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Subject: Re: [docbook-apps] DocBook and InDesign
On 6/16/2010 11:53 AM, David Link wrote: > On a slightly different note, what about roundtripping from DocBook > XML to HTML and back? Do people have experience with that, or know > any problems therein? As I've mentioned in my recent posts I'm a newb to DocBook and XSLT so my answer maybe somewhat incomplete and naive. The XSLT Cookbook (ISBN 9780596009748) has a chapter on XML to HTML. From my limited knowledge going from DocBook to HTML seems to be rather straigthforward and there are a number of stylesheets 'out there' to perform comprehensive Doc Book to HTML transformations. My preferred approach is to use CSS to format the (X)HTML and let the DocBook XML <-> XHTML round-trip transformation just deal with the content. What I've struggled with more is HTML (which is mostly flat) to DocBook (which is more hierarchic). I have attached my recent effort to develop a route from InDesign exported PDF to DocBook XML (I hope the attachments make it through the list management software). The process I have at this point is far too simplistic to be useful at present, but I'm hoping I can get into a state whereby I can downconvert several thousand InDesign exported PDFs to DocBook with relative ease. My process is: InDesign created PDF -> Acrobat exported XML -> PDFXML2HTML XSLT -> HTML2DBK XSLT -> DocBook XML It is far from ready to make use of (and is only ever going to work with my particular files as is), but the bit I found the most problematic was the transformation from HTML to DocBook because of the flat to nested hierarchy. Fortunately, XSLT 2.0 makes this much easier with the for-each-group command, and a XSLT snippet on pages 340-342 of Michael Kay's incredibly comprehensive XSLT 2.0 and XPath 2.0 4th Edition (ISBN 9780470192740) - I've barely scrapped the surface of the knowledge contained in this book! I'm still not using the templates properly yet as my transformation from <p> to <para> is wrong, the <body> tags are still there - if anyone can guide me on how to correct these problems that'd be great! The PDFXML2HTML XSLT is also not ready, but as this is a transformation from an already flat hierarchy (Acrobat generated XML) to another flat hierarchy (HTML) it really is nothing much more than a one-to-one mapping (I had to manually tweak the Eoyang_processed.xml - which is the output of the PDFXML2HTML XSLT because it doesn't quite do what I want yet). The flaws in my output files is just a reflection of my limited knowledge of XSLT, but I'm already excited about the possibilities a deeper understanding of XSLT is going to allow me to explore... so any guidance is very much appreciated. At least the final DocBook XML properly contains the nested <sectX> tags, which I was really struggling with... Bye for now, Kurt
<?xml version="1.0" encoding="UTF-8" ?>
<!-- Created from PDF via Acrobat SaveAsXML -->
<!-- Mapping Table version: 28-February-2003 -->
<TaggedPDF-doc>
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<?xpacket begin="" id="W5M0MpCehiHzreSzNTczkc9d"?>
<x:xmpmeta xmlns:x="adobe:ns:meta/"
x:xmptk="Adobe XMP Core 4.2.1-c041 52.342996, 2008/05/07-20:48:00 ">
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#";>
<rdf:Description rdf:about="" xmlns:xmp="http://ns.adobe.com/xap/1.0/";
xmlns:xmpGImg="http://ns.adobe.com/xap/1.0/g/img/";>
<xmp:CreateDate>2005-07-22T20:51:52-04:00</xmp:CreateDate>
<xmp:MetadataDate>2005-07-22T20:51:53-04:00</xmp:MetadataDate>
<xmp:ModifyDate>2005-07-22T20:51:53-04:00</xmp:ModifyDate>
<xmp:CreatorTool>Adobe InDesign CS2 (4.0)</xmp:CreatorTool>
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<_No_paragraph_style_>55</_No_paragraph_style_>
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<_No_paragraph_style_>56</_No_paragraph_style_>
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<Story>
<References>E:CO Vol. 6 Nos. 1-2 2004 pp. 55-60</References>
</Story>
<Story>
<References>Eoyang</References>
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<Figure>
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<References>The practitioner’s landscape</References>
<References>E:CO Special Double Issue Vol. 6 Nos. 1-2 2004 pp. 55-60</References>
</Story>
<Story>
<References>Practitioner</References>
</Story>
<Story>
<Paper_Title>The practitioner’s landscape</Paper_Title>
<Authors>Glenda H. Eoyang</Authors>
<Affilliations>Human Systems Dynamics Institute, Minnesota, US</Affilliations>
</Story>
<Story>
<Main_Text>An array of complexity-based tools and techniques are available today, but
how does the practitioner select a particular approach to respond to a particular
need? We present a simple taxonomy to describe the landscape of complexity-derived
methods for human systems dynamics. Practitioners can use the landscape to understand
the diversity of tools and techniques, to foster respect for approaches different
from ones’ own, to build an understanding of the field as a whole, and to select
specific techniques to apply in specific situations.</Main_Text>
<Main_Text/>
<heading_1>In the beginning...</heading_1>
<Main_Text>In the 1980s, when some of us first delved into applications of complexity to
human systems, there were no preformed models or even solid metaphors to guide us. We
dove into the science and mathematics of chaos and complexity, came up gasping for
breath, and put together the language, tools, and methods that we thought would be
most helpful for ourselves, our colleagues, and our clients. Though the deserted
landscape was lonely and intimidating sometimes, it left us free to explore
opportunities and to invent tools and techniques to meet the immediate needs as we
understood them at the time. It also allowed us to make low-risk mistakes, either in
our understanding of the science or in our expectations for its application to
real-life human systems. We were a creative bunch and generated an endless stream of
complexity-based inventions.</Main_Text>
<Main_Text/>
<heading_1>A rugged landscape</heading_1>
<Main_Text>Today, the landscape is different. Early adapters and inventors have passed
through this territory before. They’ve left a trail of methods, models, languages,
and expectations that are not always consistent within each approach and certainly
not coherent among the various approaches. Each explorer has synthesized his or her
experience, theoretical frameworks, and client’s needs to create tools and methods
that work in a given time and place. These creations have sometimes taken on lives of
their own - being codified and generalized to be applied in multiple situations. What
used to be a desert is now a rugged landscape of tools and techniques to help apply
principles of complexity science to the challenges that plague individuals,
institutions, and communities today. This emerging landscape of human systems
dynamics tools and techniques includes:</Main_Text>
<Main_Text/>
<List_Bullet>15% Solution (Morgan, 1997)</List_Bullet>
<List_Bullet>Complex responsive processes (Stacey, 2001)</List_Bullet>
<List_Bullet>Self-organizing leadership (Knowles, 2002)</List_Bullet>
<List_Bullet>Difference questioning (Goldstein, 1994)</List_Bullet>
<List_Bullet>Metaphorical landscapes (Lissack & Roos, 1999)</List_Bullet>
<List_Bullet>Difference Matrix (Olson & Eoyang, 2001)</List_Bullet>
<List_Bullet>Generative Relationship STAR (Zimmerman, et al., 2001)</List_Bullet>
<Main_Text/>
<Main_Text>And many, many more.</Main_Text>
<heading_1/>
<heading_1>Making sense of the pattern</heading_1>
<Main_Text>The accumulation of complexity-based techniques makes it possible for
practitioners to address the complex adaptive nature of human systems without a deep
understanding of the nonlinear dynamics that drive emergent patterns of behavior.
More people can use dynamical approaches to increase the power and (usually) the
efficacy of their interventions. On the other hand, the library of powerful tools can
quickly become a graveyard of irrelevant approaches.</Main_Text>
<Main_Text/>
<Main_Text> Given this complex, rugged landscape of complexity-related tools and
techniques, how does a practitioner select which of the many complexity-based tools
to use in a particular situation?</Main_Text>
<Main_Text/>
<heading_1>Differences that make a difference</heading_1>
<Main_Text>We propose a two-dimensional, twelve category classification system that
represents the landscape of the work of practitioners in human systems
dynamics.</Main_Text>
<Main_Text/>
<Main_Text> This approach clusters available tools and techniques into groups to help a
practitioner think about the many options and select the approach that constitutes a
‘best fit’ for a given consulting challenge. This taxonomy is based on the two
fundamental ‘differences that make a difference’ to practitioners when they engage
with client systems to recognize and influence emergent patterns of complex dynamical
interactions.</Main_Text>
<Main_Text/>
<Main_Text> The first dimension deals with the phenomenon of interest. How are the
relevant patterns exhibited in the situation? Of course a conversation between client
and consultant is required to determine the relevant patterns. During this
conversation the practitioner learns where the challenge lies in relation to these
three broad phenomenological categories. </Main_Text>
<Main_Text/>
<Main_Text>Phenomena category 1: Surface structures. In some situations, the client
invites the consultant to deal with patterns that are evident to any observer.
