Fair, Efficient, and Truthful Resource Allocation in Dynamic Environments
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.