  Humans and other intelligent agents often rely on collective decision making
based 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 a
real-world citizen science task environment in which individuals with
manipulated differences in task-relevant training collaborated. We find 1)
dyads gradually improve in performance but do not experience a collective
benefit compared to individuals in most situations; 2) the cost of coordination
to efficiency and speed that results when switching to a dyadic context after
training individually is consistently larger than the leverage of having a
partner, even if they are expertly trained in that task; and 3) on the most
complex tasks having an additional expert in the dyad who is adequately trained
improves accuracy. These findings highlight that the extent of training
received by an individual, the complexity of the task at hand, and the desired
performance indicator are all critical factors that need to be accounted for
when weighing up the benefits of collective decision-making.
