Hidden Indicators of Collective Intelligence in Crowdfunding

Emoke Ágnes Horvát, Henry Kudzanai Dambanemuya, Jayaram Uparna, Brian Uzzi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Extensive literature argues that crowds possess essential collective intelligence benefits that allow superior decision-making by untrained individuals working in low-information environments. Classic wisdom of crowds theory is based on evidence gathered from studying large groups of diverse and independent decision-makers. Yet, most human decisions are reached in online settings of interconnected like-minded people that challenge these criteria. This observation raises a key question: Are there surprising expressions of collective intelligence online? Here, we explore whether crowds furnish collective intelligence benefits in crowdfunding systems. Crowdfunding has grown and diversified quickly over the past decade, expanding from funding aspirant creative works and supplying pro-social donations to enabling large citizen-funded urban projects and providing commercial interest-based unsecured loans. Using nearly 10 million loan contributions from a market-dominant lending platform, we find evidence for collective intelligence indicators in crowdfunding. Our results, which are based on a two-stage Heckman selection model, indicate that opinion diversity and the speed at which funds are contributed predict who gets funded and who repays, even after accounting for traditional measures of creditworthiness. Moreover, crowds work consistently well in correctly assessing the outcome of high-risk projects. Finally, diversity and speed serve as early warning signals when inferring fundraising based solely on the initial part of the campaign. Our findings broaden the field of crowd-aware system design and inform discussions about the augmentation of traditional financing systems with tech innovations.

Original languageEnglish (US)
Title of host publicationACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023
PublisherAssociation for Computing Machinery, Inc
Pages3806-3815
Number of pages10
ISBN (Electronic)9781450394161
DOIs
StatePublished - Apr 30 2023
Event2023 World Wide Web Conference, WWW 2023 - Austin, United States
Duration: Apr 30 2023May 4 2023

Publication series

NameACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023

Conference

Conference2023 World Wide Web Conference, WWW 2023
Country/TerritoryUnited States
CityAustin
Period4/30/235/4/23

Keywords

  • aggregation speed
  • collective decision-making
  • crowds
  • early warning signals
  • opinion diversity

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

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