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Learning from Collaboratively Playing with Simulation Models in Policy Making: An Experimental Evaluation in Fisheries Management
Author(s) -
Stouten Hendrik,
Polet Hans,
Heene Aimé,
Gellynck Xavier
Publication year - 2017
Publication title -
systems research and behavioral science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 45
eISSN - 1099-1743
pISSN - 1092-7026
DOI - 10.1002/sres.2464
Subject(s) - policy learning , fisheries management , fishery , knowledge management , policy making , computer science , management science , psychology , economics , biology , machine learning , public economics , fishing
This article builds on earlier work that found that real‐world decision makers did not learn from playing with a high‐complexity simulation model designed as a learning laboratory for their decision‐making domain. The absence of clear learning effects was sought in the absence of collaboration with others during participants' interaction with the simulation model. Collaboration enables the participant to draw on their combined memory capacity and joint information processing abilities. The present study therefore investigates whether individual learning occurs if real‐world decision makers collaboratively play with high‐complex simulation models designed as learning laboratories for their decision‐making domain. No superior learning effects were found for collaborative play. As a result, this study provides additional support for collaborative learning not being more effective than individual learning in high‐complexity situations. Copyright © 2017 John Wiley & Sons, Ltd.