Duane,
I sense you have
hit the heart of a major issue. The human mind often approaches “interactively
complex” (i.e. complex) problems as if they were only “structurally
complex” (i.e. complicated) ones, attempting to apply solutions that would
normally work in contexts that adhere to the principles of “proportionality,
replication, additivity, and demonstrability of cause and effect.” Unfortunately,
inter-organizational disaster relief efforts are “interactively complex”.
Inter-organizational
disaster relief efforts, in fact, exhibit nested interactive complexity. The
organizations responding to disaster form a complex system, but then each
organization itself is a complex system of departments, which are complex
systems of people (who’s neural nets can be seen as complex systems…).
Furthermore, these
systems are not just “complex”, but they are adaptive, exhibiting
dynamic learning. Therefore, the same influence that can illicit a certain individual
(or system) behavior one day (hour, minute, second) might induce a counter-productive
behavior the next.
During my
presentation, I will address how we can (relatively) reliably encourage
continuous improvements in collective system behavior despite the complex and
adaptive nature of the inter-organizational response.
Jamison
PS: For the
interested, I have attached a paper (currently under review) that contextualizes
disaster relief efforts as inter-organizational complex adaptive systems. Also,
here are some additional definitions of complexity from an inter-organizational
perspective:
Pathak,
Day, Nair, Sawaya, Krystal, “Complexity and Adaptivity in Supply Networks:
Building Supply Network Theory Using a Complex Adaptive Systems Perspective,”
Decision Sciences, Vol. 38, No. 4, Nov. 2007, pp. 547-579
(p. 559)
Complexity vs.
Complicatedness:
The distinction
between “complicated” research and “complexity-oriented”
research is important for ensuring a broad-based research agenda. Cilliers (2000) suggests that something that is complicated can be
intricate but the relationship between the components is fixed and
well-defined. For instance, a jumbo jet is a complicated system, which is
amenable to taking individual components apart and putting them back together.
In contrast, a complex system is characterized in terms of the nonlinear
dynamic interactions of the individual parts. Further, while a complicated
system can be viewed as the sum of its parts, a complex system cannot be viewed
that way—one cannot predict the behavior of a complex system by examining
the behavior of its individual parts. These emergent properties of complex
systems are due to the nonlinear dynamic relationship between the individual
components.
(pp. 550-551)
A complex
adaptive system (CAS) is an interconnected network of multiple entities (or
agents) that exhibit adaptive action in response to changes in both the
environment and the system of entities itself (Choi et al., 2001). Collective system performance or behavior emerges as
a nonlinear and dynamic function of the large number of activities made in
parallel by interacting entities. For example, the individual decisions made by
firms facing imperfect information and variable demand leads to a globally
observed phenomenon, i.e., the bullwhip effect (Lee & Padmanabhan, 1997).
Anderson (Anderson, 1999) outlined four common properties of such systems.
First, a CAS consists of entities which interact with other entities and with
the environment by following a set of simple decision rules (i.e. schema).
These entities may evolve over time as entities learn from their interactions.
In contrast to relational modeling, which tries to use one set of variables to
explain variation in another set of variables, CAS examines how changes in an
individual entity’s schema lead to different aggregate outcomes.
Second, complex
adaptive systems are self-organizing. Self-organization is a consequence of
interactions between entities. Self-organization is defined as a process in
which new structures, patterns and properties emerge without being externally
imposed on the system. Because the behavior in complex systems comes from
dynamic interactions among the agents and between the environment and the
agents, the changes tend to be non-linear with respect to the original changes
in the system. Thus, there may be small changes which have a dramatic effect on
the system, or conversely, large changes which have relatively little effect.
Choi et al. (2001, pp. 357) state, “the behavior of a complex
system cannot be written down in closed form; it is not amenable to prediction
via the formulation of a parametric model, such as a statistical forecasting
model.” However, even though the future may not be possible to predict in
an exact manner, the future may be non-random. While the changes that are made
to a system may be dramatic and unpredictable, there may be patterns of
behavior that can be considered prototypical. Appropriate analyses may yield
some knowledge of key patterns of behavior that are likely to develop in the
system over time.
Third, complex
adaptive systems co-evolve to the edge of chaos. Choi et al. (2001) explains co-evolution positing that a CAS reacts to
and creates its environment so that as the environment changes it may cause the
agents within it to change, who, in turn, cause other changes to the
environment. A CAS exhibits dynamism as changes occur in the environment which
affects the system. Environmental factors may cause changes to which the agents
must adapt, influencing the way agents perceive their environment or the schema
used by the agents themselves. Thus, the rules followed by the individual
entities organize the system, since individual entities are not privy to the
objective function of the system as a whole. The co-evolution of the system
happens in the rugged fitness landscapes in which the CAS exists. These
landscapes may be thought of in terms of an analogy of a range of mountains
that represents an objective function (i.e. performance function) that is
filled with hills and valleys (Kauffman, 1995). The hills or peaks represent the desired optimal
states, where a “rugged landscape” has many peaks surrounded by
deep valleys.
