Re: REQUEST: Integrating complexity science

[FVoehl@aol.com]

In a message dated 12/1/99 4:18:58 AM, Henny@hawaii.rr.com wrote:

<<I am a graduate nuring student presently working in the healthcare industry 
in quality management and am wondering if anyone has any experience 
integrating complexity science or the the complexity advantage with TQM and 
how that was done and what the results were. >>

The study of "Complex organizations" and complexity science has been an 
important arena in Internet postings and organizational studies for years.  
Historically, organizational scholars have examined:
1- vertical complexity (the number of levels in a hierarchy), 
2- horizontal complexity (the number of differentiated departments), and 
3- spatial complexity (the geographic dispersion of organizational subunits).

Organizational environments have also been characterized as more or less
complex depending on how heterogeneous and dispersed resources are within
them. However, a different view of complexity is emerging that may have
important implications for DENizen scholarship and TQM research.  Within the 
past decade, interest in the "sciences of complexity" has increased 
dramatically. 

The study of complex system dynamics has perhaps progressed furthest in the 
natural sciences, but it is also beginning to penetrate the social sciences.  
This interdisciplinary field of study is still pre-paradigmatic, and it 
embraces a wide variety of approaches.  Although it is not yet clear whether 
a genuine science of complexity will emerge, it does seem clear that scholars 
in a variety of fields are viewing complexity in a different way than 
organizational scholars traditionally have. 

A number of findings now seem fairly well-established, including the
following: 
* Many dynamic systems do not reach an equilibrium (either a fixed
  point or a cyclical equilibrium).
* Processes that appear to be random may actually be chaotic, in
  other words may revolve around identifiable types of "strange attractors."
  Tests exist that can detect whether apparently random processes are
  in fact chaotic.
* Two entities with very similar initial states can follow radically
  divergent paths over time.  The behavior of complex processes can be
  quite sensitive to small differences in initial conditions.  This can
  lead to highly path-dependent behavior, and historical accidents may
  "tip" outcomes strongly in a particular direction.
* Very complex patterns can arise from the interaction of agents
  following relatively simple rules.  These patterns are "emergent"
  in the sense that new properties appear at each level in a hierarchy.
* Complex systems may resist reductionist analyses.  In other words,
  it may not be possible to describe some systems simply by holding
  some of their subsystems constant in order to study other subsystems.
* Time series that appear to be random walks may actually be fractals
  with self-reinforcing trends.  In such cases we may observe a "hand
  of the past" in operation.
* Complex systems may tend to exhibit "self-organizing" behavior.
  Starting in a random state, they may naturally evolve toward order
  instead of disorder.

The most interesting research into complex systems sheds fresh light on
nonlinear dynamics, which usually evolve from interactions among agents.
Organizational scholars seldom come to grips with nonlinear phenomena.
Instead, we tend to model phenomena as if they were linear in order to
make them tractable, and we tend to model aggregate behavior as if it is
produced by individual entities which all exhibit average behavior.

Understanding complex processes may also require a shift from studying
sequential processes to studying simultaneous or parallel processes.  At
this juncture, organizational researchers have few templates that suggest
to them how to hypothesize about or model such behavior.  Researchers have
suggested that it is difficult to know how to draw a conceptual model and how
to report the results of empirical inquiries into complex organizational
phenomena.  The special issue aims to provide scholars with useful templates
to follow when analyzing complex processes that involve organizations. 

Although studies of complex systems in other disciplines are often very
sophisticated technically, the DEN has not made much of an effort to bridge
the teachings of Dr. Deming with Complexity Theory.  Examples of appropriate
topics might include, but certainly are not limited to, the following: 

* Research that specifies plausible sources of hidden order in apparently
  random processes that occur within or among organizations.  Can we
  illuminate how that which appears random is actually ordered but in
  complex ways?  Such insights are particularly interesting if they
  generate testable implications.
* Research that explains how simple organizational processes become
  complex ones.  At what point do behaviors that are individually
  well-understood interact in ways that create difficult-to-understand
  aggregate outcomes?  What are the consequences of rising complexity
  in this sense?
* Research that compares several plausible rule sets for a group of
 interacting agents, and shows that behavior we observe in organiza-
 tions can be produced by one model of interactions but not by others.
* Research that simultaneously explores processes unfolding across
  multiple layers of context (e.g., economies, industries, and firms).
  What dynamics distinguish such processes, and how do they interrelate?
* Research addressing such topics as innovation and change, power and
  conflict, or crisis and reorientation may be particularly appropriate
  for the special issue.

A list of useful books and articles addressing complexity theory may be
found at the INFORMS College on Organization Science's Word Wide Web site:
http://www.stern.nyu.edu/informs/ Prospective contributors wishing further
information may contact Philip Anderson (philip.anderson@dartmouth.edu),
Kathleen Carley (Kathleen.Carley@CENTRO.SOAR.CS.CMU.EDU), Kathleen Eisenhardt 
(ng.kat@forsythe.stanford.edu), Alan Meyer (ameyer@oregon.uoregon.edu), or 
Andrew Pettigrew (ccscap@wbs.warwick.ac.uk). 

The above data is a compilation of many posts that I have been following over
the past two years, so it is not possible to give proper attribution to the 
exact
nature of the sources.  I hope this helps to answer your question.

Frank Voehl (FVoehl@aol.com)
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