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Eliciting Social Knowledge for Creditworthiness Assessment

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 13112)

Abstract

Access to capital is a major constraint for economic growth in the developing world. Yet lenders often face high default rates due to their inability to distinguish creditworthy borrowers from the rest. In this paper, we propose two novel scoring mechanisms that incentivize community members to truthfully report their signal on the creditworthiness of others in their community. We first design a truncated asymmetric scoring rule for a setting where the lender has no liquidity constraints. We then derive a novel, strictly-proper Vickrey-Clarke-Groves (VCG) scoring mechanism for the liquidity-constrained setting. Whereas Chen et al. [7] give an impossibility result for an analogous setting in which sequential reports are made in the context of decision markets, we achieve a positive result through appeal to interim uncertainty about the reports of others. Additionally, linear belief aggregation methods integrate nicely with the VCG scoring mechanism that we develop.

Keywords

  • Information elicitation
  • Scoring rules
  • Mechanism design

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Fig. 1.

Notes

  1. 1.

    An initial deployment of the scheme, conducted under Harvard University’s IRB, is underway in Uganda with 100 agricultural borrowers, thanks to a partnership with Makere University.

  2. 2.

    The first concept of IR is ex post with respect to the reports of others. For this reason, we adopt the phrasing strong ex post IR here, since this holds once outcomes are observed.

  3. 3.

    In particular, our payment function in Eq. 18 does not involve a \(1/w_i\) term as in weighted VCG mechanisms [18]. We discuss this further in the extended version [29].

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Acknowledgements

This project was funded in part through the generous support of the OCP Group in Casablanca, Morocco and the Global Challenges in Economics and Computation Grant. The authors would like to thank Yiling Chen, Ariel Procaccia, Pablo Ducru, and Mutembesa Daniel for their detailed feedback.

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York, M., Dahleh, M., Parkes, D.C. (2022). Eliciting Social Knowledge for Creditworthiness Assessment. In: Feldman, M., Fu, H., Talgam-Cohen, I. (eds) Web and Internet Economics. WINE 2021. Lecture Notes in Computer Science(), vol 13112. Springer, Cham. https://doi.org/10.1007/978-3-030-94676-0_24

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  • DOI: https://doi.org/10.1007/978-3-030-94676-0_24

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