Komitee Equal Shares: Choosing Together as Voters and as Groups with a Co-designed Virtual Budget Algorithm

• URL: http://arxiv.org/abs/2510.02040v1
• Date: Thu, 02 Oct 2025 14:11:59 GMT
• Title: Komitee Equal Shares: Choosing Together as Voters and as Groups with a Co-designed Virtual Budget Algorithm
• Authors: Joshua C. Yang, Noemi Scheurer,
• Abstract summary: We introduce Komitee Equal Shares, a priceable virtual-budget allocation framework.<n>It integrates two signals: in voter mode, participants cast point votes; in evaluator mode, small groups assess proposals.<n>The framework extends the Method of Equal Shares by translating both signals into virtual spending power and producing voting receipts.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Public funding processes demand fairness, learning, and outcomes that participants can understand. We introduce Komitee Equal Shares, a priceable virtual-budget allocation framework that integrates two signals: in voter mode, participants cast point votes; in evaluator mode, small groups assess proposals against collectively defined impact fields. The framework extends the Method of Equal Shares by translating both signals into virtual spending power and producing voting receipts. We deployed the framework in the 2025 Kultur Komitee in Winterthur, Switzerland. Our contributions are: (1) a clear separation of decision modes, addressing a gap in social choice that typically treats participatory budgeting as preference aggregation while citizens also see themselves as evaluators; and (2) the design of voting receipts that operationalise priceability into participant-facing explanations, making proportional allocations legible and traceable. The framework generalises to participatory grant-making and budgeting, offering a model where citizens act as voters and evaluators within one proportional, explainable allocation.

Related papers

• Fairness-Aware Reinforcement Learning (FAReL): A Framework for Transparent and Balanced Sequential Decision-Making [41.53741129864172]
  Equity in real-world sequential decision problems can be enforced using fairness-aware methods.<n>We propose a framework where multiple trade-offs can be explored.<n>We show that our framework learns policies that are more fair across multiple scenarios, with only minor loss in performance reward.
  arXiv  Detail & Related papers  (2025-09-26T11:42:14Z)
• Bridging Voting and Deliberation with Algorithms: Field Insights from vTaiwan and Kultur Komitee [1.2277343096128712]
  Democratic processes increasingly aim to integrate large-scale voting with face-to-face deliberation.<n>This work introduces new methods that use algorithms and computational tools to bridge online voting with face-to-face deliberation.
  arXiv  Detail & Related papers  (2025-02-07T15:45:13Z)
• Multi-Group Proportional Representation in Retrieval [46.00781543425424]
  We introduce Multi-Group Proportional Representation (MPR), a novel metric that measures representation across intersectional groups. MPR yields more proportional representation across multiple intersectional groups specified by a rich function class, often with minimal compromise in retrieval accuracy.
  arXiv  Detail & Related papers  (2024-07-11T14:59:17Z)
• Fair Allocation in Dynamic Mechanism Design [57.66441610380448]
  We consider a problem where an auctioneer sells an indivisible good to groups of buyers in every round, for a total of $T$ rounds. The auctioneer aims to maximize their discounted overall revenue while adhering to a fairness constraint that guarantees a minimum average allocation for each group.
  arXiv  Detail & Related papers  (2024-05-31T19:26:05Z)
• Intersectional Two-sided Fairness in Recommendation [41.96733939002468]
  We propose a novel approach called Inter-sectional Two-sided Fairness Recommendation (ITFR) Our method utilizes a sharpness-aware loss to perceive disadvantaged groups, and then uses collaborative loss balance to develop consistent distinguishing abilities for different intersectional groups. Our proposed approach effectively alleviates the intersectional two-sided unfairness and consistently outperforms previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-02-05T08:56:24Z)
• Designing Digital Voting Systems for Citizens: Achieving Fairness and Legitimacy in Participatory Budgeting [10.977733942901535]
  Participatory Budgeting (PB) has evolved into a key democratic instrument for resource allocation in cities. This work presents the results of behavioural experiments where participants were asked to vote in a fictional PB setting. We identify approaches to designing PB voting that minimise cognitive load and enhance the perceived fairness and legitimacy of the digital process.
  arXiv  Detail & Related papers  (2023-10-05T12:25:48Z)
• Fair and Inclusive Participatory Budgeting: Voter Experience with Cumulative and Quadratic Voting Interfaces [1.4730691320093603]
  Cumulative and quadratic voting are expressive, promoting fairness and inclusion. Despite these benefits, graphical voter interfaces for cumulative and quadratic voting are complex to implement and use effectively. This paper introduces an implementation and evaluation of cumulative and quadratic voting within a state-of-the-art voting platform: Stanford Participatory Budgeting.
  arXiv  Detail & Related papers  (2023-08-08T15:45:55Z)
• Consensus-based Participatory Budgeting for Legitimacy: Decision Support via Multi-agent Reinforcement Learning [3.3504365823045044]
  Participatory budgeting is a process where voting outcomes may not always be fair or inclusive. This paper introduces a novel and legitimate consensus-based participatory budgeting process. Voters are assisted to interact with each other to make viable compromises.
  arXiv  Detail & Related papers  (2023-07-24T16:16:23Z)
• 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)
• On Disentangled and Locally Fair Representations [95.6635227371479]
  We study the problem of performing classification in a manner that is fair for sensitive groups, such as race and gender. We learn a locally fair representation, such that, under the learned representation, the neighborhood of each sample is balanced in terms of the sensitive attribute.
  arXiv  Detail & Related papers  (2022-05-05T14:26:50Z)
• Towards Return Parity in Markov Decision Processes [36.96748490812215]
  We study a fairness problem in Markov decision processes (MDPs) We propose return parity, a fairness notion that requires MDPs from different demographic groups to achieve the same expected rewards. Motivated by our decomposition theorem, we propose algorithms to mitigate return disparity via learning a shared group policy with state visitation distributional alignment.
  arXiv  Detail & Related papers  (2021-11-19T23:25:38Z)
• Multi-Stage Decentralized Matching Markets: Uncertain Preferences and Strategic Behaviors [91.3755431537592]
  This article develops a framework for learning optimal strategies in real-world matching markets. We show that there exists a welfare-versus-fairness trade-off that is characterized by the uncertainty level of acceptance. We prove that participants can be better off with multi-stage matching compared to single-stage matching.
  arXiv  Detail & Related papers  (2021-02-13T19:25:52Z)

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.