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Simulation and Learning

Landriscina, Franco
Springer-Verlag: Berlin, 2015
ISBN 978-1489999658 (pb)

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Reviewed by Iris Lorscheid
Hamburg University of Technology, Germany

Cover of book The book Simulation and Learning by Franco Landriscina stresses the value of simulation for instruction and teaching. The valuable combination of “simulation and learning” was already acknowledged by the developers of NetLogo (Wilensky, 1999), who built the modelling environment to be a pedagogical tool. NetLogo has since grown to become the most commonly used modelling tool in the social simulation community. The motivation was to enable users to learn and teach by exploring model behaviour through simulation. The book addresses exactly this value of simulation. However, the book by Landriscina goes beyond. The book discusses an epistemic view on simulation and its connections to related topics like mental models, cognitive artefacts, and relevant theories such as embodied or distributed cognition. As a result, the author presents and explains the favourable properties of simulation, which makes simulation what it is to simulation researchers: A medium to think and learn in systems. Franco Landriscina provides us with explanations from a cognitive perspective for why this works so well.

The introduction addresses the interdisciplinary character of simulation and thus the existing variation of perceptions of the simulation paradigm. Given this, the book identifies and then incorporates contributions from cognitive science, philosophy of science, instructional science, and modelling and simulation.

The second chapter (Simulation and Cognition) introduces the reader to the cognitive science perspective on simulation. The author sets the focus on the discussion of mental models as individual cognitive representations. Additionally, existing approaches to simulate mental models and cognition are in focus, developed in particular from embodied cognition theories.

The third chapter (Models Everywhere) addresses the epistemic role and value of modelling. In particular, the pragmatic perspective and the cybernetic perspective on models are discussed. On the one hand, the pragmatic perspective addresses the mediating role of models between theory and reality, while the cybernetic perspective refers to dynamic systems and a feedback from results of the model to results of the system. Finally, general model theories are discussed, including the context of model construction (Models: Of what?, For what?, For whom?, and When?).

Chapter four (Simulation Modeling) includes the modelling and simulation perspective in consideration of simulation learning. Here, the author connects to the conceptual model framework by Robinson (2014). This chapter describes prominent scientific modelling paradigms (equation-based models, molecular dynamics), and simulation paradigms (agent-based modelling, system dynamics, and cellular modelling and simulation), and explains those by prominent studies in the areas. In the end, a comparison reveals the components of systems and how they work under these paradigms.

After the first introduction chapters laid the theoretical foundations, the core chapters of the book follow. These parts address the topics: simulation learning (Chapter 5), simulation reasoning (Chapter 6), and the epistemic cycle of simulation learning (Chapter 7).

Chapter five (Simulation-Based Learning) emphasises the value of simulation for learning not only by using the model, but also by creating a model. Landriscina stresses how the creation process supports an understanding of models being representations of systems. Without the experience of creating, one may not distinguish between model elements being necessary for implementation and those being part of the system simplification defined by the conceptual model. Overall, the book claims that simulation models are “cognitively opaque”. Construction, or at least tracing the development process, may clarify this perception.

Also, the author introduces a close relationship between the scientific field of model-based learning on the one hand and simulation-based learning on the other. This goes along with the idea of using simulation to explicate thought experiments (Jaccard & Jacoby, 2009). Simulation may support the process of developing mental (target) models by testing the results of (initial) mental models in simulations.

Next, the book stresses cognitive load. Too many or chaotic arranged elements in the interface design may lead to cognitive overload. Thus, this is another important aspect for simulation learning.

Chapter six (Simulations for Thinking) discusses the role of computer programs as cognitive artefacts in the reasoning process. In line with distributed cognition theory and embodied cognition theory, external factors are important elements of reasoning. In according to this, the book elaborates how simulation may be a cognitive extension and thus all forms of simulations are cognitive artefacts.

Finally, Chapter seven (Simulation-Based Instructions) introduces the epistemic cycle. First, a list of scientific concept types gives an overview of questions to address with simulation teaching (e.g., patterns, mechanisms, flows, stability and change). This list may be a helpful classification also for research questions in simulation research. The epistemic cycle then describes how students may acquire knowledge in an instructional context. The cycle contains the elements reality, system, model, and simulation. The described process can be related to the simulation modelling cycle by Grimm and Railsback (2005), or the logic of simulation by Gilbert and Troitzsch (2005). The described approach supports and explicates the iterative character of simulation research. A feedback loop from simulation to model and system allows a revision of the mental model and the components of the explanatory model. This goes along with the construction-revision-cycle in mental model-based reasoning. The cycle demonstrates how simulation can make an important contribution to reality by identifying new perspectives, properties, or relationships that were otherwise not connected. In this final chapter of the book I missed a discussion about the role of simulation experiments and analysis for the understanding of models and systems.

The book is rich in content while on the first view small in pages (209 pages without references). Overall, I recommend the book to readers who would like to learn about epistemic perspectives on simulation (e.g., relation of model, system, and reality), and cognitive perspectives on learning and reasoning processes by simulation and modelling (e.g., mental models, cognitive artefacts). The book is not about hands-on pedagogical concepts of how to use simulation in concrete teaching scenarios, as one may expect. Landriscina describes how to create knowledge with simulation at an epistemic level, from a cognitive and philosophy of science point of view. Thus, the book can be interesting for simulation researchers and the communication of simulation research.

* References

GILBERT, N., & Troitzsch, K. G. (2005). Simulation for the social scientist (2 ed.). New York, USA: Open University Press.

GRIMM, V., & Railsback, S. F. (2005). Individual-based modeling and ecology. Princeton: Princeton University Press.

JACCARD, J., & Jacoby, J. (2009). Theory Construction and Model-Building Skills: A Practical Guide for Social Scientists. New York, USA: The Guilford Press.

ROBINSON, S. (2014). Simulation: the practice of model development and use. Basingstoke: Palgrave Macmillan.

WILENSKY, U. (1999). NetLogo. Northwestern University, Evanston, IL: Center for Connected Learning and Computer-Based Modeling. Retrieved from http://ccl.northwestern.edu/netlogo/


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