Moving targets: collective decisions and flexible choices in house-hunting ants

Franks, Nigel R.; Hooper, James; Gumn, Mike; Bridger, Tamsyn H.; Marshall, James A. R.; Groß, Roderich; Dornhaus, Anna

doi:10.1007/s11721-007-0007-8

Cited by 19 publications

(18 citation statements)

References 39 publications

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“…Both species can select the best nest site even if news of it arrives late in the decision-making process Franks et al 2007b). However, there are some differences between the two collective decision-making systems.…”

Section: Decision-making In Social Insect Coloniesmentioning

confidence: 99%

“…Experimental (Franks et al 2007b) and theoretical work has examined the robustness of social insects' collective decision-making to this kind of late information. Our analysis shows how such robustness might be understood because under the direct-switching model, once all scouts are committed and no further information on new alternatives can arrive, decisionmaking proceeds optimally between the available alternatives based on their quality: the only departure from optimality is the bias in the starting point of the decision process based on the relative discovery times of the alternatives.…”

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confidence: 99%

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On optimal decision-making in brains and social insect colonies

Marshall

Bogacz²,

Dornhaus³

et al. 2009

J. R. Soc. Interface.

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202

156

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The problem of how to compromise between speed and accuracy in decision-making faces organisms at many levels of biological complexity. Striking parallels are evident between decision-making in primate brains and collective decision-making in social insect colonies: in both systems, separate populations accumulate evidence for alternative choices; when one population reaches a threshold, a decision is made for the corresponding alternative, and this threshold may be varied to compromise between the speed and the accuracy of decisionmaking. In primate decision-making, simple models of these processes have been shown, under certain parametrizations, to implement the statistically optimal procedure that minimizes decision time for any given error rate. In this paper, we adapt these same analysis techniques and apply them to new models of collective decision-making in social insect colonies. We show that social insect colonies may also be able to achieve statistically optimal collective decision-making in a very similar way to primate brains, via direct competition between evidence-accumulating populations. This optimality result makes testable predictions for how collective decision-making in social insects should be organized. Our approach also represents the first attempt to identify a common theoretical framework for the study of decision-making in diverse biological systems.

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Section: Decision-making In Social Insect Coloniesmentioning

confidence: 99%

mentioning

confidence: 99%

On optimal decision-making in brains and social insect colonies

Marshall

Bogacz²,

Dornhaus³

et al. 2009

J. R. Soc. Interface.

Self Cite

202

156

View full text Add to dashboard Cite

show abstract

“…In a different study on Temnothorax albipennis, involving multiple target options which increased the degree of complexity for the ants, colony splitting was observed. When the preferred nest was degraded after relocation had been initiated, only 15% of the colonies were able to switch their choice and occupy the new target nest and 22% of the colonies split between the available options (Franks et al, 2007). In a study on T. albipennis dealing with quorum sensing, it was found that these ants do not switch back from carrying to tandem running (Pratt, 2005).…”

Section: Discussionmentioning

confidence: 99%

“…How organisms respond to a midway change has hardly been explored (but see Franks et al, 2007). In the present study, we analyse the flexibility of the relocation process.…”

Section: Introductionmentioning

confidence: 99%

Response to a change in the target nest during ant relocation

Anoop

Annagiri

2015

Journal of Experimental Biology

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Decisions regarding spatial and temporal choices pertaining to a wide range of activities such as mating, feeding and resting are necessary for all organisms. Social species encounter another level of complexity, wherein inputs from multiple individuals have to be consolidated to yield a consensus. One platform on which decision making can be examined is the relocation of animal societies. Relocation is the process by which organisms move from their old dwelling to a new nest along with all the inhabitants. This exposes them to the elements and thus impacts their survival and reproduction. Diacamma indicum, the model system for our experiments is a ponerine ant that uses tandem running for colony relocation. In the present study an artificial manipulation was performed to cause a shift in the target nest. The flexibility of the relocation process and, more specifically, the response of tandem leaders to a changed target were studied. A majority of these leaders (N=262) not only re-evaluated and adapted to the change, but did so with negligible error (1.65%). This enabled colonies (N=10) to reunite at the target nest in every case. The only cost to this flexible decision making was paid in terms of additional time. Although considered to be a primitive method of recruitment, we reveal that tandem running allows D. indicum leaders to incorporate assessment of the available options at every step. This inherent flexibility in decision making would be a good strategy for organisms that need to function with incomplete information or inhabit environments that change frequently.

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“…The quorum threshold itself is not modulated depending on the quality of the new nest [30]. If nest quality is artificially manipulated during the assessment phase of an emigration, the ants are able to respond flexibly to the new nest qualities; if quality is manipulated after quorum is reach and implementation has begun, then colonies often become 'trapped' in an inferior nest [15], [?]. This indicates that quality is not re-assessed after quorum has been reached in these cavity-dwelling Temnothorax species.…”

Section: Quorum Sensing In House-hunting Antsmentioning

confidence: 99%

Computational Collective Intelligence

Araújo

Treur

2016

Lecture Notes in Computer Science

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Abstract. Population based decision mechanisms employed by many Swarm Intelligence methods can suffer poor convergence resulting in ill-defined halting criteria and loss of the best solution. Conversely, as a result of its resource allocation mechanism, the solutions found by Stochastic Diffusion Search enjoy excellent stability. Previous implementations of SDS have deployed complex stopping criteria derived from global properties of the agent population; this paper examines two new local SDS halting criteria and compares their performance with 'quorum sensing' -a natural termination criterion deployed in nature by some species of tandem-running ants. We empirically demonstrate that local termination criteria are almost as robust as the classical SDS termination criteria, whilst the average time taken to reach a decision is around three times faster.

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Moving targets: collective decisions and flexible choices in house-hunting ants

Cited by 19 publications

References 39 publications

On optimal decision-making in brains and social insect colonies

On optimal decision-making in brains and social insect colonies

Response to a change in the target nest during ant relocation

Computational Collective Intelligence

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