Coupled Stochastic Differential Equations and Collective Decision Making in the Two-Alternative Forced-Choice Task
Ioannis Poulakakis, Luca Scardovi and Naomi Ehrich Leonard
Proceedings of the American Control Conference, Baltimore, MD, 2010.
This paper investigates the effect of coupling in a collective decision-making scenario, in which the task is to correctly identify a (noisy) stimulus between two known alternatives. Multiple interconnected decision-making units, each represented by a Drift-Diffusion Model (DDM), accumulate evidence toward a decision. A number of different graph topologies among the DDM's are considered, and their effect on the accuracy of the decision is investigated. It is deduced that, for the same stimuli, the average of the collected evidence increases linearly with time toward the correct decision regardless of the communication topology. However, the uncertainty associated with the process is affected by the interconnection graph, implying that certain topologies are better than others.
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