Proceedings

A Market Clearing Solution for Social Lending

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Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 152-156.

https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-037

Abstract

The social lending market, with over a billion dollars in loans, is a two-sided matching market where borrowers specify demands and lenders specify total budgets and their desired interest rates from each acceptable borrower. Because different borrowers correspond to different risk-return profiles, lenders have preferences over acceptable borrowers; a borrower prefers lenders in order of the interest rates they offer to her. We investigate the question of what is a computationally feasible, 'good', allocation to clear this market. We design a strongly polynomial time algorithm for computing a Pareto-efficient stable outcome in a two-sided many-to-many matching market within differences, and use this to compute an allocation for the social lending market that satisfies the properties of stability — a standard notion of fairness in two-sided matching markets — and Pareto efficiency; and additionally addresses envy-freeness amongst similar borrowers and risk diversification for lenders.

Ning Chen, and Arpita Ghosh. 2011. A Market Clearing Solution for Social Lending. In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11. IJCAI, 152–156. https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-037
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