CAREER: Incentives, Fairness, and Efficiency without Monetary Transfers

职业：激励、公平和效率，无需货币转移

基本信息

批准号：
2144208
负责人：
Christos Psomas
金额：
$ 59.14万
依托单位：
Purdue University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-07-01 至 2027-06-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2144208&HistoricalAwards=false
关键词：
CAREER Incentives Fairness Efficiency without

项目摘要

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).The Internet and the vast increase in the availability of data have transformed algorithm design, as well as computer science in general. From computational resources and advertising space, to food donations, loans, kidneys, and vaccines, algorithms are increasingly being used to decide how scarce resources are allocated. As opposed to traditional optimization, the input to these algorithms must be solicited from strategic agents, with their own, private preferences over the algorithm's output. And, from resource allocation in the cloud and spectrum auctions to tournaments in major sporting events (such as the Olympics), it is well-understood that these strategic entities will behave to optimize their own benefit to the extent possible while still “following the rules,” leading to unpredictable final outcomes. At the same time, unlike traditional optimization, in many of these modern applications, system designers must also consider whether their system is equitable among its participants. Classic work in Economics, as well as extensive work in the intersection of Computer Science and Economics, provides a rich toolkit for designing algorithms that are immune to strategic manipulations as well as algorithms that balance fairness and efficiency. This project aims to advance and develop this theory with a focus on domains where monetary transfers are not allowed, by taking aim at several fundamental open questions. The project also contains plans to design, develop, and deploy a system that is based on the proposed theoretical research and that serves the local community by enabling local non-profit organizations that fight hunger to allocate their food donations in a more efficient manner.The project will expand the reach of theory into areas where there is a major gap in current understanding, by taking aim at several key theoretical questions in the following three complementary thrusts. (1) Foundations of mechanism design without money. The project takes aim at fundamental questions in this space, with the goal of developing tools for designing truthful mechanisms for a number of paradigms: divisible and indivisible goods, static and dynamic environments, worst-case and Bayesian objectives. (2) Mechanism design with imperfect rationality. There are important domains in which protecting against fully rational, expected utility-maximizing participants is overly cautious. This project puts forward and explores several possibilities for modeling imperfect rationality. (3) Mechanism design with imperfect expressivity. Eliciting complex utility functions is often infeasible, e.g., because of agents' cognitive limitations. The project explores the trade-off between expressiveness and ease of elicitation in mechanism design without money.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项是根据2021年《美国救援计划法》（公法117-2）全部或部分资助的。互联网和数据可用性的巨大增加改变了算法设计以及一般计算机科学。从计算资源和广告空间，到食品捐赠，贷款，肾脏和疫苗，算法越来越多地用于决定分配稀缺资源。与传统优化相反，必须从战略代理中巩固这些算法的输入，并具有对算法的输出的私人偏好。而且，从云和光谱拍卖中的资源分配到大型体育赛事的锦标赛（例如奥运会），就可以很好地理解这些战略实体将在可能“遵守规则”的同时，在可能的程度上优化自己的利益，从而导致不可预测的最终结果。与传统优化不同的是，在许多现代应用中，系统设计师还必须考虑其参与者中的系统是否公平。经济学的经典作品以及计算机科学与经济学交集的广泛工作，为设计算法提供了丰富的工具包，这些算法不受战略操纵的影响以及平衡公平和效率的算法。该项目旨在通过针对几个基本的开放问题来推进和发展这一理论，重点关注不允许货币转移的领域。该项目还包含计划，设计，开发和部署一个基于拟议的理论研究的系统，并通过启用当地的非营利组织来为当地社区提供服务，这些组织能够以更有效的方式将渴望分配其食品捐赠来分配其食物捐赠。通过将理论覆盖范围扩大到在当前的理解方面的主要差距，以在几个关键的问题上涉及三个关键的理论，以下三个完整的理论都在完成三个完整的问题。（1）机制设计的基础无钱。该项目针对该领域的基本问题，其目的是开发用于设计多种范式的真正机制的工具：可划分和单个商品，静态和动态环境，最差的案例和贝叶斯目标。（2）理性的机理设计。在某些重要领域中，防止完全理性的预期公用事业最大化参与者是过于灾难性的。该项目提出并探讨了建模不完美理性的几种可能性。（3）具有不完美表达的机理设计。引起复杂的效用功能通常是不可行的，例如，由于代理人的认知局限性。该项目探讨了在没有金钱的机制设计中表现力和易于启发之间的权衡。该奖项反映了NSF的法定使命，并通过使用基金会的知识分子优点和更广泛的审查标准评估来诚实地获得支持。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Leakage Inversion: Towards Quantifying Privacy in Searchable Encryption

泄漏反演：量化可搜索加密中的隐私

DOI：
10.1145/3548606.3560593
发表时间：
2022
期刊：
SIGSAC Conference on Computer and Communications Security
影响因子：
0
作者：
Kornaropoulos, Evgenios M.;Moyer, Nathaniel;Papamanthou, Charalampos;Psomas, Alexandros
通讯作者：
Psomas, Alexandros

On Hiring Secretaries with Stochastic Departures

论雇用随机离职的秘书

DOI：
10.1287/opre.2023.2476
发表时间：
2023
期刊：
Operations Research
影响因子：
2.7
作者：
Kesselheim, Thomas;Psomas, Alexandros;Vardi, Shai
通讯作者：
Vardi, Shai

Fair and Efficient Allocations Without Obvious Manipulations

公平高效的分配，没有明显的操纵

DOI：
发表时间：
2022
期刊：
Advances in neural information processing systems
影响因子：
0
作者：
Psomas, Alexandros;Verma, Paritosh
通讯作者：
Verma, Paritosh

Dynamic Fair Division with Partial Information

具有部分信息的动态公平划分

DOI：
发表时间：
2022
期刊：
Advances in neural information processing systems
影响因子：
0
作者：
Benade, Gerdus;Halpern, Daniel;Psomas, Alexandros
通讯作者：
Psomas, Alexandros

Simple Mechanisms for Welfare Maximization in Rich Advertising Auctions

丰富的广告拍卖中实现福利最大化的简单机制

DOI：
发表时间：
2022
期刊：
Advances in neural information processing systems
影响因子：
0
作者：
Aggarwal, Gagan;Bhawalkar, Kshipra;Mehta, Aranyak;Mohan, Divyarthi;Psomas, Alexandros
通讯作者：
Psomas, Alexandros