Cruz Nicole, Desai Saoirse Connor, Dewitt Stephen, Hahn Ulrike, Lagnado David, Liefgreen Alice, Phillips Kirsty, Pilditch Toby, Tešić Marko
Department of Psychological Sciences, Birkbeck, University of London, London, United Kingdom.
Department of Psychology, City, University of London, London, United Kingdom.
Front Psychol. 2020 Apr 9;11:660. doi: 10.3389/fpsyg.2020.00660. eCollection 2020.
Bayesian reasoning and decision making is widely considered normative because it minimizes prediction error in a coherent way. However, it is often difficult to apply Bayesian principles to complex real world problems, which typically have many unknowns and interconnected variables. Bayesian network modeling techniques make it possible to model such problems and obtain precise predictions about the causal impact that changing the value of one variable may have on the values of other variables connected to it. But Bayesian modeling is itself complex, and has until now remained largely inaccessible to lay people. In a large scale lab experiment, we provide proof of principle that a Bayesian network modeling tool, adapted to provide basic training and guidance on the modeling process to beginners without requiring knowledge of the mathematical machinery working behind the scenes, significantly helps lay people find normative Bayesian solutions to complex problems, compared to generic training on probabilistic reasoning. We discuss the implications of this finding for the use of Bayesian network software tools in applied contexts such as security, medical, forensic, economic or environmental decision making.
贝叶斯推理与决策被广泛认为具有规范性，因为它以连贯的方式将预测误差最小化。然而，将贝叶斯原理应用于复杂的现实世界问题往往很困难，这些问题通常有许多未知因素和相互关联的变量。贝叶斯网络建模技术使对这类问题进行建模并获得关于改变一个变量的值可能对与其相关的其他变量的值产生的因果影响的精确预测成为可能。但贝叶斯建模本身很复杂，迄今为止，外行人在很大程度上仍然难以掌握。在一项大规模实验室实验中，我们提供了原理证明：一种贝叶斯网络建模工具，经过调整可为初学者提供关于建模过程的基础培训和指导，而无需了解背后的数学原理，与概率推理的通用培训相比，它能显著帮助外行人找到复杂问题的规范性贝叶斯解决方案。我们讨论了这一发现对于在安全、医疗、法医、经济或环境决策等应用场景中使用贝叶斯网络软件工具的意义。