Fourth, complex
adaptive systems are recursive by nature and they recombine and evolve over
time. From a macroeconomic view point it can be posited that
industry supply networks are inter-related within a national or international
context and interact together as a CAS in a larger context (Arthur, Durlauf, & Lane, 1997). Thus, a complex adaptive system is often composed of
entities which can themselves be characterized as CASs comprised of smaller
constituents (a nested hierarchy of smaller scale complex systems). Changes in
these smaller systems and even in individual entities can cause the entire
system to change over time.
From: mphise-talk-bounces@xxxxxxxxxxxxxxxx on
behalf of Caneva, Duane C.
Sent: Mon 3/30/2009 9:28 AM
To: [mphise-talk]
Subject: Re: [mphise-talk] the MPHISE Conference - Day-1 Panel-1
Briefing-4 - preparation
I like this as a
simple explanation of complexity in systems. It is an
Army document that was used to understand the complexity of developing a
counter-insurgency program for Iraq. The challenge is in understanding
the differences in "structural complexity" and "interactive
complexity".
(1) Structural complexity is based upon the number of parts in a system.
The larger the number of independent parts in a system, the greater its
structural complexity.
Structural Complexity. It is possible for a system to have many parts
and therefore great structural complexity, but to exhibit almost no
interactive complexity. Machines function this way. A microchip may have
billions of internal circuits and therefore great structural complexity,
but its responses to a wide range of inputs are entirely predictable. It
is therefore interactively simple. Similarly, an automobile driver knows
when he puts his foot on the accelerator that his vehicle, which is
constructed from thousands of parts, will go faster.
(1) Such systems demonstrate linearity, because they exhibit
proportionality, replication, additivity, and demonstrability of cause
and effect. Proportionality means that a small input leads to a small
output, a larger input to a larger output. Push down lightly on the
accelerator, the car will go slowly, but push down heavily and its speed
will increase. Replication means that the system will respond the same
way to an input under the same conditions. Replication also allows cause
and effect to be demonstrated. Thus, a driver knows that changing the
position of the accelerator causes the speed to change.
(2) Additivity means that the whole is equal to the sum of the parts.5
The additive nature of linear systems legitimizes analysis. Analysis
reduces the system into progressively smaller components in order to
determine the properties of each. In a system that exhibits little
interactive complexity, the properties of the whole system can be
understood based upon the properties of the components. The most
effective way to study such a system is systematically6 and
quantitatively using the analytical problem solving. Unfortunately, the
operational problems confronting commanders at all levels are rarely
linear.
(2) Interactive complexity is based upon the behavior of the parts and
the resulting interactions between them. The greater the freedom of
action of each individual part and the more linkages among the
components, the greater is the system's interactive complexity.
Interactive Complexity. Interactive complexity makes a system more
challenging and unpredictable than structural complexity. These systems
are non-linear because they are not proportional, replicable, or
additive, and the link between cause and effect is ambiguous. They are
inherently unstable, irregular, and inconsistent. The most complex
systems are those that are both structurally and interactively complex.
However, even a structurally simple system can be interactively complex
and therefore unpredictable. Take for example, the highly interactive
dynamics associated with a small group of friends. A system composed of
people is inherently interactively complex because people have great
freedom of action and links to many others in their society.
(1) Reductionism and analysis are not as useful with interactively
complex systems because they lose sight of the dynamics between the
components. The study of interactively complex systems must be systemic7
rather than reductionist, and qualitative rather than quantitative, and
must use different heuristic approaches rather than analytical problem
solving.
5 Thomas Czerwinski, Coping with the Bounds: Speculations on
Nonlinearity in Military Affairs (Washington, D.C.: DoD Command and
Control Research Program, 1997), pp. 8-9.
6 Systematic: A methodical process dependant on an expectation of
prescriptive cause and effect within a closed system.
7 Systemic: A holistic approach that draws from systems theory, aimed at
understanding and influencing change in an open system. Note that system
is derived from a Greek word meaning "to combine." A systemic
understanding means combining components of a system in a context and
establishing the nature of their behavior and relationships. Systemic is
not equivalent to systematic.
