Researchers use consensus emergence models (CEMs) to detect when the scores of group members become similar over time. The purpose of this article is to review how CEMs often lead to spurious conclusions of consensus emergence due to the problem of distinguishability, or the notion that different data-generating mechanisms sometimes give rise to similar observed data. As a result, CEMs often cannot distinguish between observations generated from true consensus processes versus those generated by stochastic fluctuations. It will be shown that a distinct set of mechanisms, none of which exhibit true consensus, nonetheless yield spurious inferences of consensus emergence when CEMs are fitted to the observed data. This problem is demonstrated via examples and Monte Carlo simulations. Recommendations for future work are provided.

中文翻译：

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由于可区分性问题，共识出现建模中的虚假推断。

研究人员使用共识涌现模型 (CEM) 来检测小组成员的分数何时变得相似。本文的目的是回顾由于可区分性问题，或者不同的数据生成机制有时会产生相似的观测数据的概念，CEM 如何经常导致共识出现的虚假结论。因此，CEM 通常无法区分真正共识过程产生的观察结果与随机波动产生的观察结果。将表明，当 CEM 与观察到的数据拟合时，一组不同的机制（其中没有一个表现出真正的共识）会产生共识出现的虚假推论。这个问题通过例子和蒙特卡洛模拟得到证明。提供了对未来工作的建议。