A self-organising network model of decision making by the honey bee swarm

Abstract

Purpose – The purpose of this paper is to carry out a detailed investigation of the mechanisms operating during decision making by the honey bee swarm, which is now considered to be one of the best examples of collective decision making outside the human domain. Design/methodology/approach – This investigation is based on a review of the last 60 years’ published literature about swarm behaviour. It introduces a different perspective to the work by utilising a cybernetic model of a self-organising information network to analyse the findings of this body of research. Findings – Scout bees evaluating potential nest sites accumulated support for their site by differential net recruitment, so the total scout numbers present at each site was a good measure of the total evidence in favour of the site and hence the relative probability of choosing it as the swarm’s new home. The accumulation of evidence continued at a number of alternative nest site locations until a critical quorum threshold was sensed at one of them. The first alternative to reach the threshold was chosen as the preferred nest site. Quorum scouts then prepared the swarm for departure and steered it to its new home. Originality/value – Swarm decision making has not been modelled as a self-organising information network before. This novel approach reveals how a combination of network modifications, self-amplification, self-attenuation, cross-inhibition, integration and quorum mechanisms together contribute towards accurate group decision making.

Major Mechanisms Contributing to Swarm Intelligence

“…A colony is not a swarm and a swarm is not yet a colony. This is an important distinction because the majority of the so called swarm intelligence literature and algorithms are not strictly about swarms at all, they are about the collective intelligence of social insect colonies and groups of various other animal species which are making collective decisions in situations that are very different to those faced by a social insect swarm (Foss, 2016). In contrast, this paper deals with the major mechanisms contributing to honey bee swarm intelligence in its strict sense, and how this relates to decision making in human groups.…”

“…The swarm's non intentional selection process is similar to a natural selection process in which selection within a genetic deme precedes selection between demes (Wright, ). In the case of the swarm, selection within a site supporter group (through site specific and site non specific attenuation and leakage) precedes selection between groups (through sensory and perceptual cross inhibition) (Foss, , ). It is also similar to human sociocultural evolution where there is often very little cross lineage borrowing of memes (Campbell, ).…”

“…Because too small an emphasis on independence would not reliably identify the highest quality option and too small an emphasis on interdependence would not allow a sufficiently rapid aggregation of evidence, a balance between a high level of independence and a high level of interdependence was needed to ensure highly accurate decisions (List et al ., ; Conradt, ). This conclusion indicates that it might be important to maintain some sort of a variety balance between horizontal variety (independence) and vertical variety (interdependence) within a self organizing information network, just as is required within a System One of the Viable System Model (Beer, ; Foss, , , ). The reason for this is that a balance reduces the amount of residual variety escaping from the network that may need to be dealt with by the next higher level system.…”

A Self‐organizing System for Innovation in Large Organizations

“…In human organizations, many decision‐making problems such as the many different forms of bias (Koehler, ; Nickerson, ; Maqsood et al ., ; Forsyth, ; Lunenburg, ), blind imitation (Hull, ), information cascades (Bikchandani et al ., ; Shiller, ; Hung and Plott, ), groupthink (Janis, ) and majority illusions (Lerman et al ., ) are due to the widespread sharing of what should be independent, private opinions. Also, cross inhibition is another information segregation mechanism, which helps the bee swarm to discriminate the small differences between near equally valued alternatives and which therefore reduces the risk of decision deadlock (Schlegel et al ., ; Seeley et al ., ; Foss, ). The critical need to segregate some types of information is well recognized in organizations operating Chinese Walls or screens (Brewer and Nash, ; Gollman, ) and in brainstorming, nominal and Delphi decision‐making (Osborn, ; Delbecq et al ., ; Lunenburg, ).…”

“…It is, however, envisaged that the proposed self‐organizing innovation system would deliver a number of crucial advantages over existing research and development arrangements. The system has been designed to provide the six major mechanisms needed for a collective intelligence system that were identified during previous studies of decision‐making by the honey bee swarm (Foss, , , ). These are as follows: Decentralization of power; Diversity of potential solutions; Independence of opinion; Natural selection of potential solutions; Aggregation of evidence; Decision by quorum threshold. …”