Interpersonal conflicts, lagging sales, customer dissatisfaction are examples of
patterns that arise in such surface structures. They are immediately evident to
observers of and participants in the human system. We call these ‘surface structures’
because they are patterns that emerge in the most evident facets of our human
systems. </Main_Text>
<Main_Text/>
<Main_Text>Phenomena category 2: Evident deep structures. Other times, the presenting
problem is opaque to the client. He or she describes a sense of discomfort with
people or dissatisfaction with processes and products. Though the pattern may not be
evident to the client, the tools and discipline of a human systems dynamics
professional allow the practitioner to discern and describe the patterns in ways that
the client understands and recognizes as accurate. We call these ‘evident deep
structures’. These patterns emerge from dynamics embedded deep within the system, but
they become evident through applications of fairly accessible tools and techniques. </Main_Text>
<Main_Text/>
<Main_Text>Phenomena category 3: Subtle deep structures. Finally, some situations
exhibit emergent patterns that are both subtle and deep. Neither the client’s native
instincts nor the tools that function as simple cognitive filters can articulate a
coherent pattern of relationships in the complex dynamics of the system. The
complexity of these situations transcends the capacity of one level of complexity
tool and demands more subtle and/or complicated methods and models. Hospital error
rates and other complex phenomena where data points are distributed across time
(e.g., 1/f ‘pink’ noise phenomena) are examples of deep structural patterns that
require more subtle analysis techniques.</Main_Text>
<Main_Text/>
<Main_Text> Each of these levels of subtly and depth requires a different set of
complexity tools and techniques, and an adept practitioner will be able to assess the
environment and select approaches that fit the phenomena of interest and the
complexity of the patterns emergent in the client system.</Main_Text>
<Main_Text/>
<Main_Text> The second dimension that defines the categories of the Practice Landscape
deals not with the situation itself but with the tools that are chosen for
understanding and intervention. In short, these are ways that the situation can be
represented by the client and the consultant. They are epistemological distinctions,
and they determine how the team thinks and talks about the patterns that emerge from
the dynamics of the situation. We define four different categories along a continuum
from least abstract (practice) to most abstract (mathematics) tools for understanding
and intervention. The four are described below.</Main_Text>
<Main_Text/>
<Main_Text>Tools category 1: Practice. It is possible to respond to quite complex
dynamics immediately - without aid of language or conscious analysis. Some
complexity-based approaches are defined to support just such practice-oriented
response. Many clients are perfectly satisfied with options for action that do not
come along with complicated explanations, strange language, or sophisticated
justifications. They ask, “Did it work?” If the answer is, “Yes,” that is all they
need to know. Such tools were not accessible in traditional organization development
or management practice because explanations or interventions were expected to predict
their own success. The unpredictability of complex adaptive systems removes this
requirement and makes accessible to study a whole new domain of intuition- and
practice-based interventions that can only be assessed in retrospect.</Main_Text>
<Main_Text/>
<Main_Text>Tools category 2: Descriptive metaphors. Complexity science is full of rich
and engaging metaphors. Butterfly effects, attractors, fractals, edge of chaos - they
are poetic and easily accessible terms for the lay person. They can also be
meaningful descriptors of patterns that emerge from human systems dynamics. We refer
to these as ‘descriptive’ metaphors not because they are less valuable than ‘dynamic’
ones, but because the application requires a less literal interpretation of the
mathematical complexity concept as it is applied to social systems. Using descriptive
metaphors, one can think about how ‘butterfly effects’ name patterns that appear
commonly in human systems. For example, the descriptive metaphor can represent small
deviations in team procedure that may generate a major shift in direction. Such
descriptive applications of the complexity concepts can help build shared mental
models, even when sensitive dependence on initial conditions cannot be measured or
proven in any formal way.</Main_Text>
<Main_Text/>
<Main_Text>Tools category 3: Dynamic metaphors. Moving up the scale of abstraction and
toward quantitative methods, we reach the tools category of dynamic metaphors. Here
we encounter methods of qualitative analysis, but ones that hold more closely to the
literal interpretation of the complexity metaphors. Rather than just a superficial
isomorphism with patterns of complex adaptive or deterministic chaotic systems,
dynamic metaphors focus on similarities between the underlying dynamics of the human
system and other nonlinear dynamical systems.</Main_Text>
<Main_Text/>
<Main_Text>Tools category 4: Mathematics. The most abstract of the ways to understand
and intervene are those that derive from mathematics. Quantitative languages are much
more formal and less ambiguous than metaphorical or practice approaches. This does
not make them better, but it does make them more precise. Only the practitioner in a
specific environment can choose whether the situation, client needs, and resources
warrant investment in mathematical analyses and interventions.</Main_Text>
<Main_Text/>
<Main_Text> These three phenomenological and four epistemological categories define
twelve clusters of complexity-inspired interventions. Table 1 defines and gives an
example of each category. We will not attempt a definitive categorization of each of
the many complexity and human systems approaches because that is beyond the scope of
this paper. We do believe, however, that this rubric can help practitioners select an
appropriate suite of complexity tools based on the immediacy of the emergent pattern
and the desire of the client and consultant for precision of understanding and
planned intervention.</Main_Text>
<heading_1/>
<heading_1>Applications in practice</heading_1>
<Main_Text>The twelve areas represented on the landscape provide ways to categorize the
many options for working with and within complex human systems. Each cell represents
a class of approaches that can be used to understand and influence complex human
dynamics. Table 1 also gives an example of an approach that fits each of the
locations on the Practice Landscape. These examples are provided merely to help
explain the options that the Landscape describes. Any one of the areas could include
a large number of other interventions or approaches. These examples should help
explain the structure and function of the Practice Landscape. The following sets of
examples demonstrate how a practitioner might use each of the tools categories might
be applied within each of the phenomenological categories.</Main_Text>
<Main_Text/>
<Main_Text> Some phenomena in complex adaptive systems are obvious even to the casual
observer. These are the surface structures that appear across the first row of the
Practice Landscape. For a variety of reasons, practitioners might choose to focus on
these phenomena rather than the more subtle patterns that emerge in self-organizing
systems. A practitioner might take this path when a client is new to the field and
somewhat skeptical, or when time is short and dynamics are particularly disruptive.
Even when focusing on these obvious patterns, options for complexity-inspired
interventions are many. Gareth Morgan’s 15% solution (Zimmerman, 2001) encourages one
to take action and observe how that action influences emergent patterns over time.
Another option is to name the obvious pattern of behavior using one of the beautiful
and descriptive metaphors of complexity, such as the butterfly effect (Wheatley,
1992). Moving beyond the language, there are interventions that can shape intervening
action when the metaphors of complexity are taken somewhat more literally. Coupling
(Eoyang, 1997) is an example of using the relationships of complexity to shape not
only descriptions but decisions in a dynamical human system. Finally, complex
dynamics can be captured in simple mathematics when measures, such as the Balanced
Scorecard (Kaplan & Norton, 1996), are used to track mutually causal factors in a
complex and adaptive system. So, a wide range of options (from action to mathematics)
is available when a practitioner needs or wants to influence the superficial
structures that emerge in a complex system.</Main_Text>
<Main_Text/>
<Main_Text> Right below the surface in human systems dynamics are patterns that might be
missed by the casual observer. These patterns, called evident deep structures, can be
accessible to the ‘naked’ eye, but they require training and heightened sensitivity
to discern the patterns as they emerge. Some clients and many human systems dynamics
professionals are trained to see these patterns as they emerge. Various tools help
articulate and translate these patterns into meaningful action. In terms of practice,
reflection is a method that uncovers patterns that otherwise would be hidden from
view. Practitioners use a variety of reflective activities from journaling to guided
imagery to help people see emergent patterns in their human systems. Moving to the
descriptive metaphorical ways of understanding and action, many metaphors can be used
to represent these patterns as they emerge. One often used (and sometime misused)
metaphor is the strange attractor. ‘Attractor’ presents the image of emergent
behavior that has a finite bound and infinite variability within the bound. This
language can help a group be aware of and use its inherent patterns of behavior. The
next group of tools, dynamic metaphors, can shape shared action in a group as they
become aware of their own emerging patterns. Future Search (Weisbord & Janoff,
2000) is an example of an approach that uses the evident deep structures of a
dynamical human system (such as sensitive dependence on initial conditions,
self-similarity, coupling, and mutual causality) to establish conditions for
organizational transformation. Finally, the mathematics of network analysis
(Barabási, 2002) can make the invisible visible to a group of people seeking to
understand their shared dynamics. So, each category of tool, from unspoken practice
through descriptive and dynamic metaphors and to mathematics, can be used to help
articulate the deep structures of human dynamics that are accessible to trained
observers.</Main_Text>
<Main_Text/>
<Main_Text> The third, and final, level of phenomena involves those patterns that cannot
be directly observed, even by trained observers. This level is called subtle deep
structures. Depending on the dimensionality of the system and/or its stage of
evolution, some complex adaptive systems evince patterns that are so deeply ingrained
and so subtle that they cannot be seen without special tools and techniques.
