The role of learning in complex problem solving using MicroDYN

Herrmann, W.; Beckmann, J.F.; Kretzschmar, A.

Abstract

It is still an open question which cognitive and non-cognitive personality traits are useful for describing and explaining behaviour and performance in complex problems. During complex problem solving (CPS), problem solvers have to interact with the task in a way in which learning ability might be beneficial for successful task completion. By investigating the relationship between learning ability and CPS, while accounting for interactions between complex system characteristics and person characteristics, this paper aims to understand the role of learning processes in CPS more closely. In a sample of N = 241 participants, we performed a preregistered analysis to investigate the relationship between knowledge acquisition performance in a CPS test (MicroDYN) and learning test performance (ADAFI) with a multilevel modeling approach across 10 CPS systems with various characteristics. In line with our expectations, we replicated previous findings on a relationship between learning test and MicroDYN performance and found this relationship to be more pronounced in systems with (vs. without) autonomous changes. Further system and person characteristics also showed effects as expected, with better performance in systems with lower complexity, with more experience with the task, and with more strategic exploration behaviour. Our results provide further evidence for the notion that learning is an important component for the successful completion of CPS tasks.

Citation

Herrmann, W., Beckmann, J., & Kretzschmar, A. (2023). The role of learning in complex problem solving using MicroDYN. Intelligence, 100, Article 101773. https://doi.org/10.1016/j.intell.2023.101773

• Journal Article Type: Article
• Acceptance Date: Jun 25, 2023
• Online Publication Date: Jul 5, 2023
• Publication Date: 2023
• Deposit Date: Oct 25, 2023
• Publicly Available Date: Oct 25, 2023
• Journal: Intelligence
• Print ISSN: 0160-2896
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 100
• Article Number: 101773
• DOI: https://doi.org/10.1016/j.intell.2023.101773
• Public URL: https://durham-repository.worktribe.com/output/1817567