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

通过人工智能（AI），经济学和计算的整合，该项目研究了在动态环境和缺乏可转移货币的情况下资源分配的新解决方案。随着在线平台的出现，经济学理论成为一种基本方法，可以在各种领域（如轮班安排，课程注册，云计算和众包）中促进效率，公平和真实性的理想社会属性。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、动态人群和在线项目的复杂场景需要新颖、实用和可扩展的解决方案。该项目解决了人工智能和经济学交叉领域的各种基本问题，同时丰富了对动态环境中资源分配的算法和社会理解。 这与传统机制形成鲜明对比，传统机制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。本项目研究的新技术旨在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产生深远的影响，以公平地分配资源，同时激励参与者的真实行为。具体而言，该项目研究两个相互关联的组成部分：（1）不确定性下的顺序分配，通过将人工智能中研究的模型与经济理论相结合，调查，分析和创建新的机制，这些机制是公平的，并在代理人偏好不断变化的环境中阻止战略操纵（例如护士排班和课程分配）;以及（2）在线机制，通过采用算法设计和人工智能的见解来研究当代理人随着时间或可用性到达和离开时分配机制的公平性和效率物品的数量不确定（例如粮食银行组织和众包平台）。新技术的目的是利用数学框架下的不确定性，以及公理化的算法经济学和机制设计的方法来开发动态资源分配的理论模型，并采用多智能体设计范式调查之间的关系，建立机制的公平分配问题。最终，这项研究的结果将导致在动态环境中的实际应用，需要有效的和社会可取的decisions.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.