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.

翻译：人类及其他智能体常基于“群体优于个体”的直觉依赖集体决策，然而目前我们仍缺乏对群体何时表现更优的完整理论理解。本研究在一个现实公民科学任务环境中考察集体决策表现，该环境中的个体具有经过操控的任务相关训练差异并进行协作。我们发现：1）二元组的表现虽逐步提升，但在多数情境下并未相较个体产生集体收益；2）个体训练后转向二元协作情境时，协调对效率和速度造成的代价始终大于合作伙伴带来的杠杆效应——即使合作伙伴在该任务中受过专业训练；3）在最高复杂度的任务中，二元组中增加一名受过充分训练的专家可提升准确率。这些发现表明：个体所受训练程度、当前任务复杂度以及期望绩效指标均为评估集体决策效益时必须考量的关键因素。