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A simple threshold rule is sufficient to explain sophisticated collective decision-making.

Robinson EJ, Franks NR, Ellis S, Okuda S, Marshall JA - PLoS ONE (2011)

Bottom Line: This highlights the need to carefully design experiments to detect individual comparison.We present empirical data strongly suggesting that best-of-n comparison is not used by individual ants, although individual sequential comparisons are not ruled out.This parsimonious mechanism could promote collective rationality in group decision-making.

View Article: PubMed Central - PubMed

Affiliation: School of Biological Sciences, University of Bristol, Bristol, United Kingdom. Elva.Robinson@yccsa.org

ABSTRACT
Decision-making animals can use slow-but-accurate strategies, such as making multiple comparisons, or opt for simpler, faster strategies to find a 'good enough' option. Social animals make collective decisions about many group behaviours including foraging and migration. The key to the collective choice lies with individual behaviour. We present a case study of a collective decision-making process (house-hunting ants, Temnothorax albipennis), in which a previously proposed decision strategy involved both quality-dependent hesitancy and direct comparisons of nests by scouts. An alternative possible decision strategy is that scouting ants use a very simple quality-dependent threshold rule to decide whether to recruit nest-mates to a new site or search for alternatives. We use analytical and simulation modelling to demonstrate that this simple rule is sufficient to explain empirical patterns from three studies of collective decision-making in ants, and can account parsimoniously for apparent comparison by individuals and apparent hesitancy (recruitment latency) effects, when available nests differ strongly in quality. This highlights the need to carefully design experiments to detect individual comparison. We present empirical data strongly suggesting that best-of-n comparison is not used by individual ants, although individual sequential comparisons are not ruled out. However, by using a simple threshold rule, decision-making groups are able to effectively compare options, without relying on any form of direct comparison of alternatives by individuals. This parsimonious mechanism could promote collective rationality in group decision-making.

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Schematic of model.A simulated ant continues searching until it encounters a nest of a quality (b) exceeding the ant's individual threshold (a), taking into account assessment error (ε). Ants may revisit the same nest (with probability r), and do not have any memory of previously visited nests.
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pone-0019981-g002: Schematic of model.A simulated ant continues searching until it encounters a nest of a quality (b) exceeding the ant's individual threshold (a), taking into account assessment error (ε). Ants may revisit the same nest (with probability r), and do not have any memory of previously visited nests.

Mentions: To investigate the proposed threshold rule, we present a very simple model. As we are only concerned with independent discovery by scouts before recruitment commences, we examine the colony's decision process analytically and using Monte-Carlo simulation, modelling ants as independent instantiations of a Markov process. Ants independently accept sites according to a probability specified by the site's quality and their individual threshold, otherwise they search randomly for a site to assess (Fig. 2). We have modelled a discontinuous acceptance function here, but this could be relaxed to be a smooth (e.g. sigmoidal) function of difference between internal threshold and sampled quality, without qualitatively affecting the results. The Markov process has five states: ‘assessing home site’, ‘assessing poor site’, ‘assessing good site’, ‘committed to poor site’, and ‘committed to good site’. All individuals start in the ‘assessing home site’ state but the home site is considered uninhabitable in the model, and its quality is set to negative infinity, therefore an ant can never become committed to it. Ants can switch between the assessment states, but we assume that once committed to a site an ant remains so and recruits nest-mates to its preferred option; the recruitment process is not modelled, and the ‘committed’ states are therefore absorbing states.


A simple threshold rule is sufficient to explain sophisticated collective decision-making.

Robinson EJ, Franks NR, Ellis S, Okuda S, Marshall JA - PLoS ONE (2011)

Schematic of model.A simulated ant continues searching until it encounters a nest of a quality (b) exceeding the ant's individual threshold (a), taking into account assessment error (ε). Ants may revisit the same nest (with probability r), and do not have any memory of previously visited nests.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3101226&req=5

pone-0019981-g002: Schematic of model.A simulated ant continues searching until it encounters a nest of a quality (b) exceeding the ant's individual threshold (a), taking into account assessment error (ε). Ants may revisit the same nest (with probability r), and do not have any memory of previously visited nests.
Mentions: To investigate the proposed threshold rule, we present a very simple model. As we are only concerned with independent discovery by scouts before recruitment commences, we examine the colony's decision process analytically and using Monte-Carlo simulation, modelling ants as independent instantiations of a Markov process. Ants independently accept sites according to a probability specified by the site's quality and their individual threshold, otherwise they search randomly for a site to assess (Fig. 2). We have modelled a discontinuous acceptance function here, but this could be relaxed to be a smooth (e.g. sigmoidal) function of difference between internal threshold and sampled quality, without qualitatively affecting the results. The Markov process has five states: ‘assessing home site’, ‘assessing poor site’, ‘assessing good site’, ‘committed to poor site’, and ‘committed to good site’. All individuals start in the ‘assessing home site’ state but the home site is considered uninhabitable in the model, and its quality is set to negative infinity, therefore an ant can never become committed to it. Ants can switch between the assessment states, but we assume that once committed to a site an ant remains so and recruits nest-mates to its preferred option; the recruitment process is not modelled, and the ‘committed’ states are therefore absorbing states.

Bottom Line: This highlights the need to carefully design experiments to detect individual comparison.We present empirical data strongly suggesting that best-of-n comparison is not used by individual ants, although individual sequential comparisons are not ruled out.This parsimonious mechanism could promote collective rationality in group decision-making.

View Article: PubMed Central - PubMed

Affiliation: School of Biological Sciences, University of Bristol, Bristol, United Kingdom. Elva.Robinson@yccsa.org

ABSTRACT
Decision-making animals can use slow-but-accurate strategies, such as making multiple comparisons, or opt for simpler, faster strategies to find a 'good enough' option. Social animals make collective decisions about many group behaviours including foraging and migration. The key to the collective choice lies with individual behaviour. We present a case study of a collective decision-making process (house-hunting ants, Temnothorax albipennis), in which a previously proposed decision strategy involved both quality-dependent hesitancy and direct comparisons of nests by scouts. An alternative possible decision strategy is that scouting ants use a very simple quality-dependent threshold rule to decide whether to recruit nest-mates to a new site or search for alternatives. We use analytical and simulation modelling to demonstrate that this simple rule is sufficient to explain empirical patterns from three studies of collective decision-making in ants, and can account parsimoniously for apparent comparison by individuals and apparent hesitancy (recruitment latency) effects, when available nests differ strongly in quality. This highlights the need to carefully design experiments to detect individual comparison. We present empirical data strongly suggesting that best-of-n comparison is not used by individual ants, although individual sequential comparisons are not ruled out. However, by using a simple threshold rule, decision-making groups are able to effectively compare options, without relying on any form of direct comparison of alternatives by individuals. This parsimonious mechanism could promote collective rationality in group decision-making.

Show MeSH
Related in: MedlinePlus