Crowd wisdom enhanced by costly signaling in a virtual rating system

Ofer Tchernichovski, Lucas C. Parra, Daniel Fimiarz, Arnon Lotem, Dalton Conley

Research output: Contribution to journalArticle

Abstract

Costly signaling theory was developed in both economics and biology and has been used to explain a wide range of phenomena. However, the theory’s prediction that signal cost can enforce information quality in the design of new communication systems has never been put to an empirical test. Here we show that imposing time costs on reporting extreme scores can improve crowd wisdom in a previously cost-free rating system. We developed an online game where individuals interacted repeatedly with simulated services and rated them for satisfaction. We associated ratings with differential time costs by endowing the graphical user interface that solicited ratings from the users with “physics,” including an initial (default) slider position and friction. When ratings were not associated with differential cost (all scores from 0 to 100 could be given by an equally low-cost click on the screen), scores correlated only weakly with objective service quality. However, introducing differential time costs, proportional to the deviation from the mean score, improved correlations between subjective rating scores and objective service performance and lowered the sample size required for obtaining reliable, averaged crowd estimates. Boosting time costs for reporting extreme scores further facilitated the detection of top performances. Thus, human collective online behavior, which is typically cost-free, can be made more informative by applying costly signaling via the virtual physics of rating devices.

Original languageEnglish (US)
Pages (from-to)7256-7265
Number of pages10
JournalProceedings of the National Academy of Sciences of the United States of America
Volume116
Issue number15
DOIs
StatePublished - Apr 9 2019

All Science Journal Classification (ASJC) codes

  • General

Keywords

  • Collective behavior
  • Costly signaling
  • Crowd wisdom
  • Online rating
  • Social network

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