Intuition is a practice tool that accesses these subtle structures. Some gifted
individuals can sense a ‘subtle realm’ when it is inaccessible to others or even to a
conscious investigation by the intuitive. Open Space Technology (Owen, 2004), a large
group meeting facilitation technique, uses the dynamics of complexity to build
system-wide patterns of understanding. Open Space depends on simple rules that define
the underlying patterns of individual and group behavior, so it gives names for the
deep and subtle structures that drive the dynamics of human systems. Computer
simulation models generate even stronger metaphors for invisible patterns in human
systems dynamics. By representing the systems’ interactions and emergent patterns,
the simulation can make visible the deep, subtle patterns that emerge from complex
interactions. Finally, these subtle patterns can be uncovered by complex mathematical
analyses, such as nonlinear time series analysis (Kaplan & Glass, 1995). These
different types of tools can be used to discover, describe, and influence the deep
structures and patterns of behavior that emerge in complex human systems.</Main_Text>
<Main_Text/>
<Main_Text> These twelve categories of practice, defined by the object of focus and the
tools of investigation, provide a rubric to help a practitioner understand the wide
variety of complexity-based approaches and to select the one that is most appropriate
for a given situation. Armed with this understanding, the practitioner can select the
approach that best fits the needs and opportunities of the situation and the
moment.</Main_Text>
<heading_1/>
<heading_1>Benefits of the practice landscape</heading_1>
<Main_Text>When one is faced with the multitude of complexity-inspired approaches, the
Practice Landscape can provide a variety of benefits. Choices are simplified without
restricting options. When a situation is viewed through this landscape, practitioners
have two choices to make. One can view more or less subtle patterns with more or less
abstract tools. Focusing on these two variables, a practitioner can focus in on a
small subset of tools and approaches that might meet the immediate need.</Main_Text>
<Main_Text/>
<Main_Text> All options are equally valid. No one part of the landscape is by nature
superior to another. In some circumstances you need to deal with the patterns that
are already seen by everyone in a group. Sometimes you need to practice your insights
about complexity without using the language. In other situations you may be able to
use the mathematical tools of complex adaptive systems to demonstrate subtle and
surprising dynamics. No place on the landscape is any less useful or true than any
other. The only question is, “Which of the options fits your practice environment at
a particular place or time?” </Main_Text>
<Main_Text> New approaches can be envisioned that take a known approach from one domain
and finds ways to apply it in another. Likewise, this set of categories can be a
framework for personal development as a practitioner recognizes his or her strengths
and works to overcome personal weaknesses.</Main_Text>
<Main_Text/>
<Main_Text> A group of colleagues can use the Practice Landscape to support a planning
process. It provides a shared language that acknowledges the power of multiple
perspectives while providing meaningful distinctions and criteria for shared
decisions. </Main_Text>
<Main_Text> </Main_Text>
<heading_1>Challenges to the neatness of the </heading_1>
<heading_1>landscape</heading_1>
<Main_Text>It would be nice to believe that the Practice Landscape provides unambiguous
order for the messy collection of practices in human systems dynamics. This is not
the case. Like most models, this gives one some level of meaning and leaves other
questions unanswered. Some questions for future study include:</Main_Text>
<Main_Text/>
<Main_Text>Can subtle deep structures and evident deep structures be objectively
distinguished? Any abstract definition of the two would appear to be arbitrary, on
the other hand, in practice a specific case offers little ambiguity. Either the
practitioner is able to recognize and describe emergent patterns in ways that make
them manifest to the client or not. If so, then the structure can be said to be
evident, though deep. If the patterns become manifest only with the application of
some more sophisticated methodology, then they can be said to be subtle deep
structures.</Main_Text>
<Main_Text/>
<Main_Text>Is the distinction between dynamic and descriptive metaphors a helpful one?
The terms are not meant to be pejorative - both descriptive and dynamic metaphors can
be equally useful. But there is a practical distinction between the two. Descriptive
metaphors use the language of complexity to describe patterns that emerge in human
systems. These descriptions are based on apparent isomorphisms between chaotic or
complex adaptive patterns in physical systems and emergent behavior in human systems.
No causal connection is perceived or implied. Dynamic metaphors, on the other hand,
posit similar dynamics between the physical and human systems, allowing the
practitioner to use the principles of complexity to influence intentionally the
conditions or interactions that result in the emergent behaviors. </Main_Text>
<Main_Text/>
<Main_Text>Are the number of categories for either the phenomena or the tools
sufficient? Are more divisions needed to capture the meaningful distinctions among
current human systems dynamics tools and techniques? Both dimensions - phenomenon and
tools - are probably more continua than discrete clusters, but the finite number of
distinct categories simplifies the process of recognizing the needs and matching
methods to requirements.</Main_Text>
<Main_Text/>
<Main_Text> Like most useful models, the Practice Landscape introduces a whole new set
of meaningful questions that will affect both research and practice in the field.
Some questions for future consideration include:</Main_Text>
<Main_Text/>
<List_Bullet>What is a catalogue of complexity-inspired approaches that fall into each
of the twelve categories?</List_Bullet>
<List_Bullet>Which categories have most tools and techniques available and which
categories need further development or investigation?</List_Bullet>
<List_Bullet>How does a practitioner assess a situation to determine whether the
patterns are more or less deep or evident?</List_Bullet>
<List_Bullet>What is the appropriate role of client awareness and consultant
consciousness of the phenomena and available tools?</List_Bullet>
<Normal/>
<Main_Text> There is no doubt that principles from chaos and complexity can be helpful
to practitioners who work in human systems, but the myriad approaches and tools can
be quite confusing. The Practice Landscape provides a taxonomy to articulate useful
differences among tools and techniques that have been developed by scholars and
practitioners. Based on these distinctions, methods, tools, and techniques can be
selected that are most fitting for the situation and for the expectations and
perspectives of the client and the practitioner.</Main_Text>
<heading_1/>
<heading_1>Acknowledgements</heading_1>
<Main_Text>I wish to thank Jeffrey Goldstein and other members of the community who
provided input and especially to the peer reviewers who provided feedback on earlier
drafts of this paper. Any missteps, however, are the sole responsibility of the
author.</Main_Text>
<Main_Text/>
<heading_1>References</heading_1>
<References>Barabási, A. (2002). Linked: The new science of networks, Cambridge, MA:
Perseus Publishing.</References>
<References>Eoyang, G. (1997). Coping with chaos: Seven simple tools, Cheyenne, Wyoming:
Lagumo Publishing.</References>
<References>Goldstein, J. (1994). The unshackled organization, New York: Productivity
Press.</References>
<References>Kaplan, R. and D. Norton. (1996). The balanced scorecard, Boston, MA:
Harvard Business School Press.</References>
<References>Kaplan, D. and L. Glass. (1995) Understanding nonlinear dynamics, New York,
NY: Springer-Verlag. </References>
<References>Knowles, R. (2002). The leadership dance: Pathways to extraordinary
organizational effectiveness, NY: The Center for Self-Organizing Leadership. </References>
<References>Lissack, M. and Roos, J. (1999). The next common sense: Mastering corporate
complexity through coherence, London, UK: Nicholas Brealey Publishing Limited. </References>
<References>Morgan, G. (1997). Imaginization: New mindsets for seeing, organizing, and
managing, San Francisco, CA: Berrett-Koehler Publishers, Inc. </References>
<References>Olson, E. and G. Eoyang. (2001). Facilitating organization change: Lessons
from complexity science, San Francisco, CA: Jossey-Bass/Pfeiffer. </References>
<References>Owen, H. (2004). The practice of peace, Circle Pines, Minnesota: HSD
Institute Press.</References>
<References>Stacey, R. (2001). Complex responsive processes, New York, NY:
Routledge.</References>
<References>Wheatley, M. (1992). Leadership and the new science: Learning about
organization from an orderly universe, San Francisco, CA: Berrett-Koehler Publishers,
Inc.</References>
<References>Weisbord, M. and S. Janoff. (2000). Future search: An action guide to
finding common ground in organizations & communities, San Francisco, CA:
Berrett-Koehler Publishers, Inc.</References>
<References>Zimmerman, B., Lindberg, C. and Plsek, P. (2001). Edgeware: Insights from
complexity science for health care leaders, Irving, TX: VHA, Inc.</References>
<References/>
<Main_Text>Dr. Glenda Eoyang is founding Executive Director of the Human Systems
Dynamics Institute, a network of individuals and organizations developing theory and
practice at the intersection of complexity and social sciences. Since 1988, she has
explored the world of complexity in physical systems and used the insights to develop
concepts, methods, tools, and techniques to improve innovation and productivity in
human systems. She is author of Coping with Chaos: Seven Simple Tools (Lagumo, 1997);
Facilitating Organization Change: Lessons from Complexity Science
(Jossey-Bass/Pfeiffer, 2001), which she wrote with Edwin E. Olson; and numerous
articles and lectures. She is also editor of and contributor to Voices from the
Field: An Introduction to Human Systems Dynamics (HSD Institute Press, 2003).
