Cim ame june 2009 sample paper

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Cim ame june 2009 sample paper

Conceived and designed the model: Received Dec 6; Accepted Apr Copyright Robinson et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.

This article has been cited by other articles in PMC. Associated Data Figure S1: Expected time for an ant to accept any site across varying qualities probabilities of acceptance of sites A and B, calculated from equation 2.

Expected time for an ant to accept any site, using the parameters of Figure 6for the no-comparison threshold-rule of the main text redand for the direct-comparison threshold-rule described above green. This supplementary model information covers details of model parameterisation for the analytical and simulation modelling.

It also describes an alternative analytic model of thresholds with sequential-comparison. It provides the basic model code of the Monte Carlo model.

Further details of model parameterization.

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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 albipennisin 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.

Cim ame june 2009 sample paper

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.

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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.

Introduction Animals need to make choices between multiple available options at many stages in their life histories, such as during mate selection, foraging, or when deciding where to shelter or to build a nest.

Cim ame june 2009 sample paper

The fitness benefits of choosing the best option mean that decision-making strategies will be subject to natural selection.

In other situations, e. One simple but effective strategy is sequential search in which the animal keeps searching until it finds an option that exceeds a threshold of acceptability [2][7].

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This kind of fixed-threshold strategy is used in foraging [8]mate choice [9] and refuge selection [10]. Social animals make collective decisions between available options, particularly about where the group spends its time.

When making a shared collective decision, a group faces the challenges of integrating information from multiple individuals and managing the different decision-preferences of group members [11].

Such decisions are influenced by individual information, conflicts of interest and time-constraints [12][13].In addition to the simulation model described in this paper, we can derive analytic results for the proposed rule for switching between nests.

June–July Colonies were queenright and contained 80– workers and brood of all stages. Colonies were housed in artificial nests This sample size is small because this behaviour was.

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This paper describes a framework to annotate images using personal and social network contexts. The problem is important as the correct context reduces the number of image annotation choices.

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