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AbstractThe honey bee swarm carried out its best‐of‐N nest site computations by operating more than a dozen different information processing loops in parallel and by recruiting more resources to provide greater precision in loops evaluating the better quality sites. The positive feedback effects of recurrent recruitment by means of waggle dance signalling amplified the utilisation of the swarm's energy, memory and carrier resources. The relatively strong negative feedback effects of various attenuation mechanisms tended to reduce resource use and therefore counter‐balanced amplification for long enough for a meaningful nest site survey and quorum decision to be made. Some information processing mechanisms such as exploration tendency, waggle dance signalling, site non‐specific attenuation, noise reduction, independent site evaluation, energy efficient coding, mixed precision processing, self‐organising computation and quorum decision making were found to profoundly influence the efficiency of resource use. Significant insights were also gained into extended cognition, dark data processing, information quality and resource leakage. Finally, the energy cost of acquiring and processing sensory information was estimated.

AbstractThis study investigated the resource budget available to a typical honey bee swarm as it gathered information and decided which was the best of some 13–34 potential nest sites to move into and become its new home. After a brief description of the decision‐making mechanisms involved, the utilization of the swarm's finite energy, memory and carrier resources is described. It was found that each individual scout decision maker only carried enough energy reserves to provide for 2.6 days of visits to evaluate distant sites and, effectively, only enough memory space to remember the location and quality of approximately one site. Scouts, who only formed 4.4% of the swarm bees, consumed 22.8% of the swarm's energy budget, showing that their best‐of‐N computations were energetically very expensive. A typical swarm of 12,000 bees only contained enough energy reserves to last for 13.5 days after which foraging had to restart.

Technological innovation is almost certainly the key driver of long‐term economic growth. Yet for many years, not only have discoveries depended on serendipity and happenstance but also selection and development of the innovations has been managed by over‐centralized bureaucracies that are more concerned with stability and error reduction than with adaptation to a changing world. This paper uses bee swarm logic to suggest a different way forward. The novel system proposed would consist of a designed collective intelligence platform co‐ordinating the work of a number of competing, self‐organizing research groups, each using evolutionary algorithms to optimize their potential solution to a given innovation problem. Each research group would compete to recruit supporters in strict proportion to the independently estimated profitability of its solution. The first solution to accumulate a quorum level of support would win the competition, and its supporters would then be able to develop the solution through implementation. Copyright © 2017 John Wiley & Sons, Ltd.

This article examines some of the major mechanisms that might contribute to the apparent collective intelligence of the honey bee swarm and determines their role in its nest site decision making process. Decentralization of power allowed the swarm to utilize massively parallel information processing during its search for a new home. Various diversity generating mechanisms provided the swarm with a full range of potential nest sites for consideration. Numerous independent evaluations of nest site quality provided a relatively unbiased and noise free assessment of each site's survival worthiness. Differential net recruitment to the better quality sites aggregated evidence in favour of each site. A natural selection type process selected out the best competing sites and the first site to accumulate a quorum level of support was chosen as the swarm's new home. The article discusses the implications of these findings and how they relate to collective decision making by human groups. Copyright © 2016 John Wiley & Sons, Ltd.