</Main_Text>
</Story>
<Story>
<Table>
<TR>
<TD/>
<TD>
<_No_paragraph_style_>Tools for understanding and
Intervention</_No_paragraph_style_>
</TD>
</TR>
<TR>
<TD>
<_No_paragraph_style_>Phenomena</_No_paragraph_style_>
</TD>
<TD>
<_No_paragraph_style_>Practice</_No_paragraph_style_>
</TD>
<TD>
<_No_paragraph_style_>Weak metaphors</_No_paragraph_style_>
</TD>
<TD>
<_No_paragraph_style_>Strong metaphors</_No_paragraph_style_>
</TD>
<TD>
<_No_paragraph_style_>Mathematics</_No_paragraph_style_>
</TD>
</TR>
<TR>
<TD>
<_No_paragraph_style_>Surface structures</_No_paragraph_style_>
<_No_paragraph_style_/>
<_No_paragraph_style_/>
<_No_paragraph_style_/>
<_No_paragraph_style_/>
<_No_paragraph_style_/>
<_No_paragraph_style_>Example</_No_paragraph_style_>
</TD>
<TD>
<_No_paragraph_style_>Act in response to the surface structures of human
systems dynamics.</_No_paragraph_style_>
<_No_paragraph_style_/>
<_No_paragraph_style_/>
<_No_paragraph_style_/>
<_No_paragraph_style_>15% Solution</_No_paragraph_style_>
</TD>
<TD>
<_No_paragraph_style_>Describe patterns that emerge in human systems with
metaphors drawn from complexity sciences.</_No_paragraph_style_>
<_No_paragraph_style_/>
<_No_paragraph_style_>Butterfly Effects</_No_paragraph_style_>
</TD>
<TD>
<_No_paragraph_style_>Intervene using tools derived from complexity to
influence the surface structures of human systems.</_No_paragraph_style_>
<_No_paragraph_style_/>
<_No_paragraph_style_>Coupling</_No_paragraph_style_>
</TD>
<TD>
<_No_paragraph_style_>Represent complex relationships among variables of the
surface dynamics of complex human systems.</_No_paragraph_style_>
<_No_paragraph_style_/>
<_No_paragraph_style_>Balanced Scorecard</_No_paragraph_style_>
</TD>
</TR>
<TR>
<TD>
<_No_paragraph_style_>Evident deep </_No_paragraph_style_>
<_No_paragraph_style_>structures</_No_paragraph_style_>
<_No_paragraph_style_/>
<_No_paragraph_style_/>
<_No_paragraph_style_/>
<_No_paragraph_style_/>
<_No_paragraph_style_/>
<_No_paragraph_style_>Example</_No_paragraph_style_>
</TD>
<TD>
<_No_paragraph_style_>Act in response to the deep structures of human systems
dynamics that are evident when I know where and how to
look.</_No_paragraph_style_>
<_No_paragraph_style_/>
<_No_paragraph_style_>Reflection</_No_paragraph_style_>
</TD>
<TD>
<_No_paragraph_style_>Describe subtle structures that shape human system
dynamics using complexity metaphors.</_No_paragraph_style_>
<_No_paragraph_style_/>
<_No_paragraph_style_/>
<_No_paragraph_style_/>
<_No_paragraph_style_>Attractors</_No_paragraph_style_>
</TD>
<TD>
<_No_paragraph_style_>Influence the self-organizing process in human systems by
shifting the nonlinear dynamics that are visible.</_No_paragraph_style_>
<_No_paragraph_style_/>
<_No_paragraph_style_/>
<_No_paragraph_style_>Future Search</_No_paragraph_style_>
</TD>
<TD>
<_No_paragraph_style_>Represent the more subtle nonlinear dynamics of human
systems using tools of mathematics.</_No_paragraph_style_>
<_No_paragraph_style_/>
<_No_paragraph_style_/>
<_No_paragraph_style_/>
<_No_paragraph_style_>Network Analysis</_No_paragraph_style_>
</TD>
</TR>
<TR>
<TD>
<_No_paragraph_style_>Subtle deep structures</_No_paragraph_style_>
<_No_paragraph_style_/>
<_No_paragraph_style_/>
<_No_paragraph_style_/>
<_No_paragraph_style_/>
<_No_paragraph_style_/>
<_No_paragraph_style_/>
<_No_paragraph_style_>Example</_No_paragraph_style_>
</TD>
<TD>
<_No_paragraph_style_>Act in response to structures that are so deep within the
nonlinear dynamics that I am unaware of what the patterns
are.</_No_paragraph_style_>
<_No_paragraph_style_/>
<_No_paragraph_style_/>
<_No_paragraph_style_>Intuition</_No_paragraph_style_>
</TD>
<TD>
<_No_paragraph_style_>Support a system as it describes for itself the nonlinear
dynamics that drive its tensions, productivity, and
history.</_No_paragraph_style_>
<_No_paragraph_style_/>
<_No_paragraph_style_/>
<_No_paragraph_style_>Open Space Technology</_No_paragraph_style_>
</TD>
<TD>
<_No_paragraph_style_>Represent the system dynamics so that the subtle deep
patterns are visible and accessible to influence.</_No_paragraph_style_>
<_No_paragraph_style_/>
<_No_paragraph_style_/>
<_No_paragraph_style_>Computer Simulation Models</_No_paragraph_style_>
</TD>
<TD>
<_No_paragraph_style_>Use mathematical tools to discover subtle structures in
complex human systems.</_No_paragraph_style_>
<_No_paragraph_style_/>
<_No_paragraph_style_/>
<_No_paragraph_style_/>
<_No_paragraph_style_>Nonlinear Time Series Modeling</_No_paragraph_style_>
</TD>
</TR>
</Table>
<Caption>Table 1 Human systems dynamics: The practice landscape</Caption>
</Story>
</Article>
</Document>
</TaggedPDF-doc>
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<body>5556
<p>E:CO Vol. 6 Nos. 1-2 2004 pp. 55-60</p>
<p>Eoyang</p>
<p>The practitioner’s landscape</p>
<p>E:CO Special Double Issue Vol. 6 Nos. 1-2 2004 pp. 55-60</p>
<p>Practitioner</p>The practitioner’s landscapeGlenda H. EoyangHuman Systems Dynamics Institute, Minnesota, US
<p>An array of complexity-based tools and techniques are available today, but
how does the practitioner select a particular approach to respond to a particular
need? We present a simple taxonomy to describe the landscape of complexity-derived
methods for human systems dynamics. Practitioners can use the landscape to understand
the diversity of tools and techniques, to foster respect for approaches different
from ones’ own, to build an understanding of the field as a whole, and to select
specific techniques to apply in specific situations.
</p>
<h1>In the beginning...</h1>
<p>In the 1980s, when some of us first delved into applications of complexity to
human systems, there were no preformed models or even solid metaphors to guide us. We
dove into the science and mathematics of chaos and complexity, came up gasping for
breath, and put together the language, tools, and methods that we thought would be
most helpful for ourselves, our colleagues, and our clients. Though the deserted
landscape was lonely and intimidating sometimes, it left us free to explore
opportunities and to invent tools and techniques to meet the immediate needs as we
understood them at the time. It also allowed us to make low-risk mistakes, either in
our understanding of the science or in our expectations for its application to
real-life human systems. We were a creative bunch and generated an endless stream of
complexity-based inventions.
</p>
<h1>A rugged landscape</h1>
<p>Today, the landscape is different. Early adapters and inventors have passed
through this territory before. They’ve left a trail of methods, models, languages,
and expectations that are not always consistent within each approach and certainly
not coherent among the various approaches. Each explorer has synthesized his or her
experience, theoretical frameworks, and client’s needs to create tools and methods
that work in a given time and place. These creations have sometimes taken on lives of
their own - being codified and generalized to be applied in multiple situations. What
used to be a desert is now a rugged landscape of tools and techniques to help apply
principles of complexity science to the challenges that plague individuals,
institutions, and communities today. This emerging landscape of human systems
dynamics tools and techniques includes:
</p>
<li>15% Solution (Morgan, 1997)</li>
<li>Complex responsive processes (Stacey, 2001)</li>
<li>Self-organizing leadership (Knowles, 2002)</li>
<li>Difference questioning (Goldstein, 1994)</li>
<li>Metaphorical landscapes (Lissack & Roos, 1999)</li>
<li>Difference Matrix (Olson & Eoyang, 2001)</li>
<li>Generative Relationship STAR (Zimmerman, et al., 2001)</li>
<p>And many, many more.</p>
<h1>Making sense of the pattern</h1>
<p>The accumulation of complexity-based techniques makes it possible for
practitioners to address the complex adaptive nature of human systems without a deep
understanding of the nonlinear dynamics that drive emergent patterns of behavior.
More people can use dynamical approaches to increase the power and (usually) the
efficacy of their interventions. On the other hand, the library of powerful tools can
quickly become a graveyard of irrelevant approaches.
</p>
<p> Given this complex, rugged landscape of complexity-related tools and
techniques, how does a practitioner select which of the many complexity-based tools
to use in a particular situation?
</p>
<h1>Differences that make a difference</h1>
<p>We propose a two-dimensional, twelve category classification system that
represents the landscape of the work of practitioners in human systems
dynamics.
</p>
<p> This approach clusters available tools and techniques into groups to help a
practitioner think about the many options and select the approach that constitutes a
‘best fit’ for a given consulting challenge. This taxonomy is based on the two
fundamental ‘differences that make a difference’ to practitioners when they engage
with client systems to recognize and influence emergent patterns of complex dynamical
interactions.
</p>
<p> The first dimension deals with the phenomenon of interest. How are the
relevant patterns exhibited in the situation? Of course a conversation between client
and consultant is required to determine the relevant patterns. During this
conversation the practitioner learns where the challenge lies in relation to these
three broad phenomenological categories.
</p>
<p>Phenomena category 1: Surface structures. In some situations, the client
invites the consultant to deal with patterns that are evident to any observer.
Interpersonal conflicts, lagging sales, customer dissatisfaction are examples of
patterns that arise in such surface structures. They are immediately evident to
observers of and participants in the human system. We call these ‘surface structures’
because they are patterns that emerge in the most evident facets of our human
systems.
</p>
<p>Phenomena category 2: Evident deep structures. Other times, the presenting
problem is opaque to the client. He or she describes a sense of discomfort with
people or dissatisfaction with processes and products. Though the pattern may not be
evident to the client, the tools and discipline of a human systems dynamics
professional allow the practitioner to discern and describe the patterns in ways that
the client understands and recognizes as accurate. We call these ‘evident deep
structures’. These patterns emerge from dynamics embedded deep within the system, but
they become evident through applications of fairly accessible tools and techniques.
</p>
<p>Phenomena category 3: Subtle deep structures. Finally, some situations
exhibit emergent patterns that are both subtle and deep. Neither the client’s native
instincts nor the tools that function as simple cognitive filters can articulate a
coherent pattern of relationships in the complex dynamics of the system. The
complexity of these situations transcends the capacity of one level of complexity
tool and demands more subtle and/or complicated methods and models. Hospital error
rates and other complex phenomena where data points are distributed across time
(e.g., 1/f ‘pink’ noise phenomena) are examples of deep structural patterns that
require more subtle analysis techniques.
</p>
<p> Each of these levels of subtly and depth requires a different set of
complexity tools and techniques, and an adept practitioner will be able to assess the
environment and select approaches that fit the phenomena of interest and the
complexity of the patterns emergent in the client system.
</p>
<p> The second dimension that defines the categories of the Practice Landscape
deals not with the situation itself but with the tools that are chosen for
understanding and intervention. In short, these are ways that the situation can be
represented by the client and the consultant. They are epistemological distinctions,
and they determine how the team thinks and talks about the patterns that emerge from
the dynamics of the situation. We define four different categories along a continuum
from least abstract (practice) to most abstract (mathematics) tools for understanding
and intervention. The four are described below.
</p>
<p>Tools category 1: Practice. It is possible to respond to quite complex
dynamics immediately - without aid of language or conscious analysis. Some
complexity-based approaches are defined to support just such practice-oriented
response. Many clients are perfectly satisfied with options for action that do not
come along with complicated explanations, strange language, or sophisticated
justifications. They ask, “Did it work?” If the answer is, “Yes,” that is all they
need to know. Such tools were not accessible in traditional organization development
or management practice because explanations or interventions were expected to predict
their own success. The unpredictability of complex adaptive systems removes this
requirement and makes accessible to study a whole new domain of intuition- and
practice-based interventions that can only be assessed in retrospect.
