Crowds vs swarms, a comparison of intelligence

Louis Rosenberg, David Baltaxe, Niccolo Pescetelli

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

42 Scopus citations

Abstract

For well over a century, researchers in the field of Collective Intelligence have shown that groups can outperform individuals when making decisions, predictions, and forecasts. The most common methods for harnessing the intelligence of groups treats the population as a 'crowd' of independent agents that provide input in isolation in the form of polls, surveys, and market transactions. While such crowd-based methods can be effective, they are markedly different from how natural systems harness group intelligence. In the natural world, groups commonly form real-time closed-loop systems (i.e. 'swarms') that converge on solutions in synchrony. The present study compares the predictive ability of crowds and swarms when tapping the intelligence of human groups. More specifically, the present study tasked a crowd of 469 football fans and a swarm of 29 football fans in a challenge to predict 20 Prop Bets during the 2016 Super Bowl. Results revealed that the crowd, although 16 times larger in size, was significantly less accurate (at 47% correct) than the swarm (at 68% correct). Further, the swarm outperformed 98% of the individuals in the full study. These results suggest that swarming, with closed-loop feedback, is potentially a more effective method for tapping the insights of groups than traditional polling.

Original languageEnglish (US)
Title of host publication2016 Swarm/Human Blended Intelligence, SHBI 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509035021
DOIs
StatePublished - Dec 9 2016
Externally publishedYes
Event2016 Swarm/Human Blended Intelligence, SHBI 2016 - Cleveland, United States
Duration: Oct 21 2016Oct 23 2016

Publication series

Name2016 Swarm/Human Blended Intelligence, SHBI 2016

Conference

Conference2016 Swarm/Human Blended Intelligence, SHBI 2016
Country/TerritoryUnited States
CityCleveland
Period10/21/1610/23/16

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Human-Computer Interaction

Keywords

  • Artificial intelligence
  • Collective intelligence
  • Human swarming
  • Swarm intelligence
  • Wisdom of crowds

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