The Wisdom of Crowds: Social Learning with Online Ratings

Author : Shraman Banerjee, Swagata Bhattacharjee

Abstract : The digital revolution has profoundly altered the epistemic foundations of consumer behaviour. Today, a potential consumer gathers most of the information through a large volume of decentralized, user-generated content that is freely accessible. Online reviews, a dominant form of this content, purport to democratize knowledge, enabling buyers to make informed decisions based on the aggregated experience of peers. This empirical prominence invites a theoretical interrogation: Does the "wisdom of crowds" manifest in online reviews? Can decentralized and voluntary feedback approximate an objective signal of quality? This paper provides a formal answer, employing the social learning framework of Smith and Sørensen (2000) to analyse the informational quality of online ratings. Since reviews are self-reported after consumption, they are susceptible to endogenous selection.This self-reported nature of ratings leads to only extreme reviews being posted. It distorts the ratings distribution: a past consumer not purchasing the good vis-a-vis a past consumer purchasing but not leaving a rating becomes informationally equivalent. This prohibits complete learning even with a large number of reviews, this shows a possible failure of the “wisdom of crowds” theory. We also find that the ability to post fake reviews may actually enhance the aggregation properties of online reviews, leading to complete learning.

Keywords : Ethical governance, artificial superintelligence, ahumanism, letting-be, impact-oriented, positive inaction.

Conference Name : International Conference on Customer Behavior in Digital Business Environments (ICCBDBE-26)

Conference Place : Berlin, Germany

Conference Date : 30th Jun 2026