</p>
<p>Tools category 2: Descriptive metaphors. Complexity science is full of rich
and engaging metaphors. Butterfly effects, attractors, fractals, edge of chaos - they
are poetic and easily accessible terms for the lay person. They can also be
meaningful descriptors of patterns that emerge from human systems dynamics. We refer
to these as ‘descriptive’ metaphors not because they are less valuable than ‘dynamic’
ones, but because the application requires a less literal interpretation of the
mathematical complexity concept as it is applied to social systems. Using descriptive
metaphors, one can think about how ‘butterfly effects’ name patterns that appear
commonly in human systems. For example, the descriptive metaphor can represent small
deviations in team procedure that may generate a major shift in direction. Such
descriptive applications of the complexity concepts can help build shared mental
models, even when sensitive dependence on initial conditions cannot be measured or
proven in any formal way.
</p>
<p>Tools category 3: Dynamic metaphors. Moving up the scale of abstraction and
toward quantitative methods, we reach the tools category of dynamic metaphors. Here
we encounter methods of qualitative analysis, but ones that hold more closely to the
literal interpretation of the complexity metaphors. Rather than just a superficial
isomorphism with patterns of complex adaptive or deterministic chaotic systems,
dynamic metaphors focus on similarities between the underlying dynamics of the human
system and other nonlinear dynamical systems.
</p>
<p>Tools category 4: Mathematics. The most abstract of the ways to understand
and intervene are those that derive from mathematics. Quantitative languages are much
more formal and less ambiguous than metaphorical or practice approaches. This does
not make them better, but it does make them more precise. Only the practitioner in a
specific environment can choose whether the situation, client needs, and resources
warrant investment in mathematical analyses and interventions.
</p>
<p> These three phenomenological and four epistemological categories define
twelve clusters of complexity-inspired interventions. Table 1 defines and gives an
example of each category. We will not attempt a definitive categorization of each of
the many complexity and human systems approaches because that is beyond the scope of
this paper. We do believe, however, that this rubric can help practitioners select an
appropriate suite of complexity tools based on the immediacy of the emergent pattern
and the desire of the client and consultant for precision of understanding and
planned intervention.
</p>
<h1>Applications in practice</h1>
<p>The twelve areas represented on the landscape provide ways to categorize the
many options for working with and within complex human systems. Each cell represents
a class of approaches that can be used to understand and influence complex human
dynamics. Table 1 also gives an example of an approach that fits each of the
locations on the Practice Landscape. These examples are provided merely to help
explain the options that the Landscape describes. Any one of the areas could include
a large number of other interventions or approaches. These examples should help
explain the structure and function of the Practice Landscape. The following sets of
examples demonstrate how a practitioner might use each of the tools categories might
be applied within each of the phenomenological categories.
</p>
<p> Some phenomena in complex adaptive systems are obvious even to the casual
observer. These are the surface structures that appear across the first row of the
Practice Landscape. For a variety of reasons, practitioners might choose to focus on
these phenomena rather than the more subtle patterns that emerge in self-organizing
systems. A practitioner might take this path when a client is new to the field and
somewhat skeptical, or when time is short and dynamics are particularly disruptive.
Even when focusing on these obvious patterns, options for complexity-inspired
interventions are many. Gareth Morgan’s 15% solution (Zimmerman, 2001) encourages one
to take action and observe how that action influences emergent patterns over time.
Another option is to name the obvious pattern of behavior using one of the beautiful
and descriptive metaphors of complexity, such as the butterfly effect (Wheatley,
1992). Moving beyond the language, there are interventions that can shape intervening
action when the metaphors of complexity are taken somewhat more literally. Coupling
(Eoyang, 1997) is an example of using the relationships of complexity to shape not
only descriptions but decisions in a dynamical human system. Finally, complex
dynamics can be captured in simple mathematics when measures, such as the Balanced
Scorecard (Kaplan & Norton, 1996), are used to track mutually causal factors in a
complex and adaptive system. So, a wide range of options (from action to mathematics)
is available when a practitioner needs or wants to influence the superficial
structures that emerge in a complex system.
</p>
<p> Right below the surface in human systems dynamics are patterns that might be
missed by the casual observer. These patterns, called evident deep structures, can be
accessible to the ‘naked’ eye, but they require training and heightened sensitivity
to discern the patterns as they emerge. Some clients and many human systems dynamics
professionals are trained to see these patterns as they emerge. Various tools help
articulate and translate these patterns into meaningful action. In terms of practice,
reflection is a method that uncovers patterns that otherwise would be hidden from
view. Practitioners use a variety of reflective activities from journaling to guided
imagery to help people see emergent patterns in their human systems. Moving to the
descriptive metaphorical ways of understanding and action, many metaphors can be used
to represent these patterns as they emerge. One often used (and sometime misused)
metaphor is the strange attractor. ‘Attractor’ presents the image of emergent
behavior that has a finite bound and infinite variability within the bound. This
language can help a group be aware of and use its inherent patterns of behavior. The
next group of tools, dynamic metaphors, can shape shared action in a group as they
become aware of their own emerging patterns. Future Search (Weisbord & Janoff,
2000) is an example of an approach that uses the evident deep structures of a
dynamical human system (such as sensitive dependence on initial conditions,
self-similarity, coupling, and mutual causality) to establish conditions for
organizational transformation. Finally, the mathematics of network analysis
(Barabási, 2002) can make the invisible visible to a group of people seeking to
understand their shared dynamics. So, each category of tool, from unspoken practice
through descriptive and dynamic metaphors and to mathematics, can be used to help
articulate the deep structures of human dynamics that are accessible to trained
observers.
</p>
<p> The third, and final, level of phenomena involves those patterns that cannot
be directly observed, even by trained observers. This level is called subtle deep
structures. Depending on the dimensionality of the system and/or its stage of
evolution, some complex adaptive systems evince patterns that are so deeply ingrained
and so subtle that they cannot be seen without special tools and techniques.
Intuition is a practice tool that accesses these subtle structures. Some gifted
individuals can sense a ‘subtle realm’ when it is inaccessible to others or even to a
conscious investigation by the intuitive. Open Space Technology (Owen, 2004), a large
group meeting facilitation technique, uses the dynamics of complexity to build
system-wide patterns of understanding. Open Space depends on simple rules that define
the underlying patterns of individual and group behavior, so it gives names for the
deep and subtle structures that drive the dynamics of human systems. Computer
simulation models generate even stronger metaphors for invisible patterns in human
systems dynamics. By representing the systems’ interactions and emergent patterns,
the simulation can make visible the deep, subtle patterns that emerge from complex
interactions. Finally, these subtle patterns can be uncovered by complex mathematical
analyses, such as nonlinear time series analysis (Kaplan & Glass, 1995). These
different types of tools can be used to discover, describe, and influence the deep
structures and patterns of behavior that emerge in complex human systems.
</p>
<p> These twelve categories of practice, defined by the object of focus and the
tools of investigation, provide a rubric to help a practitioner understand the wide
variety of complexity-based approaches and to select the one that is most appropriate
for a given situation. Armed with this understanding, the practitioner can select the
approach that best fits the needs and opportunities of the situation and the
moment.
</p>
<h1>Benefits of the practice landscape</h1>
<p>When one is faced with the multitude of complexity-inspired approaches, the
Practice Landscape can provide a variety of benefits. Choices are simplified without
restricting options. When a situation is viewed through this landscape, practitioners
have two choices to make. One can view more or less subtle patterns with more or less
abstract tools. Focusing on these two variables, a practitioner can focus in on a
small subset of tools and approaches that might meet the immediate need.
</p>
<p> All options are equally valid. No one part of the landscape is by nature
superior to another. In some circumstances you need to deal with the patterns that
are already seen by everyone in a group. Sometimes you need to practice your insights
about complexity without using the language. In other situations you may be able to
use the mathematical tools of complex adaptive systems to demonstrate subtle and
surprising dynamics. No place on the landscape is any less useful or true than any
other. The only question is, “Which of the options fits your practice environment at
a particular place or time?”
</p>
<p> New approaches can be envisioned that take a known approach from one domain
and finds ways to apply it in another. Likewise, this set of categories can be a
framework for personal development as a practitioner recognizes his or her strengths
and works to overcome personal weaknesses.
</p>
<p> A group of colleagues can use the Practice Landscape to support a planning
process. It provides a shared language that acknowledges the power of multiple
perspectives while providing meaningful distinctions and criteria for shared
decisions.
</p>
<h1>Challenges to the neatness of the </h1>
<h1>landscape</h1>
<p>It would be nice to believe that the Practice Landscape provides unambiguous
order for the messy collection of practices in human systems dynamics. This is not
the case. Like most models, this gives one some level of meaning and leaves other
questions unanswered. Some questions for future study include:
</p>
<p>Can subtle deep structures and evident deep structures be objectively
distinguished? Any abstract definition of the two would appear to be arbitrary, on
the other hand, in practice a specific case offers little ambiguity. Either the
practitioner is able to recognize and describe emergent patterns in ways that make
them manifest to the client or not. If so, then the structure can be said to be
evident, though deep. If the patterns become manifest only with the application of
some more sophisticated methodology, then they can be said to be subtle deep
structures.
</p>
<p>Is the distinction between dynamic and descriptive metaphors a helpful one?
The terms are not meant to be pejorative - both descriptive and dynamic metaphors can
be equally useful. But there is a practical distinction between the two. Descriptive
metaphors use the language of complexity to describe patterns that emerge in human
systems. These descriptions are based on apparent isomorphisms between chaotic or
complex adaptive patterns in physical systems and emergent behavior in human systems.
