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Humans and other intelligent agents often rely on collective decision makingbased on an intuition that groups outperform individuals. However, at present,we lack a complete theoretical understanding of when groups perform better.Here we examine performance in collective decision-making in the context of areal-world citizen science task environment in which individuals withmanipulated differences in task-relevant training collaborated. We find 1)dyads gradually improve in performance but do not experience a collectivebenefit compared to individuals in most situations; 2) the cost of coordinationto efficiency and speed that results when switching to a dyadic context aftertraining individually is consistently larger than the leverage of having apartner, even if they are expertly trained in that task; and 3) on the mostcomplex tasks having an additional expert in the dyad who is adequately trainedimproves accuracy. These findings highlight that the extent of trainingreceived by an individual, the complexity of the task at hand, and the desiredperformance indicator are all critical factors that need to be accounted forwhen weighing up the benefits of collective decision-making.

The cost of coordination can exceed the benefit of collaboration in performing complex tasks