Behavior analysts have long recognized the benefits of closely following their data; however, the data we are following may be moving faster than the tools we have to accurately analyze and predict future behaviors. This problem even saturates behavior-analytic investigations that focus on the evaluation of complex data related to public policy issues in areas such as poverty, geriatrics, and child welfare practice. In the face of this research enigma, there exists a more powerful and precise set of classification and prediction platforms for researchers in the behavioral sciences. In this article, we describe a combination of neural network strategies that predict child welfare professionals’ decision making. Extending the data analysis from the Yelick and Thyer (2019) study, we employed our current version of the Kohonen self-organizing map in conjunction with our deep neural network as a strategy for identifying participants who were at high probability for making heuristic decisions.

行为分析师早就认识到密切关注他们的数据的好处；然而，我们所跟踪的数据可能比我们准确分析和预测未来行为所需的工具移动得更快。这个问题甚至使行为分析调查饱和，这些调查侧重于评估与贫困、老年病学和儿童福利实践等领域的公共政策问题相关的复杂数据。面对这个研究之谜，行为科学领域的研究人员有一套更强大、更精确的分类和预测平台。在本文中，我们描述了预测儿童福利专业人员决策的神经网络策略组合。扩展 Yelick 和 Thyer (2019) 研究的数据分析，