Please join us this Wednesday at Santa Fe Complex at 11:30a for a talk
by Kenneth Lloyd on "Network graph formalism for the study of complex
systems"
After Ken's talk, we'll meander over to El Tesoro Restaurant for
lunch...
ABSTRACT:
I will introduce the foundation concepts for
a methodology proven useful in developing products and processes of
dynamically evolving, large-
scale complex systems (DELCS). The methodology is based upon a
mathematical formalism using
hybrid, network graphs that underly most formal modeling languages
such as the UML, SysML or
Petri nets. Having identified incompleteness in Traditional Systems
Engineering’s (TSE) historically
reductionist, machine-model approach, this methodology represents an
alternative form of systems
engineering using phases of complexification and simplification in
ameliorating many problematic
effects inherent in the design of complex systems.
While it may seem paradoxical, we specifically utilize these
complexity characteristics as enabling
agents providing in large-scale systems, and methods that add
complexity have historically been
considered antithetical to the practice of systems engineering,
therefore avoided. We term this new
domain Complex Systems Engineering (CSE), and our methodology WattSys.
The guiding principles for the foundations of WattSys are:
1. To model the effects of non-equilibrium system thermodynamics, upon
structures of energy,
information, entropy, space and time.
2. To consider both temporal dynamics and state models through
dynamical architecture.
3. To facilitate better congruence with scientific foundations.
4. To facilitate reduction in risk from scientific uncertainty.
5. To provide better navigation, visualization, simulation and
ultimately a better understanding of
systems through models and data.
The method’s foundation originated in the domains of systems and
software engineering, but are
reified1 through concepts in complexity theory and complexity science
as extended from quantum
physics, thermodynamics and statistical mechanics, graph theory and
inverse theory. These are
implemented as heterotic network models embedded in n-dimensional
context manifolds extended
in temporal dimensions. The network graphs serve as knowledge models
that are used to encode,
describe and report information for analysis, to simulate behavior,
and to provide insight into
alternative patterns. Specifically, the methodology searches the large-
scale networks for small-
world properties, using multiple dimensions of self-similarity in
discovering navigational paths and
distances. It ‘simplifies’ complexity, not by reduction but
through resolution by adding these
discoveries as small functional parameters into the network structure
genotype. Therefore it may
be described a complex meta-system that replicates and evolves complex
system models using
evolutionary genetic algorithms and historical information in the form
of data.
It is proposed that these techniques may be utilized for engineering
such diverse complex dynamical
systems as large-scale software systems, collaboration networks,
internet fact webs, commercial
enterprises, the national defense, and even ad hoc teams within these
organizations. It is proposed
better results will be seen compared with using either TSE, game
theory, forms of regulated self-
organization, highly optimized tolerances, or negotiated group
consensus, individually.
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FRIAM Applied Complexity Group listserv
Meets Fridays 9a-11:30 at cafe at St. John's College
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