This document discusses complexity and complex adaptive systems. It notes that complexity grows when systems are overlaid on infrastructure and interact with human populations. It then discusses complex adaptive environments, noting they are characterized by complexity, adaptation, and surprise/novelty. Adaptive management is discussed as a way to manage complex systems through experimentation, collaboration, and building trust over time through practice and "play." Decision spaces are proposed as a way for diverse groups to safely discuss problems, apply tools, and decide on actions.

1. The word “complexity” is a growing part of everyday language. People texting while they
drive is an appropriate metaphor of the struggles we have keeping up with information
technology in our daily lives. The information industry promises us everything from instant
access to our data, to a 3 minute response to a 911 call, to any entertainment we desire
such as games and movies – on demand! The systems that deliver these services are
complex. And complexity grows exponentially when overlaid on terrain, political and
infrastructure topologies and interface with dynamics created by human populations.
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2. I have over 27 years of experience in training and education – it is my passion. Whether
working for ITT Technical Institute or working for a manufacturing company, there were
always elements of experiential learning that could be leveraged to enhance problem‐
solving. I believe that problem solving is what makes us human, and what drives the
greatest satisfaction in life (well, besides kids and tax‐free interest).
I have concurrent experience as a military officer with experience dealing with large scale
public emergencies, such as the Howard Hanson Dam Rehabilitation Project, Oso, and the
development and validation of the Homeland Response Force.
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4. The Complex Adaptive Environment – this is what it is all about. The term “Complex
Adaptive Environment” comes from ecologists such as Lance Gunderson, C.S. Holling and
others and their theory about the complexities and inherent adaptive capabilities within
ecosystems. In much of their work, they have noted potential applications of this theory in
economics, business, and other human affairs.
The Complex Adaptive Environment has many characteristics, but there are three that I will
focus on today:
• Complexity – what is it, what are some ways that we can describe it?
• Adaptation – How does adaptation occur?
• Surprise and novelty – How do surprise events spur innovation and experimentation?
Gunderson, L.H., C.S. Holling, and S.S. Light. 1995b. Barriers broken and bridges built: A
synthesis. Pages 489‐532 in Barriers and Bridges to the Renewal of Ecosystems and
Institutions, L.H. Gunderson, C.S. Holling, and S.S. Light (editors). Columbia University Press,
New York.
Walters, C.J. 1986. Adaptive Management of Renewable Resources. Macmillan, New York.
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5. While the ecosystem way of describing environments and their interactions is compelling,
the authors of this theory also recognize that these representations, by themselves, are not
adequate to describe what is actually going on. What is necessary (and this is a key point),
is active experimentation within the context of natural and human processes to improve
knowledge of the complex system and how it interacts. A key point in our discussion today
is that experimentation is a needed component of collaborative problem solving.
Experimentation, when conducted using an agreed upon methodology, provides data that
reinforces the way forward. Otherwise, members of a collaborative group must resort to
their intuition and “belief” of what “right” looks like.
Belief is always a source of conflict.
C.S. Holling, L.H. Gunderson, D. Ludwig, 2002, In quest of a theory of adaptive change, page
10 in, Panarchy: Understanding Transformations in Human and Natural Systems, Island
Press, Washington D.C.
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6. Take a moment and reflect on the idea of complexity.
• We start with a single node that interacts with other nodes around it.
• Then it builds out to interact with other systems in three dimensional way. The strength
of these connections can vary and differ in terms of duration, and what resources they
share.
• As the network expands, so does the interconnectedness and overall resilience and
stability of the system. If any one node is affected, the others may expand in order to
fill‐in the gap.
• What is not depicted very well, is that these nodes can also have relationships across
and within the grid.
• Thus ‐ highly networked entities are stable and resilient.
• Another thing not depicted in the cube, are the nature of the relationships between the
nodes. These relationships vary in strength, are often non‐linear, episodic and irregular.
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7. In our “ecosystem” of cyber we can think of our systems as occupying layers. Each layer,
such as the transportation layer, represents a capability. Entities on each of these layers
have dependencies and relationships with other entities on these layers, and in‐between
layers.
The Transportation Layer overlays the earth and is a network of roads, bridges, electrical,
fiber‐optic and water and provides a means of conveyance.
Above that is the population layer, which represents people and their community and
ethnic ties. This layer is particularly dynamic, because people interact with each other and
move around very quickly. Furthermore, people are a source of demand and they load
resources for our systems. The population layer interacts with the transportation layer,
because people move along roads.
On top there is a layer of coordinating entities such as businesses, logistics, government,
hospitals and others. It is very important for these agencies to be able to communicate and
coordinate during a surprise event. They must coordinate to move resources to demand
and they too are dependent on the transportation layer to convey instructions to entities
that would act on those instructions.
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8. So to summarize – complexity is, well, complicated! Because of all of the nuances,
behaviors, cycles and non‐linearity of these types of systems, it requires an adaptable form
of management.
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9. Adaptive Management offer us promise. It focuses on a “Communicative Planning” style
that originates from diplomacy, negotiations and consensus building. Diplomacy,
negotiation and consensus are perhaps the three most antithetical words in the world, to
the idea of speed and progress. Nevertheless, planning is fundamentally agreement to
purpose and resources among a diverse group of people.
Adaptive management differs from command and control. Command and control uses a
reductive planning approach to simplify and prioritize activities in a plan. Command and
control is often used because of the speed at which it can be applied, but it is criticized for
its lack of granularity and over dependence on a single person’s judgment.
