Decision making (DM) is an essential component of the problem-solution at
many levels -
from decisions at international level (e.g. aimed at securing nuclear
safety or preventing flood damage), to individual organisations (e.g.
increasing productivity or improving urban traffic), systems (e.g.
process or robot control, fault detection, medical diagnostics) and to
the level of individual human
multiple personal aims in their changing environments.
man-made complex system is composed of DM units called participants.
Participants can he machines, groups of humans or
Attempts to optimise centrally the overall performance of a collection of
mutually interacting participants soon reach complexity barrier that
allows performance improvements only at unacceptable costs. Use of
sophisticated distributed or multiple-participant DM methodologies is then
an only viable way towards desirable high efficiency. Excellent particular
variants exist that overcome the
complexity barrier by exploiting specificity of their application domains.
None of them is yet able to serve as a common domain-independent
pattern and a real need for theory of multiple-participant DM persists.
This book brings together contributions of experts of
different backgrounds who inspect various aspects of the problem, push the
the knowledge towards the dreamt-of theory and open a range of questions
to be addressed. At least the last item makes this collection worth of
About the Author
represent two generation of researchers of Department of Adaptive Systems
of the Institute of Information Theory and
Automation, Academy of Sciences of the
Czech Republic. All of them got their degrees Ing. (MSc) from Czech
University in Prague. Josef
Andry'sek graduated in Software Engineering. His research is focused on
recursive estimation of high-dimensional finite
probabilistic mixtures that serve as universal approximation of non-linear
Miroslav Karny graduated in Theoretical Cybernetics and
received his CSc (PhD) and DrSc degrees from Czechoslovak Academy
of Sciences both in Technical
Cybernetics. His research interests cover various theoretical, algorithmic
and application aspects of
dynamic decision-making under uncertainty. Adaptive advising and control
based on recursively estimated finite dynamic probabilistic
mixtures and their fully probabilistic optimisation dominate his current
Jan Kracik graduated in
Mathematical Modelling. His research on fair governing led him to
inspection of combining knowledge and aims in