT
Title: As Time Goes By: Adding a Temporal Dimension Towards Resolving Delegations in Liquid Democracy
Authors: Evangelos Markakis and Georgios Papasotiropoulos
Abstract summary: Our work takes a first step to integrate a time horizon into decision-making problems in Liquid Democracy systems. Our approach, via a computational complexity analysis, exploits concepts and tools from temporal graph theory.
Score: 16.219158909792256
License: http://creativecommons.org/licenses/by/4.0/
Abstract: In recent years, the study of various models and questions related to Liquid Democracy has been of growing interest among the community of Computational Social Choice. A concern that has been raised, is that current academic literature focuses solely on static inputs, concealing a key characteristic of Liquid Democracy: the right for a voter to change her mind as time goes by, regarding her options of whether to vote herself or delegate her vote to other participants, till the final voting deadline. In real life, a period of extended deliberation preceding the election-day motivates voters to adapt their behaviour over time, either based on observations of the remaining electorate or on information acquired for the topic at hand. By adding a temporal dimension to Liquid Democracy, such adaptations can increase the number of possible delegation paths and reduce the loss of votes due to delegation cycles or delegating paths towards abstaining agents, ultimately enhancing participation. Our work takes a first step to integrate a time horizon into decision-making problems in Liquid Democracy systems. Our approach, via a computational complexity analysis, exploits concepts and tools from temporal graph theory which turn out to be convenient for our framework.

Related papers

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)
Generative AI Voting: Fair Collective Choice is Resilient to LLM Biases and Inconsistencies [21.444936180683147]
We show for the first time in real-world a proportional representation of voters in direct democracy. We also show that fair ballot aggregation methods, such as equal shares, prove to be a win-win: fairer voting outcomes for humans with fairer AI representation.
arXiv Detail & Related papers (2024-05-31T01:41:48Z)
Temporal Fairness in Multiwinner Voting [28.930682052949017]
Multiwinner voting captures a wide variety of settings, from parliamentary elections in democratic systems to product placement in online shopping platforms. There is a large body of work dealing with axiomatic characterizations, computational complexity, and algorithmic analysis of multiwinner voting rules. We propose a unified framework for studying temporal fairness in this domain, drawing connections with various existing bodies of work, and consolidating them within a general framework.
arXiv Detail & Related papers (2023-12-07T16:38:32Z)
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)
Measuring a Priori Voting Power -- Taking Delegations Seriously [8.12790806321461]
We introduce new power indices to measure the a priori voting power of voters in liquid democracy elections where an underlying network restricts delegations. We show that computing the criticality of a voter is #P-hard even when voting weights are asly-bounded in the size of the instance. We highlight their theoretical properties and provide numerical results to illustrate how restricting the possible delegations can alter voters' voting power.
arXiv Detail & Related papers (2023-01-06T11:16:57Z)
Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies [79.60322329952453]
We show how to develop interpretable representations of how agents make decisions. By understanding the decision-making processes underlying a set of observed trajectories, we cast the policy inference problem as the inverse to this online learning problem. We introduce a practical algorithm for retrospectively estimating such perceived effects, alongside the process through which agents update them. Through application to the analysis of UNOS organ donation acceptance decisions, we demonstrate that our approach can bring valuable insights into the factors that govern decision processes and how they change over time.
arXiv Detail & Related papers (2022-03-14T17:40:42Z)
Addressing the Long-term Impact of ML Decisions via Policy Regret [49.92903850297013]
We study a setting in which the reward from each arm evolves every time the decision-maker pulls that arm. We argue that an acceptable sequential allocation of opportunities must take an arm's potential for growth into account. We present an algorithm with provably sub-linear policy regret for sufficiently long time horizons.
arXiv Detail & Related papers (2021-06-02T17:38:10Z)
On Parameterized Complexity of Liquid Democracy [5.897728689802829]
In liquid democracy, each voter either votes herself or delegates her vote to some other voter. To decide the voters who eventually votes along with the subset of voters whose votes they give, we need to resolve the cycles in the delegation graph. Putting a cap on the number of voters whose votes a voter gives enable the system designer restrict the power of any individual voter.
arXiv Detail & Related papers (2020-11-28T18:48:22Z)
Electoral Forecasting Using a Novel Temporal Attenuation Model: Predicting the US Presidential Elections [91.3755431537592]
We develop a novel macro-scale temporal attenuation (TA) model, which uses pre-election poll data to improve forecasting accuracy. Our hypothesis is that the timing of publicizing opinion polls plays a significant role in how opinion oscillates, especially right before elections. We present two different implementations of the TA model, which accumulate an average forecasting error of 2.8-3.28 points over the 48-year period.
arXiv Detail & Related papers (2020-04-30T09:21:52Z)
A Liquid Perspective on Democratic Choice [1.3833241949666322]
The idea of liquid democracy responds to a widely-felt desire to make democracy more "fluid" and continuously participatory. This paper develops and explores the "liquid" notion and what it might mean for purposes of enhancing voter choice. The goal of this paper is to disentangle and further develop some of the many concepts and goals that liquid democracy ideas often embody.
arXiv Detail & Related papers (2020-03-26T09:43:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.