A market clearing solution for social lending

  • Authors:
  • Ning Chen;Arpita Ghosh

  • Affiliations:
  • Division of Mathematical Sciences, Nanyang Technological University, Singapore;Yahoo! Research, Santa Clara, CA

  • Venue:
  • IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
  • Year:
  • 2011

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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 with indifferences, 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.