No causal connection is perceived or implied. Dynamic metaphors, on the other hand,
posit similar dynamics between the physical and human systems, allowing the
practitioner to use the principles of complexity to influence intentionally the
conditions or interactions that result in the emergent behaviors.
</p>
<p>Are the number of categories for either the phenomena or the tools
sufficient? Are more divisions needed to capture the meaningful distinctions among
current human systems dynamics tools and techniques? Both dimensions - phenomenon and
tools - are probably more continua than discrete clusters, but the finite number of
distinct categories simplifies the process of recognizing the needs and matching
methods to requirements.
</p>
<p> Like most useful models, the Practice Landscape introduces a whole new set
of meaningful questions that will affect both research and practice in the field.
Some questions for future consideration include:
</p>
<li>What is a catalogue of complexity-inspired approaches that fall into each
of the twelve categories?
</li>
<li>Which categories have most tools and techniques available and which
categories need further development or investigation?
</li>
<li>How does a practitioner assess a situation to determine whether the
patterns are more or less deep or evident?
</li>
<li>What is the appropriate role of client awareness and consultant
consciousness of the phenomena and available tools?
</li>
<p> There is no doubt that principles from chaos and complexity can be helpful
to practitioners who work in human systems, but the myriad approaches and tools can
be quite confusing. The Practice Landscape provides a taxonomy to articulate useful
differences among tools and techniques that have been developed by scholars and
practitioners. Based on these distinctions, methods, tools, and techniques can be
selected that are most fitting for the situation and for the expectations and
perspectives of the client and the practitioner.
</p>
<h1>Acknowledgements</h1>
<p>I wish to thank Jeffrey Goldstein and other members of the community who
provided input and especially to the peer reviewers who provided feedback on earlier
drafts of this paper. Any missteps, however, are the sole responsibility of the
author.
</p>
<h1>References</h1>
<p>Barabási, A. (2002). Linked: The new science of networks, Cambridge, MA:
Perseus Publishing.
</p>
<p>Eoyang, G. (1997). Coping with chaos: Seven simple tools, Cheyenne, Wyoming:
Lagumo Publishing.
</p>
<p>Goldstein, J. (1994). The unshackled organization, New York: Productivity
Press.
</p>
<p>Kaplan, R. and D. Norton. (1996). The balanced scorecard, Boston, MA:
Harvard Business School Press.
</p>
<p>Kaplan, D. and L. Glass. (1995) Understanding nonlinear dynamics, New York,
NY: Springer-Verlag.
</p>
<p>Knowles, R. (2002). The leadership dance: Pathways to extraordinary
organizational effectiveness, NY: The Center for Self-Organizing Leadership.
</p>
<p>Lissack, M. and Roos, J. (1999). The next common sense: Mastering corporate
complexity through coherence, London, UK: Nicholas Brealey Publishing Limited.
</p>
<p>Morgan, G. (1997). Imaginization: New mindsets for seeing, organizing, and
managing, San Francisco, CA: Berrett-Koehler Publishers, Inc.
</p>
<p>Olson, E. and G. Eoyang. (2001). Facilitating organization change: Lessons
from complexity science, San Francisco, CA: Jossey-Bass/Pfeiffer.
</p>
<p>Owen, H. (2004). The practice of peace, Circle Pines, Minnesota: HSD
Institute Press.
</p>
<p>Stacey, R. (2001). Complex responsive processes, New York, NY:
Routledge.
</p>
<p>Wheatley, M. (1992). Leadership and the new science: Learning about
organization from an orderly universe, San Francisco, CA: Berrett-Koehler Publishers,
Inc.
</p>
<p>Weisbord, M. and S. Janoff. (2000). Future search: An action guide to
finding common ground in organizations & communities, San Francisco, CA:
Berrett-Koehler Publishers, Inc.
</p>
<p>Zimmerman, B., Lindberg, C. and Plsek, P. (2001). Edgeware: Insights from
complexity science for health care leaders, Irving, TX: VHA, Inc.
</p>
<p>Dr. Glenda Eoyang is founding Executive Director of the Human Systems
Dynamics Institute, a network of individuals and organizations developing theory and
practice at the intersection of complexity and social sciences. Since 1988, she has
explored the world of complexity in physical systems and used the insights to develop
concepts, methods, tools, and techniques to improve innovation and productivity in
human systems. She is author of Coping with Chaos: Seven Simple Tools (Lagumo, 1997);
Facilitating Organization Change: Lessons from Complexity Science
(Jossey-Bass/Pfeiffer, 2001), which she wrote with Edwin E. Olson; and numerous
articles and lectures. She is also editor of and contributor to Voices from the
Field: An Introduction to Human Systems Dynamics (HSD Institute Press, 2003).
</p>
<table>Tools for understanding and
InterventionPhenomenaPracticeWeak metaphorsStrong metaphorsMathematicsSurface structuresExampleAct in response to the surface structures of human
systems dynamics.15% SolutionDescribe patterns that emerge in human systems with
metaphors drawn from complexity sciences.Butterfly EffectsIntervene using tools derived from complexity to
influence the surface structures of human systems.CouplingRepresent complex relationships among variables of the
surface dynamics of complex human systems.Balanced ScorecardEvident deep structuresExampleAct in response to the deep structures of human systems
dynamics that are evident when I know where and how to
look.ReflectionDescribe subtle structures that shape human system
dynamics using complexity metaphors.AttractorsInfluence the self-organizing process in human systems by
shifting the nonlinear dynamics that are visible.Future SearchRepresent the more subtle nonlinear dynamics of human
systems using tools of mathematics.Network AnalysisSubtle deep structuresExampleAct in response to structures that are so deep within the
nonlinear dynamics that I am unaware of what the patterns
are.IntuitionSupport a system as it describes for itself the nonlinear
dynamics that drive its tensions, productivity, and
history.Open Space TechnologyRepresent the system dynamics so that the subtle deep
patterns are visible and accessible to influence.Computer Simulation ModelsUse mathematical tools to discover subtle structures in
complex human systems.Nonlinear Time Series Modeling
</table>Table 1 Human systems dynamics: The practice landscape
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The practitioner’s landscapeGlenda H. EoyangHuman Systems Dynamics Institute, Minnesota, US<article xmlns:xs="http://www.w3.org/2001/XMLSchema";>
<body>
<para>
<p>An array of complexity-based tools and techniques are available today, but
how does the practitioner select a particular approach to respond to a particular
need? We present a simple taxonomy to describe the landscape of complexity-derived
methods for human systems dynamics. Practitioners can use the landscape to understand
the diversity of tools and techniques, to foster respect for approaches different
from ones’ own, to build an understanding of the field as a whole, and to select
specific techniques to apply in specific situations.
</p>
</para>
<sect1>
<title>In the beginning...</title>
<para>
<p>In the 1980s, when some of us first delved into applications of complexity to
human systems, there were no preformed models or even solid metaphors to guide us. We
dove into the science and mathematics of chaos and complexity, came up gasping for
breath, and put together the language, tools, and methods that we thought would be
most helpful for ourselves, our colleagues, and our clients. Though the deserted
landscape was lonely and intimidating sometimes, it left us free to explore
opportunities and to invent tools and techniques to meet the immediate needs as we
understood them at the time. It also allowed us to make low-risk mistakes, either in
our understanding of the science or in our expectations for its application to
real-life human systems. We were a creative bunch and generated an endless stream of
complexity-based inventions.
</p>
</para>
</sect1>
<sect1>
<title>A rugged landscape</title>
<para>
<p>Today, the landscape is different. Early adapters and inventors have passed
through this territory before. They’ve left a trail of methods, models, languages,
and expectations that are not always consistent within each approach and certainly
not coherent among the various approaches. Each explorer has synthesized his or her
experience, theoretical frameworks, and client’s needs to create tools and methods
that work in a given time and place. These creations have sometimes taken on lives of
their own - being codified and generalized to be applied in multiple situations. What
used to be a desert is now a rugged landscape of tools and techniques to help apply
principles of complexity science to the challenges that plague individuals,
institutions, and communities today. This emerging landscape of human systems
dynamics tools and techniques includes:
</p>
<ul>
<li>15% Solution (Morgan, 1997)</li>
<li>Complex responsive processes (Stacey, 2001)</li>
<li>Self-organizing leadership (Knowles, 2002)</li>
<li>Difference questioning (Goldstein, 1994)</li>
<li>Metaphorical landscapes (Lissack & Roos, 1999)</li>
<li>Difference Matrix (Olson & Eoyang, 2001)</li>
<li>Generative Relationship STAR (Zimmerman, et al., 2001)</li>
</ul>
<p>And many, many more.</p>
</para>
</sect1>
<sect1>
<title>Making sense of the pattern</title>
<para>
<p>The accumulation of complexity-based techniques makes it possible for
practitioners to address the complex adaptive nature of human systems without a deep
understanding of the nonlinear dynamics that drive emergent patterns of behavior.
More people can use dynamical approaches to increase the power and (usually) the
efficacy of their interventions. On the other hand, the library of powerful tools can
quickly become a graveyard of irrelevant approaches.
</p>
<p> Given this complex, rugged landscape of complexity-related tools and
techniques, how does a practitioner select which of the many complexity-based tools
to use in a particular situation?
</p>
</para>
</sect1>
<sect1>
<title>Differences that make a difference</title>
<para>
<p>We propose a two-dimensional, twelve category classification system that
represents the landscape of the work of practitioners in human systems
dynamics.
</p>
<p> This approach clusters available tools and techniques into groups to help a
practitioner think about the many options and select the approach that constitutes a
‘best fit’ for a given consulting challenge. This taxonomy is based on the two
fundamental ‘differences that make a difference’ to practitioners when they engage
with client systems to recognize and influence emergent patterns of complex dynamical
interactions.