(2) Since warfare represents a clash between societies or cultures,
most operational problems are both structurally and interactively
complex. Several features of the current and future operational
environment have magnified the non-linear complexity inherent in all
warfare. War amongst the people has increased the number of linkages
within the operational environment, and made the freely-formed opinions
of large groups of people on all sides-to include neutrals-important to
the outcome. The media carry images and perceptions of the ongoing
operations and each action carries an implicit message. Each Soldier
thus has potential links to the members of a global audience, and
therefore his actions can "directly impact on the outcome of [a] larger
operation."8
(3) The ways that adversaries are organized add to the complexity of the
operational environment. In many cases, the adversaries are
indistinguishable from the rest of the population. Their organizations
and objectives are not just different than the regular armies of states;
they have a completely different logic, one that makes the recognition
of cultural narratives and the study of anthropology, history, and
language essential for a more complete understanding of the nature of
the conflict.
Best,
Duane
Duane C. Caneva, MD, FACEP
Director, Medical Preparedness Policy
White House Homeland Security Council
202-456-2171 (o)
202-503-5439 (c)
202-456-6024 (f)
DCaneva@xxxxxxxxxxx
-----Original Message-----
From: mphise-talk-bounces@xxxxxxxxxxxxxxxx
[mailto:mphise-talk-bounces@xxxxxxxxxxxxxxxx]
On Behalf Of Eva K. Lee
Sent: Monday, March 30, 2009 4:59 AM
To: [mphise-talk]
Subject: Re: [mphise-talk] the MPHISE Conference - Day-1 Panel-1
Briefing-4 - preparation
George, I too have the same problem, I can login with the password to
view subscription list etc, but cannot login to view the mphise-talk
Archives. Best, Eva
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
On Sun, 29 Mar 2009, George wrote:
> Unable to gain access to view the draft slides. Encounter a userid and
> password.
> Regards,
> George
>
> -----Original Message-----
> From: mphise-talk-bounces@xxxxxxxxxxxxxxxx
> [mailto:mphise-talk-bounces@xxxxxxxxxxxxxxxx]
On Behalf Of Peter Yim
> Sent: Saturday, March 28, 2009 10:39 AM
> To: mphise-talk
> Subject: [mphise-talk] the MPHISE Conference - Day-1 Panel-1
Briefing-4 -
> preparation
>
> Mark, Leo, Steve, Bob, Ram, Susan (and Duane),
>
>
> Further to my earlier message
> (http://ontolog.cim3.net/forum/mphise-talk/2009-03/msg00024.html#nid06
> ) ...
>
> I have just posted a strawman into the wiki, comprising a draft
> outline for our presentation and the process to get us ready for that.
> See:
>
http://cosine.cim3.net/cgi-bin/wiki.pl?MphISE_Conference_Panel_114_Prep#
nid7
> 9W
>
> (repeating the initial post here) ...
>
> Proposed Briefing Outline: (8 content slides; max 10)
(7A2)
>
> 1. cover slide (7AB)
> 2. outline (7AC)
> 3. what is an ontology? (7AD)
> 4. why are ontologies, ontological engineering, ontology-based
> standards and semantic technologies relevant to MPHISE?
(7AE)
> 5. why are virtual communities of practice crucial to MPHISE?
(as a
> co-evolving human-tools capability infrastructure;
examples) (7AF)
> 6. what (institutions, standards, projects, communities, ...)
is
> already in place that is relevant to MPHISE (7AG)
> 7. what is missing (7AH)
> 8. recommendations (7AI)
> 9. references (7AJ)
>
> Proposed process: (7A3)
>
> * review outline, tweak and adopt [All: via mailng
list] (7A4)
> * (in parallel) provide input, thoughts, resources [All:
via
> mailng list, wiki & shared-file workspace] (7A5)
> o suggestion: maybe
everyone can send in a slide deck (or
> two) they have used before that are relevant to the needs here
> (7AK)
> * confirm partitioning and task assignments [All: via
mailng list]
> (7A6)
> * LeoObrst & RamSriram puts together the draft slide
deck [Leo &
> Ram] (7A7)
> o LeoObrst &
RamSriram discuss and decide on how they would
> present it at the panel session [Leo & Ram: subsequent to Mon
> 2009.03.30 conference call] (7A8)
> * team review and tweaks, and finalizes slide deck [All:
via Mon
> 2009.03.30 conference call] (7A9)
> * team discuss and possibly adopt positions (especially
on
> contentious issues) that we may take, going into the conference
> discussion [All: via Mon 2009.03.30 conference call, as well as in
> parallel on the mailing list] (7AA)
>
> Ideas, comments, suggestions ... ?
>
> Let's use this thread and that wiki page (at:
>
http://cosine.cim3.net/cgi-bin/wiki.pl?MphISE_Conference_Panel_114_Prep
> ) to get ready for the briefing.
>
>
> Best. =ppy
> --
>
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>
>
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