Small-Scale Markets for Bilateral Resource Trading in the Sharing Economy

TL;DR

This paper models small-scale agent-to-agent resource markets using a Mean Field Game approach, demonstrating near-perfect trade ratios and benefits for participants through simulations and case studies.

Contribution

It introduces a novel Mean Field Game framework for small-scale resource markets, proving equilibrium existence and achieving high trade efficiency.

Findings

Almost 100% trade ratio achieved in simulations

Market benefits both individuals and the system

Validated through agent computing and photovoltaic case studies

Abstract

We consider a general small-scale market for agent-to-agent resource sharing, in which each agent could either be a server (seller) or a client (buyer) in each time period. In every time period, a server has a certain amount of resources that any client could consume, and randomly gets matched with a client. Our target is to maximize the resource utilization in such an agent-to-agent market, where the agents are strategic. During each transaction, the server gets money and the client gets resources. Hence, trade ratio maximization implies efficiency maximization of our system. We model the proposed market system through a Mean Field Game approach and prove the existence of the Mean Field Equilibrium, which can achieve an almost 100% trade ratio. Finally, we carry out a simulation study motivated by an agent-to-agent computing market, and a case study on a proposed photovoltaic market, and show the designed market benefits both individuals and the system as a whole.