Bandits in Matching Markets: Ideas and Proposals for Peer Lending

TL;DR

This paper models peer-to-peer lending as a matching market, proposing a sequential decision-making framework that accounts for preferences and uncertainty, and demonstrates its effectiveness through simulated experiments.

Contribution

It introduces a novel market design framework for P2P lending using matching market principles and sequential decision-making techniques.

Findings

Lender regret depends on initial preferences.

Optimal matching influences regret dynamics.

Sequential decisions improve market efficiency.

Abstract

Motivated by recent applications of sequential decision making in matching markets, in this paper we attempt at formulating and abstracting market designs for P2P lending. We describe a paradigm to set the stage for how peer to peer investments can be conceived from a matching market perspective, especially when both borrower and lender preferences are respected. We model these specialized markets as an optimization problem and consider different utilities for agents on both sides of the market while also understanding the impact of equitable allocations to borrowers. We devise a technique based on sequential decision making that allow the lenders to adjust their choices based on the dynamics of uncertainty from competition over time and that also impacts the rewards in return for their investments. Using simulated experiments we show the dynamics of the regret based on the optimal borrower-lender matching and find that the lender regret depends on the initial preferences set by the lenders which could affect their learning over decision making steps.