</p>
<p> The first dimension deals with the phenomenon of interest. How are the
relevant patterns exhibited in the situation? Of course a conversation between client
and consultant is required to determine the relevant patterns. During this
conversation the practitioner learns where the challenge lies in relation to these
three broad phenomenological categories.
</p>
<p>Phenomena category 1: Surface structures. In some situations, the client
invites the consultant to deal with patterns that are evident to any observer.
Interpersonal conflicts, lagging sales, customer dissatisfaction are examples of
patterns that arise in such surface structures. They are immediately evident to
observers of and participants in the human system. We call these ‘surface structures’
because they are patterns that emerge in the most evident facets of our human
systems.
</p>
<p>Phenomena category 2: Evident deep structures. Other times, the presenting
problem is opaque to the client. He or she describes a sense of discomfort with
people or dissatisfaction with processes and products. Though the pattern may not be
evident to the client, the tools and discipline of a human systems dynamics
professional allow the practitioner to discern and describe the patterns in ways that
the client understands and recognizes as accurate. We call these ‘evident deep
structures’. These patterns emerge from dynamics embedded deep within the system, but
they become evident through applications of fairly accessible tools and techniques.
</p>
<p>Phenomena category 3: Subtle deep structures. Finally, some situations
exhibit emergent patterns that are both subtle and deep. Neither the client’s native
instincts nor the tools that function as simple cognitive filters can articulate a
coherent pattern of relationships in the complex dynamics of the system. The
complexity of these situations transcends the capacity of one level of complexity
tool and demands more subtle and/or complicated methods and models. Hospital error
rates and other complex phenomena where data points are distributed across time
(e.g., 1/f ‘pink’ noise phenomena) are examples of deep structural patterns that
require more subtle analysis techniques.
</p>
<p>Each of these levels of subtly and depth requires a different set of
complexity tools and techniques, and an adept practitioner will be able to assess the
environment and select approaches that fit the phenomena of interest and the
complexity of the patterns emergent in the client system.
</p>
<p>The second dimension that defines the categories of the Practice Landscape
deals not with the situation itself but with the tools that are chosen for
understanding and intervention. In short, these are ways that the situation can be
represented by the client and the consultant. They are epistemological distinctions,
and they determine how the team thinks and talks about the patterns that emerge from
the dynamics of the situation. We define four different categories along a continuum
from least abstract (practice) to most abstract (mathematics) tools for understanding
and intervention. The four are described below.
</p>
<p>Tools category 1: Practice. It is possible to respond to quite complex
dynamics immediately - without aid of language or conscious analysis. Some
complexity-based approaches are defined to support just such practice-oriented
response. Many clients are perfectly satisfied with options for action that do not
come along with complicated explanations, strange language, or sophisticated
justifications. They ask, “Did it work?” If the answer is, “Yes,” that is all they
need to know. Such tools were not accessible in traditional organization development
or management practice because explanations or interventions were expected to predict
their own success. The unpredictability of complex adaptive systems removes this
requirement and makes accessible to study a whole new domain of intuition- and
practice-based interventions that can only be assessed in retrospect.
</p>
<p>Tools category 2: Descriptive metaphors. Complexity science is full of rich
and engaging metaphors. Butterfly effects, attractors, fractals, edge of chaos - they
are poetic and easily accessible terms for the lay person. They can also be
meaningful descriptors of patterns that emerge from human systems dynamics. We refer
to these as ‘descriptive’ metaphors not because they are less valuable than ‘dynamic’
ones, but because the application requires a less literal interpretation of the
mathematical complexity concept as it is applied to social systems. Using descriptive
metaphors, one can think about how ‘butterfly effects’ name patterns that appear
commonly in human systems. For example, the descriptive metaphor can represent small
deviations in team procedure that may generate a major shift in direction. Such
descriptive applications of the complexity concepts can help build shared mental
models, even when sensitive dependence on initial conditions cannot be measured or
proven in any formal way.
</p>
<p>Tools category 3: Dynamic metaphors. Moving up the scale of abstraction and
toward quantitative methods, we reach the tools category of dynamic metaphors. Here
we encounter methods of qualitative analysis, but ones that hold more closely to the
literal interpretation of the complexity metaphors. Rather than just a superficial
isomorphism with patterns of complex adaptive or deterministic chaotic systems,
dynamic metaphors focus on similarities between the underlying dynamics of the human
system and other nonlinear dynamical systems.
</p>
<p>Tools category 4: Mathematics. The most abstract of the ways to understand
and intervene are those that derive from mathematics. Quantitative languages are much
more formal and less ambiguous than metaphorical or practice approaches. This does
not make them better, but it does make them more precise. Only the practitioner in a
specific environment can choose whether the situation, client needs, and resources
warrant investment in mathematical analyses and interventions.
</p>
<p>These three phenomenological and four epistemological categories define
twelve clusters of complexity-inspired interventions. Table 1 defines and gives an
example of each category. We will not attempt a definitive categorization of each of
the many complexity and human systems approaches because that is beyond the scope of
this paper. We do believe, however, that this rubric can help practitioners select an
appropriate suite of complexity tools based on the immediacy of the emergent pattern
and the desire of the client and consultant for precision of understanding and
planned intervention.
</p>
</para>
</sect1>
<sect1>
<title>Applications in practice</title>
<para>
<p>The twelve areas represented on the landscape provide ways to categorize the
many options for working with and within complex human systems. Each cell represents
a class of approaches that can be used to understand and influence complex human
dynamics. Table 1 also gives an example of an approach that fits each of the
locations on the Practice Landscape. These examples are provided merely to help
explain the options that the Landscape describes. Any one of the areas could include
a large number of other interventions or approaches. These examples should help
explain the structure and function of the Practice Landscape. The following sets of
examples demonstrate how a practitioner might use each of the tools categories might
be applied within each of the phenomenological categories.
</p>
<p> Some phenomena in complex adaptive systems are obvious even to the casual
observer. These are the surface structures that appear across the first row of the
Practice Landscape. For a variety of reasons, practitioners might choose to focus on
these phenomena rather than the more subtle patterns that emerge in self-organizing
systems. A practitioner might take this path when a client is new to the field and
somewhat skeptical, or when time is short and dynamics are particularly disruptive.
Even when focusing on these obvious patterns, options for complexity-inspired
interventions are many. Gareth Morgan’s 15% solution (Zimmerman, 2001) encourages one
to take action and observe how that action influences emergent patterns over time.
Another option is to name the obvious pattern of behavior using one of the beautiful
and descriptive metaphors of complexity, such as the butterfly effect (Wheatley,
1992). Moving beyond the language, there are interventions that can shape intervening
action when the metaphors of complexity are taken somewhat more literally. Coupling
(Eoyang, 1997) is an example of using the relationships of complexity to shape not
only descriptions but decisions in a dynamical human system. Finally, complex
dynamics can be captured in simple mathematics when measures, such as the Balanced
Scorecard (Kaplan & Norton, 1996), are used to track mutually causal factors in a
complex and adaptive system. So, a wide range of options (from action to mathematics)
is available when a practitioner needs or wants to influence the superficial
structures that emerge in a complex system.
</p>
<p> Right below the surface in human systems dynamics are patterns that might be
missed by the casual observer. These patterns, called evident deep structures, can be
accessible to the ‘naked’ eye, but they require training and heightened sensitivity
to discern the patterns as they emerge. Some clients and many human systems dynamics
professionals are trained to see these patterns as they emerge. Various tools help
articulate and translate these patterns into meaningful action. In terms of practice,
reflection is a method that uncovers patterns that otherwise would be hidden from
view. Practitioners use a variety of reflective activities from journaling to guided
imagery to help people see emergent patterns in their human systems. Moving to the
descriptive metaphorical ways of understanding and action, many metaphors can be used
to represent these patterns as they emerge. One often used (and sometime misused)
metaphor is the strange attractor. ‘Attractor’ presents the image of emergent
behavior that has a finite bound and infinite variability within the bound. This
language can help a group be aware of and use its inherent patterns of behavior. The
next group of tools, dynamic metaphors, can shape shared action in a group as they
become aware of their own emerging patterns. Future Search (Weisbord & Janoff,
2000) is an example of an approach that uses the evident deep structures of a
dynamical human system (such as sensitive dependence on initial conditions,
self-similarity, coupling, and mutual causality) to establish conditions for
organizational transformation. Finally, the mathematics of network analysis
(Barabási, 2002) can make the invisible visible to a group of people seeking to
understand their shared dynamics. So, each category of tool, from unspoken practice
through descriptive and dynamic metaphors and to mathematics, can be used to help
articulate the deep structures of human dynamics that are accessible to trained
observers.
</p>
<p> The third, and final, level of phenomena involves those patterns that cannot
be directly observed, even by trained observers. This level is called subtle deep
structures. Depending on the dimensionality of the system and/or its stage of
evolution, some complex adaptive systems evince patterns that are so deeply ingrained
and so subtle that they cannot be seen without special tools and techniques.
Intuition is a practice tool that accesses these subtle structures. Some gifted
individuals can sense a ‘subtle realm’ when it is inaccessible to others or even to a
conscious investigation by the intuitive. Open Space Technology (Owen, 2004), a large
group meeting facilitation technique, uses the dynamics of complexity to build
system-wide patterns of understanding. Open Space depends on simple rules that define
the underlying patterns of individual and group behavior, so it gives names for the
deep and subtle structures that drive the dynamics of human systems. Computer
simulation models generate even stronger metaphors for invisible patterns in human
systems dynamics. By representing the systems’ interactions and emergent patterns,
the simulation can make visible the deep, subtle patterns that emerge from complex
interactions. Finally, these subtle patterns can be uncovered by complex mathematical
analyses, such as nonlinear time series analysis (Kaplan & Glass, 1995). These
different types of tools can be used to discover, describe, and influence the deep
structures and patterns of behavior that emerge in complex human systems.
