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 accuracy of decision making. In primate decision making simple models of these processes have been shown, under certain parameterisations, to implement the statistically optimal procedure that minimises decision time for any given error rate. In this chapter, 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 organised. Our approach also represents the first attempt to identify a common theoretical framework for the study of decision making in diverse biological systems.
[This originally appeared in the Journal of the Royal Society: Interface, 6, pp. 1065–1074. November 2009.]