Better Decision Making in Complex, Dynamic Tasks: Training with HumanFacilitated Interactive Learning Environments

256 Pages·2016·6.84 MB·English

by  QudratUllah H.

Understanding Complex Systems
Hassan Qudrat-Ullah
Better Decision
Making in
Complex,
Dynamic Tasks
Training with Human-Facilitated
Interactive Learning Environments
Understanding Complex Systems
Founding Editor
Prof. Dr. J. A. Scott Kelso
Center for Complex Systems and Brain Sciences
Florida Atlantic University
Boca Raton FL, USA
E-mail: [email protected]
Editorial and Programme Advisory Board
Henry Abarbanel
Department of Physics and Marine Physical Laboratory (Scripps Institution of Oceanography),
University of California, San Diego, La Jolla, CA 92903, USA
Dan Braha
New England Complex Systems, Institute and University of Massachusetts, Dartmouth
Péter Érdi
Center for Complex Systems Studies, Kalamazoo College, USA and Hungarian Academy of Sciences,
Budapest, Hungary
Karl Friston
Institute of Cognitive Neuroscience, University College London, London, UK
Hermann Haken
Center of Synergetics, University of Stuttgart, Stuttgart, Germany
Viktor Jirsa
Centre National de la Recherche Scientifique (CNRS), Université de la Méditerranée, Marseille, France
Janusz Kacprzyk
System Research, Polish Academy of Sciences, Warsaw, Poland
Kunihiko Kaneko
Research Center for Complex Systems Biology, The University of Tokyo, Tokyo, Japan
Scott Kelso
Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, USA
Markus Kirkilionis
Mathematics Institute and Centre for Complex Systems, University of Warwick, Coventry, UK
Jürgen Kurths
Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany
Andrzej Nowak
Department of Psychology, Warsaw University, Poland
Linda Reichl
Center for Complex Quantum Systems, University of Texas, Austin, USA
Peter Schuster
Theoretical Chemistry and Structural Biology, University of Vienna, Vienna, Austria
Frank Schweitzer
System Design, ETH Zürich, Zürich, Switzerland
Didier Sornette
Entrepreneurial Risk, ETH Zürich, Zürich, Switzerland
Stefan Thurner
Medical University of Vienna, Vienna, Austria
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Understanding Complex Systems
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Hassan Qudrat-Ullah
Better Decision Making in
Complex, Dynamic Tasks
Training with Human-Facilitated
Interactive Learning Environments
1 3
Hassan Qudrat-Ullah
School of Administrative Studies
York University
Toronto, ON
Canada
ISSN 1860-0832 ISSN 1860-0840 (electronic)
ISBN 978-3-319-07985-1 ISBN 978-3-319-07986-8 (eBook)
DOI 10.1007/978-3-319-07986-8
Springer Cham Heidelberg New York Dordrecht London
Library of Congress Control Number: 2014942056
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For
Muhammad
(Peace be upon him)
(571 AD–632 AD)
The best decision-maker
Foreword
Building system dynamics models and experimenting with them is widely seen
as an excellent way to get new insights into problems that should lead to better
decisions. Does that really happen? Indeed much anecdotal evidence supports an
affirmative response to that question, but there are few scientific studies that have
attempted to test that hypothesis. Hassan Qudrat-Ullah in this book revisits this
question through well-designed experiments with human subjects. Such experi-
mentation was a part of his doctoral work at the National University of Singapore,
which he has continued in his faculty role at York University, Canada. His exper-
imental work using human subjects and serious games seeks scientific evidence
for judging the efficacy of system dynamics-based serious games that are imple-
mented as interactive learning environments or ILEs.
This book, however, goes beyond merely describing his hypotheses and their
tests. It carefully describes also the interacting learning environments and the sys-
tems concepts underlying them. It is thus a stand-alone volume that explains the
interactive learning environments, how they can be used to improve decision-making
processes, and how the improvements they create are scienticfi ally veriefi d. It is a
must read both for system dynamics practitioners and those seeking evidence for the
efcfi acy of its use in decision-making.
April 2014 Khalid Saeed
vii
Preface
The computer simulation-based education and training is a multibillion dollar
industry. With the impressive advancements in technology, and the increased com-
plexity of organizational decision-making (e.g., due to globalized nature of busi-
nesses), the projected demand for “decisional aids” is on the rise.
However, when it comes to people’s training for decision-making in “dynamic
tasks,” effectiveness of computer simulation-based learning environments (ILEs) is
limited at best. A large of body of empirical research indicates that during sessions
with ILEs, people perform poorly on “dynamic tasks.” They do not develop adequate
