Abstract
In this commentary, we critically review the study of Greiff, Stadler, Sonnleitner, Wolff, and Martin, “Sometimes less is more: Comparing the validity of complex problem solving measures” (Intelligence, 2015, 50, 100–113). The main conclusion of Greiff et al. that the “multiple complex systems” (MCS) approach to measuring complex problem-solving ability possesses superior validity compared to classical microworld scenarios (“less is more”) seems to be an overgeneralization based on inappropriate analysis and selective interpretation of results. In its original form, MCS is a useful tool for investigating specific aspects of problem solving within dynamic systems. However, its value as an instrument for the assessment of complex problem solving ability remains limited.
Highlights
As researchers involved in developing both the “minimal/multiple complex systems” (MCS)approach and the Tailorshop microworld simulation, we feel the need to comment on the article by Greiff et al [1]
The main claim of Greiff et al [1] is that administering a selection of tasks based on simple dynamic systems yields a measure of complex problem solving ability with superior validity compared to more complex classical microworld scenarios (“less is more”)
We find that due to a number of problems in the statistical analysis and due to selective interpretation of results, conclusions about the true relation of MCS, the Tailorshop simulation, and the construct of complex problem solving presented in this article are not convincing
Summary
As researchers involved in developing both the “minimal/multiple complex systems” (MCS). The main claim of Greiff et al [1] is that administering a selection of tasks based on simple dynamic systems yields a measure of complex problem solving ability with superior validity compared to more complex classical microworld scenarios (“less is more”). In what has been called the “European tradition” of complex problem solving research [2], lay participants are faced with computer-simulated dynamic systems and given the task to explore and control these systems. In line with Greiff et al [1], we will take “classical microworlds” to refer to computer-simulated scenarios of this type, such as the Tailorshop simulation and related tasks (e.g., LEARN, FSYS; [3,4]), which moderately complex and semantically rich.
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