This post is based on a paper: “The Two Settings of Kind and Wicked Learning Environments” written by Robin M. Hogarth, Tomás Lejarraga, and Emre Soyer that appeared in Current Directions in Psychological Science 2015, Vol. 24(5) 379–385. Hogarth created the idea of kind and wicked learning environments and it is discussed in his book Educating Intuition.
Hogarth et al state that inference involves two settings: In the first, information is acquired (learning); in the second, it is applied (predictions or choices). Kind learning environments involve close matches between the informational elements in the two settings and are a necessary condition for accurate inferences. Wicked learning environments involve mismatches.
This post is based on a paper: “Learning from experience in nonlinear environments: Evidence from a competition scenario,” authored by Emre Soyer and Robin M. Hogarth, Cognitive Psychology 81 (2015) 48-73. It is not a new topic, but adds to the evidence of our nonlinear shortcomings.
In 1980, Brehmer questioned whether people can learn from experience – more specifically, whether they can learn to make appropriate inferential judgments in probabilistic environments outside the psychological laboratory. His assessment was quite pessimistic. Other scholars have also highlighted difficulties in learning from experience. Klayman, for example, pointed out that in naturally occurring environments, feedback can be scarce, subject to distortion, and biased by lack of appropriate comparative data. Hogarth asked when experience-based judgments are accurate and introduced the concepts of kind and wicked learning environments (see post Learning, Feedback, and Intuition). In kind learning environments, people receive plentiful, accurate feedback on their judgments; but in wicked learning environments they don’t. Thus, Hogarth argued, a kind learning environment is a necessary condition for learning from experience whereas wicked learning environments lead to error. This paper explores the boundary conditions of learning to make inferential judgments from experience in kind environments. Such learning depends on both identifying relevant information and aggregating information appropriately. Moreover, for many tasks in the naturally occurring environment, people have prior beliefs about cues and how they should be aggregated.