On optimal decision-making in brains and social insect colonies

Abstract

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.

Do small swarms have an advantage when house hunting? The effect of swarm size on nest-site selection by Apis mellifera

“…Nest-site selection by honeybees shares fundamental characteristics with other natural systems that exhibit speedaccuracy trade-offs in decision-making [7,36,48]. Such systems are often modelled in the context of making a choice between two alternatives (see [49] for a review).…”

“…The standard problem examined in most modelling studies of the bees' decision-making process [15,22,28,29,[32][33][34][35][36][37][38] and in some controlled experimental studies (e.g. [6]) is to give swarms a choice between two to six nest sites (but see [39] for a modelling study with a choice between 120 distinct nest sites).…”

Speed–accuracy trade-offs during foraging decisions in the acellular slime mould Physarum polycephalum

“…Although designed to examine decisionmaking in neuron-based information-processing systems, these models could theoretically be applied to any system where information is collected over time. Indeed, Marshall et al [18] recently developed a framework for examining optimal decision-making and SATs in primate brains and insect colonies. All three systems can be modelled as interacting populations that accumulate evidence favouring options up to some threshold [18].…”

“…Indeed, Marshall et al [18] recently developed a framework for examining optimal decision-making and SATs in primate brains and insect colonies. All three systems can be modelled as interacting populations that accumulate evidence favouring options up to some threshold [18]. A decision is made when accumulated evidence exceeds this threshold.…”

“…Recent work suggests that slime moulds, which are highly decentralized organisms, are capable of surprisingly sophisticated decision-making and problemsolving [10 -17]. Parallels have also been drawn between insect colonies (which use decentralized information processing) and primate brains [18]. Slime moulds provide interesting alternatives to brains and insect colonies with which to test the constraints of information processing.…”