Collective decisions under uncertainty: efficiency, ex-ante fairness, and normalization

2025-05-06 · Leo Kurata, Kensei Nakamura

This paper studies preference aggregation under uncertainty in the multi-profile framework introduced by Sprumont (2018, 2019) and characterizes a new class of aggregation rules that can address classical concerns about Harsanyi's (1955) utilitarian rules. Our class of aggregation rules, which we call relative fair aggregation rules, is grounded in three key ideas: utilitarianism, egalitarianism, and the 0--1 normalization. These rules are parameterized by a set of weights over individuals. Each ambiguous alternative is evaluated by computing the minimum weighted sum of the 0--1 normalized utility levels within that weight set. For the characterization, we propose two novel key axioms -- weak preference for mixing and restricted certainty independence -- developed using a new method of objectively randomizing outcomes even within the fully uncertain Savagean framework. Furthermore, we show that relative utilitarian aggregation rules can be identified from the above class by imposing an axiom stronger than restricted certainty independence, and that the Rawlsian maximin version can be derived by considering strong preference for mixing instead.

📄 PDF Abstract BibTeX arXiv:2505.03232

Code (0)

등록된 구현이 없습니다.

Tasks

Fairness

Methods 이 논문이 사용한 방법론

SET Dynamic Sparse Training method where weight mask is updated randomly periodically

AI and Collective Decisions: Strengthening Legitimacy and Losers' Consent

2026-04-07 · Suyash Fulay, Prerna Ravi, Emily Kubin, Shrestha Mohanty 외 arxiv

AI is increasingly used to scale collective decision-making, but far less attention has been paid to how such systems can support procedural legitimacy, particularly the conditions shaping losers' consent: whether partic…

Bayesian Optimization with Conformal Prediction Sets

2022-10-22 · Samuel Stanton, Wesley Maddox, Andrew Gordon Wilson

Bayesian optimization is a coherent, ubiquitous approach to decision-making under uncertainty, with applications including multi-arm bandits, active learning, and black-box optimization. Bayesian optimization selects dec…

Active Learning Bayesian Optimization Conformal Prediction Decision Making+4

Timing decisions as the next frontier for collective intelligence

2023-12-01 · Albert B. Kao, Shoubhik Banerjee, Fritz Francisco, Andrew M. Berdahl

The past decade has witnessed a dramatically growing interest in collective intelligence - the phenomenon of groups having an ability to make more accurate decisions than isolated individuals. However, the vast majority …

Decision Making

Agreeing to Stop: Reliable Latency-Adaptive Decision Making via Ensembles of Spiking Neural Networks

2023-10-25 · Jiechen Chen, Sangwoo Park, Osvaldo Simeone

Spiking neural networks (SNNs) are recurrent models that can leverage sparsity in input time series to efficiently carry out tasks such as classification. Additional efficiency gains can be obtained if decisions are take…

Conformal Prediction Decision Making Time Series Uncertainty Quantification+1

How robots in a large group make decisions as a whole? From biological inspiration to the design of distributed algorithms

2019-10-24 · Gabriele Valentini

Nature provides us with abundant examples of how large numbers of individuals can make decisions without the coordination of a central authority. Social insects, birds, fishes, and many other living collectives, rely on …