Swarm intelligence in animal groups: when can a collective out-perform an expert?
Swarm intelligence in animal groups: when can a collective out-perform an expert?
An important potential advantage of group-living that has been mostly neglected by life scientists is that individuals in animal groups may cope more effectively with unfamiliar situations. Social interaction can provide a solution to a cognitive problem that is not available to single individuals via two potential mechanisms: (i) individuals can aggregate information, thus augmenting their 'collective cognition', or (ii) interaction with conspecifics can allow individuals to follow specific 'leaders', those experts with information particularly relevant to the decision at hand. However, a-priori, theory-based expectations about which of these decision rules should be preferred are lacking. Using a set of simple models, we present theoretical conditions (involving group size, and diversity of individual information) under which groups should aggregate information, or follow an expert, when faced with a binary choice. We found that, in single-shot decisions, experts are almost always more accurate than the collective across a range of conditions. However, for repeated decisions - where individuals are able to consider the success of previous decision outcomes - the collective's aggregated information is almost always superior. The results improve our understanding of how social animals may process information and make decisions when accuracy is a key component of individual fitness, and provide a solid theoretical framework for future experimental tests where group size, diversity of individual information, and the repeatability of decisions can be measured and manipulated.
1-5
Katsikopoulos, Konstantinos V.
b97c23d9-8b24-4225-8da4-be7ac2a14fba
King, Andrew J.
e25571c8-c35e-448e-b7fe-308adef688e6
7 December 2010
Katsikopoulos, Konstantinos V.
b97c23d9-8b24-4225-8da4-be7ac2a14fba
King, Andrew J.
e25571c8-c35e-448e-b7fe-308adef688e6
Katsikopoulos, Konstantinos V. and King, Andrew J.
(2010)
Swarm intelligence in animal groups: when can a collective out-perform an expert?
PLoS ONE, 5 (11), , [e15505].
(doi:10.1371/journal.pone.0015505).
Abstract
An important potential advantage of group-living that has been mostly neglected by life scientists is that individuals in animal groups may cope more effectively with unfamiliar situations. Social interaction can provide a solution to a cognitive problem that is not available to single individuals via two potential mechanisms: (i) individuals can aggregate information, thus augmenting their 'collective cognition', or (ii) interaction with conspecifics can allow individuals to follow specific 'leaders', those experts with information particularly relevant to the decision at hand. However, a-priori, theory-based expectations about which of these decision rules should be preferred are lacking. Using a set of simple models, we present theoretical conditions (involving group size, and diversity of individual information) under which groups should aggregate information, or follow an expert, when faced with a binary choice. We found that, in single-shot decisions, experts are almost always more accurate than the collective across a range of conditions. However, for repeated decisions - where individuals are able to consider the success of previous decision outcomes - the collective's aggregated information is almost always superior. The results improve our understanding of how social animals may process information and make decisions when accuracy is a key component of individual fitness, and provide a solid theoretical framework for future experimental tests where group size, diversity of individual information, and the repeatability of decisions can be measured and manipulated.
Other
journal.pone.0015505
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Accepted/In Press date: 6 October 2010
e-pub ahead of print date: 24 November 2010
Published date: 7 December 2010
Identifiers
Local EPrints ID: 438450
URI: http://eprints.soton.ac.uk/id/eprint/438450
ISSN: 1932-6203
PURE UUID: e10129c9-b9b4-46ca-a61e-1520592a46fb
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Date deposited: 10 Mar 2020 17:31
Last modified: 17 Mar 2024 03:44
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