Differentiating between static and complex problems: A theoretical framework and its empirical validation

Matthias Stadler, Christoph Niepel and Samuel Greiff

Intelligence, 2019, vol. 72, issue C, 1-12

Abstract: Ever since its first conception, the concept of complex problem solving (CPS) has been debated regarding its role within the conception of human intelligence. The aim of the current paper was to theoretically introduce and empirically test a multifaceted framework of CPS that subsumes different positions and provides testable predictions on the nature of CPS. Following a review of the existing literature on complex problem solving, we conclude that it is necessary to differentiate between the dimensions of connectivity and dynamics. These dimensions are further distinguishable for both phases of CPS, knowledge acquisition and knowledge application, resulting in four facets. Static problems, as used in conventional measures of intelligence, on the other hand, do not include dynamics as much as complex problems. We argue that the differences in CPS and static problem solving proposed by various researchers result from the dimension of dynamics that is highly relevant for CPS but less so for static problem solving. An empirical analysis based on two independent samples supported the assumptions made by the framework. This brings substantial implications for the understanding of CPS as well as the interpretation of previous research, which we discuss.

Keywords: Complex problem solving; Intelligence; Problem solving; Framework; Cognitive abilities; Structural equation modeling (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intell:v:72:y:2019:i:c:p:1-12

DOI: 10.1016/j.intell.2018.11.003

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