[논문 리뷰] The (Computational) Social Choice Take on Indivisible Participatory Budgeting

Simon Rey, Schmidt, Felicia외 1명|arXiv (Cornell University)|2023. 03. 01.

Game Theory and Voting Systems인용 수 16

한 줄 요약

이 설문은 사회선택 관점에서 나눌 수 없는 참여 예산(indivisible participatory budgeting)에 대한 문헌을 검토하며 투표 형식, 집계 규칙, 공정성 개념, 공리, 알고리즘 및 확장을 상세히 다룬다.

ABSTRACT

In this survey, we review the literature investigating participatory budgeting as a social choice problem. Participatory Budgeting (PB) is a democratic process in which citizens are asked to vote on how to allocate a given amount of public money to a set of projects. 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. This problem has received substantial attention 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. The 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.

연구 동기 및 목표

• Provide a unified, formal overview of indivisible participatory budgeting (PB) in the social-choice framework.
• Categorize and compare ballot formats (cardinal, approval, ordinal) and their implications for PB.
• Survey welfare-based rules, fairness concepts, axiomatic properties, and algorithmic approaches in indivisible PB.
• Present variations and extensions of the standard PB model and connect to practice and related fields.

제안 방법

• Define the standard PB voting model I = <P, c, b> with projects, costs, and budget.
• Classify ballot formats into cardinal and ordinal, including approval, knapsack, t-approval, and cumulative ballots.
• Introduce satisfaction notions and how ballots induce utility or satisfaction for agents.
• Survey rules for outcome determination (welfare-maximising, Sequential Phragmén, Maximin, Equal Shares, etc.).
• Discuss fairness notions (Justified Representation, Core, Priceability, Proportionality) and axioms (exhaustiveness, monotonicity, strategy-proofness).
• Outline algorithmic problems and variations/extensions of the PB model (end-to-end processes, uncertainty, weights, distributional constraints).

실험 결과

연구 질문

• RQ1 What ballot formats best balance information elicitation and practicality in indivisible PB?
• RQ2 How do different PB rules perform with respect to welfare, fairness, and axioms under various ballots?
• RQ3 What notions of fairness (JR, core, priceability, proportionality) are applicable and tractable in indivisible PB?
• RQ4 What are the computational and practical implications of extending the standard PB model to richer settings and uncertainty?

주요 결과

• Approval ballots are the most widely used in practice and literature, though they communicate limited information.
• Knapsack ballots can yield low quality decisions in worst cases, affecting distortion negatively.
• Distortion analyses show varying theoretical guarantees across ballot formats, with some formats achieving worse or unbounded distortion in deterministic settings and improved bounds under randomisation.
• Empirical experiments (e.g., Fairstein, Benadè, Gal 2023) suggest k-approval ballots offer favorable time, ease of use, and expressiveness among studied formats.
• A rich set of fairness concepts (e.g., JR, core, priceability, proportionality) provide multiple lenses to evaluate PB outcomes, along with axioms (exhaustiveness, monotonicity, strategy-proofness) guiding rule design.
• The literature also covers extensive algorithmic approaches for outcome determination and welfare maximisation, as well as numerous variations and extensions beyond the standard model.]
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• table_rows: []}```structured``` doesn't apply; corrected to proper JSON. Note: Last lines include table_headers and table_rows as separate fields; ensure JSON structure matches schema. However as produced, there is a formatting error: table_headers and table_rows were appended inside key_findings array. Need to ensure proper fields at top-level. Let's fix. Proper: top-level fields: title, tldr, meta_description, objective, method, research_questions, key_findings, table_headers, table_rows. Our previous output mistakenly placed table_headers and table_rows inside the key_findings. Let's correct. Provide final. Wait we must output only once. I'll provide corrected JSON. Also ensure the array items for objective, method, research_questions, key_findings match translated content; but objective still contains English strings; I mistakenly left English. The instruction: translate ONLY natural-language text; keep numbers, equations, etc. The objective items are English; they should be translated to Korean as natural-language. Let's translate them accordingly. So objective items translations should be:
• Provide a unified...
• 범주화하고 비교하기 cardinal, approval, ordinal ...
• 기대효용에 기반한 규칙, 공정성 개념, 공리적 특성, 그리고 알고리즘적 접근 방법을 나눌 수 없는 PB에서 조사합니다.
• 표준 PB 모델의 변형과 확장을 제시하고 실무 및 관련 분야와 연결합니다.