English

Eliciting Social Knowledge for Creditworthiness Assessment

Computer Science and Game Theory 2021-08-23 v1 Social and Information Networks

Abstract

Access to capital is a major constraint for economic growth in the developing world. Yet those attempting to lend in this space face high defaults 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 VCG scoring mechanism for the liquidity-constrained setting. Whereas Chen et al. [2011] 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 beliefs about the reports of others in a setting with simultaneous reports.Moreover, the use of VCG methods allows for the integration of linear belief aggregation methods.

Keywords

Cite

@article{arxiv.2108.09289,
  title  = {Eliciting Social Knowledge for Creditworthiness Assessment},
  author = {Mark York and Munther Dahleh and David Parkes},
  journal= {arXiv preprint arXiv:2108.09289},
  year   = {2021}
}

Comments

17 pages, submitted to WINE

R2 v1 2026-06-24T05:17:30.971Z