Collective search on rugged landscapes: A cross-environmental analysis

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Abstract

In groups and organizations, agents use both individual and social learning to solve problems. The balance between these two activities can lead collectives to very different levels of performance. We model collective search as a combination of simple learning strategies to conduct the first large-scale comparative study, across fifteen challenging environments and two different network structures. In line with previous findings in the social learning literature, collectives using a hybrid of individual and social learning perform much better than specialists using only one or the other. Importantly, we find that collective performance varies considerably across different task environments, and that different types of network structures can be superior, depending on the environment. These results suggest that recent contradictions in the social learning literature may be due to methodological differences between two separate research traditions, studying disjoint sets of environments that lead to divergent findings.

Original languageEnglish
Title of host publicationProceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016
EditorsA. Papafragou, D. J. Grodner, D. Mirman, Trueswell J.
Number of pages6
PublisherCognitive Science Society
Publication date2016
Pages918-923
ISBN (Electronic)978-0-9911967-3-9
Publication statusPublished - 2016
Externally publishedYes
Event38th Annual Meeting Of The Cognitive Science Society - Philadelphia, United States
Duration: 10. Aug 201613. Aug 2016

Conference

Conference38th Annual Meeting Of The Cognitive Science Society
Country/TerritoryUnited States
CityPhiladelphia
Period10/08/201613/08/2016

Keywords

  • Social learning
  • collective behavior
  • communication networks
  • rugged landscapes
  • search

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