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Jayaram Uparna

dblp:171/2144 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0003-2393-4909ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 2 · 1 since 2021Artificial intelligence and machine learning · 1Human-computer interaction and ubiquitous computing · 1Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational social science and digital humanities · 67% Computational finance and economics · 33%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational social science and digital humanities
collective intelligence
0.712023
Hidden Indicators of Collective Intelligence in Crowdfunding · WWW 2023
Computational finance and economics
crowdfunding
0.712023
Hidden Indicators of Collective Intelligence in Crowdfunding · WWW 2023
Computational social science and digital humanities › collective intelligence
wisdom of crowds
0.712023
Hidden Indicators of Collective Intelligence in Crowdfunding · WWW 2023

Methods — techniques the papers use, named apart from their topics

heckman selection model · 0.7
YearPublicationVenuePosition
2023 Hidden Indicators of Collective Intelligence in Crowdfunding
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.
Ágnes Horvát, Henry K. Dambanemuya, Jayaram Uparna, Brian Uzzi
WWW3
2015 Network vs Market Relations: The Effect of Friends in Crowdfunding
abstract
Crowds offer a new form of efficacious collective decision making, yet knowledge about the mechanisms by which they achieve superior outcomes remains nascent. It has been suggested that crowds work best with market-like relationships when individuals make independent decisions and possess dissimilar information. By contrast, sociological discussions of markets argue that risky decisions are mitigated by network relations that embed economic transactions in social ties that promote trustworthiness and reciprocity. To investigate the role of networks within crowds and their performance effects, we examined the complete record of financial lending decisions on Prosper.com, 1/2006-3/2012, the first U.S. crowdfunding platform and a chief gateway to capital for entrepreneurs and general borrowers that continues to disrupt conventional financial lending structures infusing more than $5.1 billion into the market in 2013. Our study reveals how reciprocity, recurring borrower-lender dyads, and persistent co-lending underpin the dynamics of network lending. Further, we show how network ties influence the evolution of the lending behavior. We find that in the early stage of fundraising, network relations provide larger proportions of loans, typically lending four times more per bid than strangers. They also respond to loan requests on average 59.5% sooner than strangers. The size of the first loan and the time to lending also tend to prompt lending by strangers, suggesting that network relations might move the market, a finding that persists even as fewer lenders dominate more of the market for loans on Prosper. Finally, network relations are associated with greater engagement: when the first loan is underwritten by a friend, 50% of the remaining loans come from friends as well.
Ágnes Horvát, Jayaram Uparna, Brian Uzzi
ASONAM2