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How could we approach these problems from a nonlinear dynamical systems
point of view? The first step certainly has to be a systems analysis.
Much know-how in this sector has been developed in the context of systems
dynamics, although there the analysis is guided by the restricted
mathematical methods and certainly has to be improved and generalized.
Guiding questions in that sense might be:
- ``Who are the major actors in this crisis?''
- ``How are these actors influencing each other?''
- ``What are external parameters that influence the decisions of
the actors?''
Answering those questions and representing their answers in some structured
form (graphical or symbolic) would constitute, what we would call a conceptual
model. Already at this conceptual level, recent progress in object oriented
graphical user interfaces (GUIs) such as the Diagram tool described below,
might provide assistance in a better understanding of the complexity of an
evolving interacting system. Once this conceptual model has been established,
often (but not always) it is straightforward to find quantitative variables
to map this conceptual model onto a computational model.
We can summarize: Decision makers will always have some model of the system of
interest to assist reasoning during a crisis. Two (conflicting) factors
contribute to the type of models chosen:
- (i)
- conceptual models use intuitive reasoning based on experience.
They typically abstract the most relevant features from a sea of redundant
information. They are very flexible to adapt to unforeseen changes and
generalize easily to qualitatively new situations.
- (ii)
- Traditional analytical models try to anticipate as many factors as
possible in as many detail as possible. Thereby they become very inflexible
and practically useless in surprise situations. These models are typically
not very adaptive.
Chaos theory suggests the use of models with local, short term predictability:
Adaptability requires
- simple, low-dimensional models which should be intuitive
- fast and direct access to and integration of current, global data
- multimedia user interface for efficient representation of the results
- global sensitivity analysis and identification of crisis domains
- efficient individual archiving and retrieval system
A efficient simulation environment would provide an object oriented integration
of all of the elements described above:
- conceptualization of complex developments without strict formalization
- access to global information systems that allow global estimation of
parameters for low-dimensional chaotic models with global scan of scenarios
- links to detailed simulation systems where data and quantification
requirements are satisfied.
In the remaining sections we want to give a brief description of some elements
that might illustrate some of the points discussed above.
Next: Adaptive Control and
Up: Messy Futures and Global
Previous: How can Chaos
Gottfried Mayer-Kress
Sat Apr 22 21:04:59 MDT 1995