Limits...
Swarm intelligence in animal groups: when can a collective out-perform an expert?

Katsikopoulos KV, King AJ - PLoS ONE (2010)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Center for Adaptive Behavior and Cognition, Max Planck Institute for Human Development, Berlin, Germany.

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.

Show MeSH
The probablity of usage of the aggregated rule (filled circles) and expert rule (open circles) for repeated decisions, as a function of decision number.a, probability that the first decision made is correct = 0.1; b, probablity that the first decision made is correct = 0.5; c, probablity that the first decision made is correct = 0.9. Results presented are the average of 4,000 simulations (across each group size, n = 3, 5, 7, 9, 11, 13, or 15).
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC2991365&req=5

pone-0015505-g002: The probablity of usage of the aggregated rule (filled circles) and expert rule (open circles) for repeated decisions, as a function of decision number.a, probability that the first decision made is correct = 0.1; b, probablity that the first decision made is correct = 0.5; c, probablity that the first decision made is correct = 0.9. Results presented are the average of 4,000 simulations (across each group size, n = 3, 5, 7, 9, 11, 13, or 15).

Mentions: Thus far we have reported the results of models that assume single-shot, independent choices. These situations may be representative of ephemeral and/or unstable social groups that are faced with making collective decisions only occasionally (or, more precisely, rarely face repeated collective decisions). In more stable social groups, where individuals encounter repeated collective decisions, individuals may be able to store and recall information [22]. We therefore used a Bayesian model to predict the probability of groups using expert and aggregated rules across time, based on the outcome (accuracy) of past decisions (Model 3: see Methods and Analyses). We assumed that each rule was equally likely to be used to make the first decision, and found that, for all group sizes (n = 3 to 15), the probability that groups use each rule-type converges after approximately 20 decisions. The model predicted that the aggregated rule is always favoured, unless the first decision that a group makes is correct with high probability, in which case groups marginally favour the expert rule (Figure 2).


Swarm intelligence in animal groups: when can a collective out-perform an expert?

Katsikopoulos KV, King AJ - PLoS ONE (2010)

The probablity of usage of the aggregated rule (filled circles) and expert rule (open circles) for repeated decisions, as a function of decision number.a, probability that the first decision made is correct = 0.1; b, probablity that the first decision made is correct = 0.5; c, probablity that the first decision made is correct = 0.9. Results presented are the average of 4,000 simulations (across each group size, n = 3, 5, 7, 9, 11, 13, or 15).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2991365&req=5

pone-0015505-g002: The probablity of usage of the aggregated rule (filled circles) and expert rule (open circles) for repeated decisions, as a function of decision number.a, probability that the first decision made is correct = 0.1; b, probablity that the first decision made is correct = 0.5; c, probablity that the first decision made is correct = 0.9. Results presented are the average of 4,000 simulations (across each group size, n = 3, 5, 7, 9, 11, 13, or 15).
Mentions: Thus far we have reported the results of models that assume single-shot, independent choices. These situations may be representative of ephemeral and/or unstable social groups that are faced with making collective decisions only occasionally (or, more precisely, rarely face repeated collective decisions). In more stable social groups, where individuals encounter repeated collective decisions, individuals may be able to store and recall information [22]. We therefore used a Bayesian model to predict the probability of groups using expert and aggregated rules across time, based on the outcome (accuracy) of past decisions (Model 3: see Methods and Analyses). We assumed that each rule was equally likely to be used to make the first decision, and found that, for all group sizes (n = 3 to 15), the probability that groups use each rule-type converges after approximately 20 decisions. The model predicted that the aggregated rule is always favoured, unless the first decision that a group makes is correct with high probability, in which case groups marginally favour the expert rule (Figure 2).

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Center for Adaptive Behavior and Cognition, Max Planck Institute for Human Development, Berlin, Germany.

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.

Show MeSH