The heterogeneity of outcomes in behavioral research has long been perceived as a challenge for the validity of various theoretical models. More recently, however, researchers have started perceiving heterogeneity as something that needs to be not only acknowledged but also actively addressed, particularly in applied research. A serious challenge, however, is that classical psychological methods are not well suited for making practical recommendations when heterogeneous outcomes are expected. In this article, we argue that heterogeneity requires a separation between basic and applied behavioral methods, and between different types of behavioral expertise. We propose a novel framework for evaluating behavioral expertise and suggest that selective expertise can easily be automated via various machine learning methods. We illustrate the value of our framework via an empirical study of the preferences towards battery electric vehicles. Our results suggest that a basic multiarm bandit algorithm vastly outperforms human expertise in selecting the best interventions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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收获异质性：选择性专业知识与机器学习。


长期以来，行为研究结果的异质性一直被视为对各种理论模型有效性的挑战。然而，最近，研究人员开始将异质性视为不仅需要承认而且需要积极解决的问题，尤其是在应用研究中。然而，一个严峻的挑战是，当预期异质性结果时，经典心理学方法并不适合提出实用的建议。在本文中，我们认为异质性需要将基本行为方法和应用行为方法以及不同类型的行为专业知识分开。我们提出了一个评估行为专业知识的新框架，并建议通过各种机器学习方法轻松实现选择性专业知识的自动化。我们通过对电池电动汽车偏好的实证研究来说明我们框架的价值。我们的结果表明，基本的多臂老虎机算法在选择最佳干预措施方面远远优于人类专业知识。（PsycInfo 数据库记录 （c） 2024 APA，保留所有权利）。