Model of the best-of-Nnest-site selection process in honeybees

Abstract

The ability of a honeybee swarm to select the best nest site plays a fundamental role in determining the future colony's fitness. To date, the nest-site selection process has mostly been modelled and theoretically analysed for the case of binary decisions. However, when the number of alternative nests is larger than two, the decision process dynamics qualitatively change. In this work, we extend previous analyses of a valuesensitive decision-making mechanism to a decision process among N nests. First, we present the decisionmaking dynamics in the symmetric case of N equal-quality nests. Then, we generalise our findings to a best-of-N decision scenario with one superior nest and N -1 inferior nests, previously studied empirically in bees and ants. Whereas previous binary models highlighted the crucial role of inhibitory stop-signalling, the key parameter in our new analysis is the relative time invested by swarm members in individual discovery and in signalling behaviours. Our new analysis reveals conflicting pressures on this ratio in symmetric and best-of-N decisions, which could be solved through a time-dependent signalling strategy. Additionally, our analysis suggests how ecological factors determining the density of suitable nest sites may have led to selective pressures for an optimal stable signalling ratio.

Inhibition and excitation shape activity selection: effect of oscillations in a decision-making circuit

“…Further to this, we also demonstrate that an equal increase of 258 initial deficits accelerates the reduction of deficits in Section 9 in S1 Text. This is a 259 clear indication that the interneuronal inhibition model employed in the presented 260 paper is sensitive to input magnitudes, which has emerged to be a general feature of 261 both individual [9,47,48] and collective decision-making [30,[49][50][51]. We present more 262 details on magnitude-sensitivity of our interneuronal inhibition model in Section 1 in S1 263 Text.…”

Frequency-Sensitivity and Magnitude-Sensitivity in Decision-Making: Predictions of a Theoretical Model-Based Study

“…Furthermore, this hallmark is also proposed to be present in collective decision-making of social insects. For example, sensitivity to the quality values of nest-sites in the decision-making of house-hunting honeybees has been found in mathematical analyses (Pais et al 2013;Reina et al 2017;Reina et al 2018) and has been discussed theoretically (Pirrone et al 2014;Bose et al 2017).…”

Scale invariance in natural and artificial collective systems: a review

“…Straightforward mechanisms for achieving SF node degree distributions such as preferential attachment are easily implemented in systems in which all components can be easily connected, but have not yet been extended to systems in which spatial distances play an important role, such as moving systems, where agents are continuously entering and leaving the communication range of other agents. Examples of engineered systems where SF topology would be hard to achieve include Mobile Ad-Hoc Networks (MANETs) [217] (that is, WSNs equipped with mobility) and swarm robotics [218][219][220][221]. Within swarm robotics, an interesting research direction could be to use virtual potential functions [222] that allow the control of robot formations to achieve SF topologies.…”