Collective Choice Lotteries

Brandt, Felix

doi:10.1007/978-3-030-18050-8_9

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…Perspectives that have been considered includes welfare maximisation (Goel, Krishnaswamy, Sakshuwong and Aitamurto, 2019;Michorzewski, Peters and Skowron, 2020), fairness guarantees (Fain, Goel and Munagala, 2016;Caragiannis, Christodoulou and Protopapas, 2022;Airiau, Aziz, Caragiannis, Kruger, Lang and Peters, 2023), strategic behaviours (Aziz, Bogomolnaia and Moulin, 2019;Freeman, Pennock, Peters and Vaughan, 2021). is se ing is also closely related to that of probabilistic social choice (Brandt, 2018).…”

Section: Divisible Participatory Budgetingmentioning

confidence: 99%

The (Computational) Social Choice Take on Indivisible Participatory Budgeting

Rey¹,

Malý²

2023

Preprint

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In this survey, we review the literature investigating participatory budgeting as a social choice problem. Participatory Budgeting (PB) is a democratic tool aiming at making budgeting decisions in a more democratic manner. Specifically, citizens are asked to vote on who to allocate a given amount of money to a set of projects that can potentially be funded. From a social choice perspective, it corresponds then to the problem of aggregating opinions about which projects should be funded, into a budget allocation satisfying a budget constraint. is problem has received substantial a ention in recent years and the literature is growing at a fast pace. In this survey, we present the most important research directions from the literature, each time presenting a large set of representative results. We only focus on the indivisible case, that is, PB problems in which projects can either be fully funded or not at all. e aim of the survey is to present a comprehensive overview of the state of the research on PB. We aim at providing both a general overview of the main research questions that are being investigated, and formal and unified definitions of the most important technical concepts from the literature.Of course a survey is never complete as the state of the research keeps changing. is document is intended to be a living document that gets updated every now and then as the literature grows. If you feel that some papers are not presented correctly, or simply missing, feel free to contact us. We will be more than happy to correct it.⋆ Laruelle (2021) considers that the agents submit weak and complete rankings over the projects, that are then converted into cardinal scores (via positional scoring functions) for the aggregation.

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Section: Divisible Participatory Budgetingmentioning

confidence: 99%

The (Computational) Social Choice Take on Indivisible Participatory Budgeting

Rey¹,

Malý²

2023

Preprint

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“…Lastly, randomization can be used to satisfy the fairness principle of equal treatment of equals (Moulin, 1991). Despite being an old research topic, the study of randomized solutions is still a hot topic where many open problems remain to be solved (Aziz, 2019;Brandt, 2019).…”

Section: Introductionmentioning

confidence: 99%

Fairness in Influence Maximization through Randomization

Becker¹,

D’Angelo²,

Ghobadi³

et al. 2022

jair

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The influence maximization paradigm has been used by researchers in various fields in order to study how information spreads in social networks. While previously the attention was mostly on efficiency, more recently fairness issues have been taken into account in this scope. In the present paper, we propose to use randomization as a mean for achieving fairness. While this general idea is not new, it has not been applied in this area. Similar to previous works like Fish et al. (WWW ’19) and Tsang et al. (IJCAI ’19), we study the maximin criterion for (group) fairness. In contrast to their work however, we model the problem in such a way that, when choosing the seed sets, probabilistic strategies are possible rather than only deterministic ones. We introduce two different variants of this probabilistic problem, one that entails probabilistic strategies over nodes (node-based problem) and a second one that entails probabilistic strategies over sets of nodes (set-based problem). After analyzing the relation between the two probabilistic problems, we show that, while the original deterministic maximin problem was inapproximable, both probabilistic variants permit approximation algorithms that achieve a constant multiplicative factor of 1 − 1/e minus an additive arbitrarily small error that is due to the simulation of the information spread. For the node-based problem, the approximation is achieved by observing that a polynomial-sized linear program approximates the problem well. For the set-based problem, we show that a multiplicative-weight routine can yield the approximation result. For an experimental study, we provide implementations of multiplicative-weight routines for both the set-based and the node-based problems and compare the achieved fairness values to existing methods. Maybe non-surprisingly, we show that the ex-ante values, i.e., minimum expected value of an individual (or group) to obtain the information, of the computed probabilistic strategies are significantly larger than the (ex-post) fairness values of previous methods. This indicates that studying fairness via randomization is a worthwhile path to follow. Interestingly and maybe more surprisingly, we observe that even the ex-post fairness values, i.e., fairness values of sets sampled according to the probabilistic strategies computed by our routines, dominate over the fairness achieved by previous methods on many of the instances tested.

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“…Lastly, randomization can be used to satisfy the fairness principle of equal treatment of equals (Moulin 1991). Despite being an old research topic, the study of randomized solutions is still a hot topic with many open problems (Aziz 2019;Brandt 2019).…”

Section: Introductionmentioning

confidence: 99%

Fairness in Influence Maximization through Randomization

Becker

D’Angelo

Ghobadi

et al. 2021

AAAI

View full text Add to dashboard Cite

The influence maximization paradigm has been used by researchers in various fields in order to study how information spreads in social networks. While previously the attention was mostly on efficiency, more recently fairness issues have been taken into account in this scope. In the present paper, we propose to use randomization as a mean for achieving fairness. While this general idea is not new, it has not been applied in the area of information spread in networks. Similar to previous works like Fish et al. (WWW '19) and Tsang et al. (IJCAI '19), we study the maximin criterion for (group) fairness. By allowing randomized solutions, we introduce two different variants of this problem. While the original deterministic maximin problem has been shown to be inapproximable, interestingly, we show that both probabilistic variants permit approximation algorithms with a constant multiplicative factor of 1-1/e plus an additive arbitrarily small error that is due to the simulation of the information spread. For an experimental study, we provide implementations of our methods and compare the achieved fairness values to existing methods. Non-surprisingly, the ex-ante values, i.e., minimum expected value of an individual (or group) to obtain the information, of the computed probabilistic strategies are significantly larger than the (ex-post) fairness values of previous methods. This confirms that studying fairness via randomization is a worthwhile direction. More surprisingly, we observe that even the ex-post fairness values, i.e., fairness values of sets sampled according to the probabilistic strategies, computed by our routines dominate over the fairness achieved by previous methods on most of the instances tested.

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Collective Choice Lotteries

Cited by 6 publications

References 22 publications

The (Computational) Social Choice Take on Indivisible Participatory Budgeting

The (Computational) Social Choice Take on Indivisible Participatory Budgeting

Fairness in Influence Maximization through Randomization

Fairness in Influence Maximization through Randomization

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