Collaborative Research: Distributed Mechanism Design with Learning Guarantees: Resource Allocation Among Networked Strategic Agents

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During the last decades there has been significant research in understanding how strategic agents in communication, transportation, energy, economic, societal networks make decisions in the presence of uncertainty about other agents' preferences. This research is motivated by a multitude of applications in our ever-connected society and economy, and the realization that assumptions such as fully-informed or fully-compliant agents are untenable in vastly decentralized networks. The design of incentives for resource allocation problems in the presence of strategic agents falls under the research area of mechanism design (MD). There are two issues with the current state of the art in MD. First, it assumes the existence of a central entity accepting bids and being capable of communicating with each agent. Second, it does not adequately address the question of how agents converge to the designed equilibria. Intellectual Merit: Our overarching objective in this proposal is to create a new subfield of research that addresses these issues in a unified framework. We utilize this framework to design mechanisms that are distributed and have learning (i.e., convergence) guarantees for a sufficiently broad range of agents' behaviors. To achieve our objective, we plan to proceed along a path that blends in fundamental research with targeted applications. In particular, we first investigate the design of distributed mechanisms. We consider two illustrative applications, namely, rate allocation in unicast/multicast-multirate networks and demand management of energy communities. We then investigate distributed mechanisms that incorporate learning guarantees, by which the community reaches an equilibrium. Finally, we study the above two problems in the context of non-Bayesian agents with ``no-regret'' type limited rationality.Broader Impacts: Besides the two illustrative applications, research to be carried in this project will benefit a broad range of practical fields of large societal impact. Examples include smart energy and infrastructure systems, communication systems, cyber physical and human systems, social and economical systems, to name a few. The research outcomes will be utilized for curriculum development, for training undergraduate and graduate students, and for various outreach activities at the PIs’ institutions.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.