</p>
<p> These twelve categories of practice, defined by the object of focus and the
tools of investigation, provide a rubric to help a practitioner understand the wide
variety of complexity-based approaches and to select the one that is most appropriate
for a given situation. Armed with this understanding, the practitioner can select the
approach that best fits the needs and opportunities of the situation and the
moment.
</p>
</para>
</sect1>
<sect1>
<title>Benefits of the practice landscape</title>
<para>
<p>When one is faced with the multitude of complexity-inspired approaches, the
Practice Landscape can provide a variety of benefits. Choices are simplified without
restricting options. When a situation is viewed through this landscape, practitioners
have two choices to make. One can view more or less subtle patterns with more or less
abstract tools. Focusing on these two variables, a practitioner can focus in on a
small subset of tools and approaches that might meet the immediate need.
</p>
<p>All options are equally valid. No one part of the landscape is by nature
superior to another. In some circumstances you need to deal with the patterns that
are already seen by everyone in a group. Sometimes you need to practice your insights
about complexity without using the language. In other situations you may be able to
use the mathematical tools of complex adaptive systems to demonstrate subtle and
surprising dynamics. No place on the landscape is any less useful or true than any
other. The only question is, “Which of the options fits your practice environment at
a particular place or time?”
</p>
<p>New approaches can be envisioned that take a known approach from one domain
and finds ways to apply it in another. Likewise, this set of categories can be a
framework for personal development as a practitioner recognizes his or her strengths
and works to overcome personal weaknesses.
</p>
<p>A group of colleagues can use the Practice Landscape to support a planning
process. It provides a shared language that acknowledges the power of multiple
perspectives while providing meaningful distinctions and criteria for shared
decisions.
</p>
</para>
</sect1>
<sect1>
<title>Challenges to the neatness of the landscape</title>
<para>
<p>It would be nice to believe that the Practice Landscape provides unambiguous
order for the messy collection of practices in human systems dynamics. This is not
the case. Like most models, this gives one some level of meaning and leaves other
questions unanswered. Some questions for future study include:
</p>
<p>Can subtle deep structures and evident deep structures be objectively
distinguished? Any abstract definition of the two would appear to be arbitrary, on
the other hand, in practice a specific case offers little ambiguity. Either the
practitioner is able to recognize and describe emergent patterns in ways that make
them manifest to the client or not. If so, then the structure can be said to be
evident, though deep. If the patterns become manifest only with the application of
some more sophisticated methodology, then they can be said to be subtle deep
structures.
</p>
<p>Is the distinction between dynamic and descriptive metaphors a helpful one?
The terms are not meant to be pejorative - both descriptive and dynamic metaphors can
be equally useful. But there is a practical distinction between the two. Descriptive
metaphors use the language of complexity to describe patterns that emerge in human
systems. These descriptions are based on apparent isomorphisms between chaotic or
complex adaptive patterns in physical systems and emergent behavior in human systems.
No causal connection is perceived or implied. Dynamic metaphors, on the other hand,
posit similar dynamics between the physical and human systems, allowing the
practitioner to use the principles of complexity to influence intentionally the
conditions or interactions that result in the emergent behaviors.
</p>
<p>Are the number of categories for either the phenomena or the tools
sufficient? Are more divisions needed to capture the meaningful distinctions among
current human systems dynamics tools and techniques? Both dimensions - phenomenon and
tools - are probably more continua than discrete clusters, but the finite number of
distinct categories simplifies the process of recognizing the needs and matching
methods to requirements.
</p>
<p> Like most useful models, the Practice Landscape introduces a whole new set
of meaningful questions that will affect both research and practice in the field.
Some questions for future consideration include:
</p>
<ul>
<li>What is a catalogue of complexity-inspired approaches that fall into each
of the twelve categories?
</li>
<li>Which categories have most tools and techniques available and which
categories need further development or investigation?
</li>
<li>How does a practitioner assess a situation to determine whether the
patterns are more or less deep or evident?
</li>
<li>What is the appropriate role of client awareness and consultant
consciousness of the phenomena and available tools?
</li>
</ul>
<p> There is no doubt that principles from chaos and complexity can be helpful
to practitioners who work in human systems, but the myriad approaches and tools can
be quite confusing. The Practice Landscape provides a taxonomy to articulate useful
differences among tools and techniques that have been developed by scholars and
practitioners. Based on these distinctions, methods, tools, and techniques can be
selected that are most fitting for the situation and for the expectations and
perspectives of the client and the practitioner.
</p>
</para>
</sect1>
<sect1>
<title>Acknowledgements</title>
<para>
<p>I wish to thank Jeffrey Goldstein and other members of the community who
provided input and especially to the peer reviewers who provided feedback on earlier
drafts of this paper. Any missteps, however, are the sole responsibility of the
author.
</p>
</para>
</sect1>
<sect1>
<title>References</title>
<para>
<p>Barabási, A. (2002). Linked: The new science of networks, Cambridge, MA:
Perseus Publishing.
</p>
<p>Eoyang, G. (1997). Coping with chaos: Seven simple tools, Cheyenne, Wyoming:
Lagumo Publishing.
</p>
<p>Goldstein, J. (1994). The unshackled organization, New York: Productivity
Press.
</p>
<p>Kaplan, R. and D. Norton. (1996). The balanced scorecard, Boston, MA:
Harvard Business School Press.
</p>
<p>Kaplan, D. and L. Glass. (1995) Understanding nonlinear dynamics, New York,
NY: Springer-Verlag.
</p>
<p>Knowles, R. (2002). The leadership dance: Pathways to extraordinary
organizational effectiveness, NY: The Center for Self-Organizing Leadership.
</p>
<p>Lissack, M. and Roos, J. (1999). The next common sense: Mastering corporate
complexity through coherence, London, UK: Nicholas Brealey Publishing Limited.
</p>
<p>Morgan, G. (1997). Imaginization: New mindsets for seeing, organizing, and
managing, San Francisco, CA: Berrett-Koehler Publishers, Inc.
</p>
<p>Olson, E. and G. Eoyang. (2001). Facilitating organization change: Lessons
from complexity science, San Francisco, CA: Jossey-Bass/Pfeiffer.
</p>
<p>Owen, H. (2004). The practice of peace, Circle Pines, Minnesota: HSD
Institute Press.
</p>
<p>Stacey, R. (2001). Complex responsive processes, New York, NY:
Routledge.
</p>
<p>Wheatley, M. (1992). Leadership and the new science: Learning about
organization from an orderly universe, San Francisco, CA: Berrett-Koehler Publishers,
Inc.
</p>
<p>Weisbord, M. and S. Janoff. (2000). Future search: An action guide to
finding common ground in organizations & communities, San Francisco, CA:
Berrett-Koehler Publishers, Inc.
</p>
<p>Zimmerman, B., Lindberg, C. and Plsek, P. (2001). Edgeware: Insights from
complexity science for health care leaders, Irving, TX: VHA, Inc.
</p>
<p>Dr. Glenda Eoyang is founding Executive Director of the Human Systems
Dynamics Institute, a network of individuals and organizations developing theory and
practice at the intersection of complexity and social sciences. Since 1988, she has
explored the world of complexity in physical systems and used the insights to develop
concepts, methods, tools, and techniques to improve innovation and productivity in
human systems. She is author of Coping with Chaos: Seven Simple Tools (Lagumo, 1997);
Facilitating Organization Change: Lessons from Complexity Science
(Jossey-Bass/Pfeiffer, 2001), which she wrote with Edwin E. Olson; and numerous
articles and lectures. She is also editor of and contributor to Voices from the
Field: An Introduction to Human Systems Dynamics (HSD Institute Press, 2003).
</p>
<table>
<tr>
<td></td>
<td>Tools for understanding and
Intervention
</td>
</tr>
<tr>
<td>Phenomena</td>
<td>Practice</td>
<td>Weak metaphors</td>
<td>Strong metaphors</td>
<td>Mathematics</td>
</tr>
<tr>
<td>Surface structuresExample</td>
<td>Act in response to the surface structures of human
systems dynamics.15% Solution
</td>
<td>Describe patterns that emerge in human systems with
metaphors drawn from complexity sciences.Butterfly Effects
</td>
<td>Intervene using tools derived from complexity to
influence the surface structures of human systems.Coupling
</td>
<td>Represent complex relationships among variables of the
surface dynamics of complex human systems.Balanced Scorecard
</td>
</tr>
<tr>
<td>Evident deep structuresExample</td>
<td>Act in response to the deep structures of human systems
dynamics that are evident when I know where and how to
look.Reflection
</td>
<td>Describe subtle structures that shape human system
dynamics using complexity metaphors.Attractors
</td>
<td>Influence the self-organizing process in human systems by
shifting the nonlinear dynamics that are visible.Future Search
</td>
<td>Represent the more subtle nonlinear dynamics of human
systems using tools of mathematics.Network Analysis
</td>
</tr>
<tr>
<td>Subtle deep structuresExample</td>
<td>Act in response to structures that are so deep within the
nonlinear dynamics that I am unaware of what the patterns
are.Intuition
</td>
<td>Support a system as it describes for itself the nonlinear
dynamics that drive its tensions, productivity, and
history.Open Space Technology
</td>
<td>Represent the system dynamics so that the subtle deep
patterns are visible and accessible to influence.Computer Simulation Models
</td>
<td>Use mathematical tools to discover subtle structures in
complex human systems.Nonlinear Time Series Modeling
</td>
</tr>
</table>
</para>
</sect1>
</body>
</article>
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