Communicative Planning is widely used when plans must be developed between multiple
jurisdictions, agencies or in multiple function incident response. This planning style is
necessary, because of the diverse group of interests, authorities and resources that are
drawn to a single purpose – problem solving.
We also see communicative planning used in incident responses, as these are “just‐in‐time”
organizations built with people who may not have ever worked together. These
organizations typically use a “just‐in‐time” planning model, since the situation is novel and
the combination of necessary resources is unknown. The main thing to remember about
Adaptive Management, is that it relies on trust and active experimentation to work.
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10. Resilience and rebuilding all depend on effective collaboration. It is collaboration that
forms the relationships between interests in a human system.1 As we probably all have
experience within collaborations, we recognize that “trust” is an absolutely vital currency in
a working collaboration. Trust is built on observable behaviors, actions and experience.2
So how can we build trust before an event occurs? The answer is regular and repetitive
training or “PLAY.”
1 Connie P. Ozawa, Planning Resilient Communities: Insights from Experiences with Risky
Technologies, in Collaborative Resilience: Moving Through Crisis to Opportunity, edited by
Bruce Evan Goldstein, (MIT Press: Cambridge, MA, 2012), p. 22
2Ibid, p. 23
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11. Play. The bottom line is that collaborative groups need to be like, “Kids playing with blocks.” A
Stanford University study of children playing blocks found that the benefits to learning go
beyond being able to stack blocks. While the term itself may seem trivial, it has much deeper
meaning and utility. Play is the dynamic that exists between children that not only provides a
laboratory for problem solving, but the social context for working together to solve problems.
Children during play, learn how to interact with each other, establish rules for working
together and actively experiment with a variety of combinations to solve the problem.
Here’s the link:
http://www.npr.org/blogs/ed/2015/02/06/384347659/behold‐the‐humble‐block‐tools‐of‐
the‐trade
Surprise events will push a complex system past its limits. In order to adapt and reconstruct
itself, the system will require novelty. Novelty requires active experimentation, interaction
and a healthy dialectic in a highly diverse collaborative group.
By deliberately designing decision spaces, collaborations can engage in meaningful
conversation, actively experiment using simulations and develop trust with playmates.
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12. I define a “Decision Space” as a safe environment where a diverse group of unaffiliated
people can gather to develop a common perspective of a problem, apply accepted tools to
purpose and decide to act at an optimum point in time.
The principles that help us to collaborate and achieve a decision are1:
1. Diversity ‐ It is important to make sure that the entities in the complex environment
are properly represented. If interested groups are left out, they may become a
stumbling block later on.
2. Data – Problems in complex systems are dynamic. Therefore, problem solvers will need
data and agree how they problem will be framed. Defining the problem is fundamental
for establishing the way forward, or for determining what the group needs to know.
3. Vocabulary – there needs to be a common language to classify and describe the
problem.
4. Rules of thumb – Heuristics are the necessary “building blocks” these groups would use
to streamline the problem solving process.
5. Modeling – The use of computer‐aided modeling to control the many variables, map
relationships between variables and to provide an end‐to‐end visualization of the
complex system is necessary for stakeholder’s to see the effects of problem solving
measures. Furthermore, modeling through simulations allows collaborators in a
decision space to play their problem solving forward to evaluate possible futures.
1Morris Zellner, Charles Hoch and Eric Welch, Leaping Forward: Building Resilience by
Communicating Vulnerability, in Collaborative Resilience: Moving Through Crisis to
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13. Opportunity, Edited By Bruce Evan Goldstein, (MIT Press: Cambridge, MA, 2012) p. 43‐44.
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14. Predictive modeling or simulations have come a long way. Computers are getting cheaper,
our understanding of complexity and adaptive systems is getting better. Modeling has
many off‐shoots of development, but I will focus on systems dynamics and agent based
modeling (and their hybrids).
Systems dynamics modeling uses mathematics to account for entities in a system and the
relationships they share. These are typically described as “stocks, flows and feedback.”
Stocks are accumulations of potential in an entity and flows are those potentials moving to
another entity. So for example, if I represent a stock, and I have $100 in the bank that I use
to pay a Visa bill, the systems model would show $100 flowing from the “me” entity to the
“Visa” entity. Let’s say Visa give me rewards points, then those would flow back to the
“me” entity as a feedback loop.
Agent modeling is a unique way to account for human behavior. An agent is an individual
agent, like a little robot, that follows certain behavioral rules. Interestingly, as you
aggregate these behaviors the predictive capabilities become more accurate. Agent
modeling is often useful when you are modeling migrations, consumption and loading
dynamics in your model. So if you refer to the population layer, this would be modeled
using agents as they can move around using various services such as roads and sheltering.
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15. A visual‐interactive environment is integral to sustaining the decision making process.
This is a key capability, because quite literally, it can be everything from a dashboard to a
3D simulation that very nearly replicates a physical environment.
Visualization is important to sustain collaboration, because it gives the group a method to
see the effects of their decision making. Without visualization, it becomes increasingly
more difficult for participants to articulate a vision of how the alternatives will work in a
systems context.
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16. I’ll conclude this presentation with a few ideas:
1. The problems we face come from complex systems and the entities in these systems
share relationships on and in‐between layers that are non‐linear, episodic and difficult
to predict.
2. To solve these problems we need adaptive management and collaboration enabled by
effective decision spaces.
3. Effective decision spaces enable us to be like children playing with blocks –
collaborative, experimental and iteratively learning and applying our knowledge
4. Simulations can model the behavior of these systems and allow us to play with blocks,
to safely and repetitively experiment and find solutions.
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