models of the task system. Instead, they continue to have a simpliefi d, linear view of
the task system. They lack the fundamental understanding about the dynamic task
system that the structure of the task system is responsible for the behavior of the task
system (e.g., there exists a time lag between “decisions” and their effects; the rela-
tionships between various variables of the task system are nonlinear). Without such
understanding of the dynamic task system, the likelihood of any signicfi ant gain
(say, in transferable skills) through the use of ILEs is questionable.
This book introduces you to system dynamics-based serious games that are
implemented as human-facilitated ILEs. The key objective of this book is to
enhance systematically your understanding of and provide you with insights into
the general process by which human-facilitated ILEs are effectively designed and
used in improving users’ decision-making in dynamic tasks.
Unique Approach of the Book
To realize the objective of this book, we performed the following three tasks:
First, we developed an integrated process model for better decision-making in
complex, dynamic tasks. The model was developed on the basis of an extensive
review of empirical research and practice of “dynamic decision-making.” With the
explicit incorporation of human facilitation in the design of an ILE, this model
facilitates the acquisition of multidimensional learning outcomes including the
understanding of the task structure, system behavior, and relationship between the
two—improving decision-making in dynamic tasks.
ix
x Preface
Second, to test the hypothesized process model, the laboratory experiments
with human subjects were conducted. The laboratory experiments provided rich
data on all the variables of the process model: prior knowledge, learning mode,
task strategies, task knowledge, task performance, and transfer learning.
Third, the process model was empirically tested to demonstrate the effectiveness
of human-facilitated ILEs in improving the subject’s capability to make better deci-
sions in complex, dynamic tasks.
Outline of the Book
This book is divided into four major parts. Part I serves as an introduction to the sub-
ject of “decision-making in dynamic tasks,” its importance, and its complexity. Part
II provides background material, drawing upon the relevant literature, for the devel-
opment of an integrated process model on the effectiveness of human-facilitated
ILEs in improving decision-making in dynamic tasks. Part III focuses on the design
and execution of laboratory experiments to gather empirical evidence for the valid-
ity of the process model. Finally, Part IV presents a comprehensive analysis of the
gathered data to provide a powerful basis for understanding important phenomena of
training with human-facilitated ILEs, thereby, helping to derive critical lessons to be
learned. To facilitate a solution-oriented approach, each chapter provides with “the
insights” that provide practical advice for the successful design, development, and
use of human-facilitated ILEs.
Intended Audience
This book provides the reader with both a comprehensive understanding of the
phenomena encountered in decision-making with human-facilitated ILEs and
a unique way of studying the effects of these phenomena on people’s ability to
make better decisions in complex, dynamic tasks. This book can be used by man-
agers and practitioners including policy makers, researchers, and students (both at
undergraduate and graduate levels) of dynamic decision-making.
For managers and practitioners, this book provides insight into the complex,
dynamic problems that they often encounter. For these readers, Part II, which
provides an extensive review of previous research, may be skipped initially so
that they can get directly to the process of evidence gathering (Part III), and the
important lessons learned (Part IV). Policy makers especially interested in health
care, energy systems, and education sectors can benefit from Chaps. 8 to 14.
Consultants who conduct education and training related activities can equally ben-
efit from the protocols developed in this volume.
Preface xi
For researchers and students, this book provides probably the most compre-
hensive study of decision-making and learning with human-facilitated ILEs. The
background material of Part II provides a solid base to understand and organize
the existing experimental research literature and approaches. The developed pro-
cess model on the effectiveness of human-facilitated ILEs in improving decision-
making in dynamic tasks, enables, for the first time, a systematic study of this
complex field. As a textbook on decision-making with ILEs, this book can be used
for a semester-based course, executive education, and self-study.
Toronto, April 2014 Hassan Qudrat-Ullah