CRII: RI: Fair, Efficient, and Truthful Resource Allocation in Dynamic Environments

通过人工智能(AI)、经济学和计算的集成，该项目研究了在动态环境和缺乏可转移货币的情况下的资源分配的新解决方案。随着在线平台的出现，经济理论成为在诸如排班、课程注册、云计算和众包等各种领域促进效率、公平和真实等理想社会属性的基本方法。这些新的应用超出了经典经济理论和市场设计的范围。涉及不断变化的偏好、动态人口和在线项目的复杂场景需要新颖、实用且可扩展的解决方案。这个项目解决了人工智能和经济学交叉点上的各种基本问题，同时丰富了动态环境下资源分配的算法和社会理解。这与传统机制形成对比，后者要么只关注静态和离线环境中资源分配的经济方面，要么忽视公平等社会方面。本项目中研究的新技术寻求扩展资源分配的算法方面，并探索动态公平分配的可行性限制。这方面的进展可以对设计有效的机制以公平分配资源产生深远影响，同时激励参与者之间的诚实行为。具体地说，该项目研究了两个相互关联的组成部分：(1)不确定情况下的顺序分配，通过将人工智能中研究的模型与经济理论相结合来调查、分析和创建新的机制，这些新机制在代理偏好不断变化的环境中是公平的，并阻止战略操纵(例如护士调度和课程分配)；以及(2)在线机制，通过使用算法设计和人工智能的见解来研究当代理随着时间的推移到达和离开或物品的可用性不确定时分配机制的公平和效率(例如食物银行组织和众包平台)。这些新技术旨在利用不确定性决策的数学框架以及算法经济学和机制设计的公理方法来建立动态资源分配的理论模型，并使用多智能体设计范式来研究公平分配问题中成熟机制之间的关系。最终，这项研究的结果将导致在需要高效和社会合意的决策的动态环境中为实际应用开发强大的系统。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Through the integration of artificial intelligence (AI), economics, and computation this project investigates novel solutions for resource allocation in dynamic environments and situations that lack transferable currency. With the advent of online platforms, economic theory emerges as a fundamental approach to promote desirable social properties of efficiency, fairness, and truthfulness in a variety of domains such as shift scheduling, course registration, cloud computing, and crowdsourcing. These new applications are beyond the scope of classical economic theory and market design. Complex scenarios involving changing preferences, dynamic populations, and online items require novel, practical, and scalable solutions. This project tackles a variety of fundamental problems at the intersection AI and economics while enriching the algorithmic and societal understanding of resource allocation in dynamic settings. This contrasts with classical mechanisms that either focus solely on economic aspects of resource allocation in static and offline settings or disregarded social aspects such as fairness. New techniques investigated in this project seek to expand the algorithmic aspects of resource allocation and explore the limits of feasibility for dynamic fair allocation. Advances here can have profound impact in designing efficient mechanisms to allocate resources fairly while incentivizing truthful behavior among the participants. Specifically, the project studies two interconnected components: (1) sequential allocation under uncertainty, by synthesizing models studied in AI with economic theory to investigate, analyze, and create new mechanisms that are fair and discourage strategic manipulation in environments where agents' preferences are evolving (e.g. nurse scheduling and course allocation); and (2) online mechanisms, by employing insights from algorithm design and AI to study fairness and efficiency of allocation mechanisms when agents arrive and depart over time or the availability of items is uncertain (e.g. food bank organizations and crowdsourcing platforms). The new techniques aim to exploit the mathematical framework of decision making under uncertainty as well as axiomatic approaches of algorithmic economic and mechanism design to develop theoretical models for dynamic resource allocation, and employ the multiagent design paradigm to investigate the relation between the well-established mechanisms in fair allocation problems. Ultimately, the findings of this research will lead to the development of robust systems for practical applications in dynamic settings that demand efficient and socially desirable decisions.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.