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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 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.
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.
References
Agarwal, A., Mandal, D., Parkes, D.C., Shah, N.: Peer prediction with heterogeneous users. In: Proceedings of the 2017 ACM Conference on Economics and Computation, EC 2017, pp. 81–98. ACM (2017)
Allahbakhsh, M., Ignjatovic, A., Benatallah, B., Beheshti, S.-M.-R., Bertino, E., Foo, N.: Collusion detection in online rating systems. In: Ishikawa, Y., Li, J., Wang, W., Zhang, R., Zhang, W. (eds.) APWeb 2013. LNCS, vol. 7808, pp. 196–207. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37401-2_21
Alon, N., Fischer, F., Procaccia, A., Tennenholtz, M.: Sum of us: strategyproof selection from the selectors. In: Proceedings of the 13th Conference on Theoretical Aspects of Rationality and Knowledge, pp. 101–110. TARK XIII, ACM (2011)
Björkegren, D., Grissen, D.: Behavior revealed in mobile phone usage predicts credit repayment. World Bank Econ. Rev. 34(3), 618–634 (2020)
Branch.co: Branch.co homepage (2021). https://branch.co/. Accessed June 2021
Budescu, D.V., Chen, E.: Identifying expertise to extract the wisdom of crowds. Manag. Sci. 61(2), 267–280 (2015)
Chen, Y., Kash, I., Ruberry, M., Shnayder, V.: Decision markets with good incentives. In: Chen, N., Elkind, E., Koutsoupias, E. (eds.) WINE 2011. LNCS, vol. 7090, pp. 72–83. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25510-6_7
Chen, Y., Pennock, D.M.: Designing markets for prediction. AI Mag. 31(4), 42–52 (2010)
Gneiting, T., Raftery, A.E.: Strictly proper scoring rules, prediction, and estimation. J. Am. Stat. Assoc. 102(477), 359–378 (2007)
Grameen: Grameen bank homepage (2020). http://www.grameen.com/. Accessed Feb 2020
Hanson, R.D.: Logarithmic market scoring rules for modular combinatorial information aggregation. J. Prediction Markets 1(1), 3–15 (2007)
Hussam, R., Rigol, N., Roth, B.: Targeting high ability entrepreneurs using community information: mechanism design in the field. Am. Econ. Rev. (2021, forthcoming)
Jurca, R., Faltings, B.: Mechanisms for making crowds truthful. J. Artif. Intell. Res. 34, 209–253 (2009)
Kassim, S.H., Rahman, M.: Handling default risks in microfinance: the case of Bangladesh. Qual. Res. Financ. Mark. 10(4), 363–380 (2018)
Kong, Y., Schoenebeck, G.: An information theoretic framework for designing information elicitation mechanisms that reward truth-telling. ACM Trans. Econ. Comput. 7(1), 2:1–2:33 (2019)
Maitra, P., Mitra, S., Mookherjee, D., Motta, A., Visaria, S.: Agent intermediated lending: a new approach to microfinance. Monash University, Department of Economics (2013)
Miller, N., Resnick, P., Zeckhauser, R.: Eliciting informative feedback: the peer-prediction method. Manag. Sci. 51(9), 1359–1373 (2005)
Nisan, N., Ronen, A.: Computationally feasible VCG mechanisms. J. Artif. Intell. Res. 29, 19–47 (2007)
Papakonstantinou, A., Rogers, A., Gerding, E.H., Jennings, N.R.: Mechanism design for the truthful elicitation of costly probabilistic estimates in distributed information systems. Artif. Intell. 175(2), 648–672 (2011)
Porter, K.: Microcredit summit (2020). https://www.microcreditsummit.org/microfinance-statistics/. Accessed Sept 2020
Radanovic, G., Faltings, B., Jurca, R.: Incentives for effort in crowdsourcing using the peer truth serum. ACM Trans. Intell. Syst. Technol. 7(4), 48:1–48:28 (2016)
Shnayder, V., Agarwal, A., Frongillo, R.M., Parkes, D.C.: Informed truthfulness in multi-task peer prediction. In: Proceedings of the 2016 ACM Conference on Economics and Computation, EC 2016, pp. 179–196. ACM (2016)
Soule, D., Grushka-Cockayne, Y., Merrick, J.R.: A heuristic for combining correlated experts. SSRN Electron. J. (2020)
Waggoner, B., Chen, Y.: Output agreement mechanisms and common knowledge. In: Proceedings of the Second AAAI Conference on Human Computation and Crowdsourcing (2014)
Wang, J., Liu, Y., Chen, Y.: Forecast aggregation via peer prediction. CoRR abs/1910.03779 (2019)
Winkler, R.L.: Evaluating probabilities: asymmetric scoring rules. Manag. Sci. 40(11), 1395–1405 (1994)
Witkowski, J., Parkes, D.C.: Peer prediction without a common prior. In: Proceedings of the 13th ACM Conference on Electronic Commerce, EC, pp. 964–981. ACM (2012)
Witkowski, J., Parkes, D.C.: A robust Bayesian truth serum for small populations. In: Proceedings of the Twenty-Sixth AAAI Conference on AI (2012)
York, M., Dahleh, M., Parkes, D.C.: Eliciting social knowledge for creditworthiness assessment. arXiv (2108.09289) (2021)
Zermeño, L.: A principal-expert model and the value of menus. Technical report, MIT Economics (2011)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-94676-0_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-94675-3
Online ISBN: 978-3-030-94676-0
eBook Packages: Computer ScienceComputer Science (R0)