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Thursday, February 28, 2019

Getting caught up in complexity science (summary of a workshop offered by the OPM Lab)

Yesterday a colleague and I went into downtown DC to a workshop on Complexity Science offered by the OPM Lab. (They may offer it again so keep an eye on their calendar! https://lab.opm.gov/class-sign-up)

What is complexity science?


Complexity science is a relatively new approach to studying complex systems and problems that are unpredictable, ever-changing and consist of multiple interconnected parts. The relationships the parts have to each other end up being the complex component to study.

Older approaches have included the tantalizing idea that if we could only accumulate enough information about how individual pieces of a system work, then we can predict how it will go when put together. This reductionist, simplistic view might work for dissecting a car engine but doesn’t apply to social systems, societies, economies, and the myriad other complicated structures we live within today. 

Think about the institution of marriage, for example. Understanding individual human behavior is one entire realm and two together (or a family!) is an entirely different one. It’s the relationship the parts have that make up the complex entity.

Part 1: mental models and paradigms


Throughout history, people have constantly sought to understand the world around them by constructing models. It’s how we make sense of the world, how we organize and take in information, and how we take action. It’s also a filter on the data we take in. The models may not necessarily be right or wrong, but they’re almost always limited. 

For example, this metro map:



Are the tracks really that parallel? No. Is it to scale? Nope. But it’s useful. That’s the purpose of having models, for utility. But we have to remember that they’re often simplifications of the real system.
“Our lives are organized by models. Once we have a model of something, the answers/decisions we come to are shaped by that model, and all models are wrong  - otherwise they wouldn’t be models. So having multiple models to look at a problem is critical.” Roger L. Martin (@RogerLMartin)
The trouble with constructing a mental model of how things work is that when data contradicts what what we believe, it can be easy to dismiss. Humans can be so focused on one aspect they miss what might have been obvious if they hadn’t narrowed their view.

For example, if you follow the below test (1:21) to see how many times players in white shirts pass the ball:


You can miss that a gorilla (suited person, that is) marched right in and walked through the whole
scene.

Knowing how easy it can be to miss the obvious can help us offset this part of human nature.

What is a paradigm?


A paradigm is a shared or collective mental model. It can be quasi-metaphysical, almost like religion. The assumptions held are almost locked into place. 

People begin solving problems from within paradigms which can be faulty because the starting ground influences the questions we ask and good problem-solving starts with understanding and framing the problem, which can only be done when we begin to recognize held assumptions. 

For example, assuming outspoken folks represent a whole group when asking, “Does anyone have any questions?” Or assuming that no one had any questions because no one speaks up. That may be true but it may also be possible that some individuals didn’t feel comfortable speaking up. And solving the problem of how to encourage more to add input can be tricky because maybe there is a singular cause or maybe multiple factors relating to the work environment, personality, history, relationships with others in the room, or even the kind of day someone is having. 

If the paradigm or model isn’t 100% correct (but still useful) we can build data points that end up not fitting and requiring a whole new model. For example, in Copernicus’ time, the view then was that the earth was the center of the universe. When data began to contradict that (as technology grew and gave us better data), it became harder and harder to ignore that it was insufficient. It eventually gave way to the heliocentric model we have today that the earth revolves around the sun. (But don’t ask today’s flat earthers what they think!)

When paradigms shift, they can shift quickly. For example, the below photo looks either like a rabbit or a duck. Once you can see both, you can switch back & forth fairly easily. But it’s an effort to switch at first.


As our knowledge of physical systems increases, it was tempting to think we could increasingly predict and control human systems. To imagine that if we just had enough knowledge, we could predict order. This deterministic view doesn’t work in real life. But it’s compelling. People appreciate order and rationality.

Order brought great benefits during the industrial age but the belief that more order is better and that some sort of final new order could be constructed and locked into place doesn’t fit for complicated individuals living in a complicated society.

Part 2: What do we mean when we talk about complexity?


Complexity has:
  • Emergent behavior 
  • a property held by the collective which is NOT held by individual 
  • no central control
  • each individual follows a very simple rule set
Have you seen the clouds of starlings gracing our skies every fall like schools of fish, darting this way and that? They’re captivating. The phenomenon is called a “murmuration” and the behavior looks very complex. In reality, each starling is following simple rules involving their seven closest neighbors where they respond to rate of speed and distance (among other factors). The effect this has on the entire group is its own complex, observable phenomenon. That is complexity.




  • Interdependence of the parts 
    • Things that are connected and depend on each other, like plants relying on fungus, bacteria, and animals to live.
The relationship between the parts that give rise to the collective behaviors of a system and how this system interacts and forms relationships with its environment is the object of study.

In user experience, designers (not necessarily graphic designers, which is what we usually think about when we hear the label “designer” but can also include the field of problem solving by design) divine what’s complex in order to make a system or process less complicated for the end user. It takes a lot of work to simplify the complex and requires multiple models to effectively problem-solve.

Part 3: key aspects of complexity for designers


The more complex or variable something is, the less quickly or easily it can be achieved on a mass scale. This is also known as a “coherent behavior” in the field but basically describes the limitations that exist in problem-solving. For example, the Model T Ford was achieved on a mass scale but it wasn’t possible to have customized colors. There are always limitations. It’s like the old saying, “Quality, speed, price: pick any two.” Scale and speed are inversely proportionate.

Things that affect scalability are constrictions on resources, like not having enough staff, time, or money. For example, applying design thinking to a communities which needed better healthcare meant redesigning the process. If providers could triage groups and branch those with simpler issues like colds or flu to a nurse practitioner, it provided more time for docs to see those with more complicated problems. (Healthcare is a great example of a complex problem because while this simple solution is useful, it doesn’t address or fix everything.)

There is a law in complexity science which describes how a system needs to be able to respond to the complexity in each of its variables or it can be disrupted. This is known as Ashby’s law of requisite variety. The world is full of disruptive technology and processes and systems and we face many opportunities to deconstruct insufficient models in order to more effectively problem-solve. 

Part 4: where Human-Centered Design fits in


A good designer is also a field ethnographer. They go out into the world and observe what they see rather than starting blindly with assumptions. Their job is to collect data and construct meaning. The broader their understanding through multiple data points, the better the ability to problem-solve. When they see outliers in data points, they don’t dismiss them. They wonder if there’s something deeper that needs to be understood. They ask more questions.

You may have heard the terms “waterfall” and “agile” before? They’re approaches to problem solving.
  • Waterfall approach: This is the old-fashioned way. It takes the info at hand to design a solution and then delivers results. It is a modernist, reductionist approach and doesn’t encompass the reality that all the limitations cannot be known in advance.
  • Agile approach: This is a newer approach and involves smaller steps, continuously developing prototypes and refining along the way. This allows failure to occur at a smaller level and reduces the amount of resources invested. (It’s a useful methodology not just for systems but life: for example, learning we cannot accommodate three cheeseburgers at lunch! Adjust quantity before we cater our next family event. :)
It’s crucial to spend a lot of time defining and framing the problem. Breakthrough innovation is about seeing old problems in new ways and effective problem definition avoids wasting resources on solving the wrong problems and expands the range of possible solutions.

Want to learn more?

For more information: 


___________________________

Workshop description from OPM:


Get Caught Up in Complexity (Science) at the Lab at OPM
By Garth Jensen, Director of Innovation at the Naval Surface Warfare Center.

What is Complexity Science?

Complexity Science is a field focused on studying how the network of relationships among the elements of a system influence the behavior of those elements, the system itself, and the surrounding ecosystem. These behaviors cannot be discerned by studying the elements separately, regardless of how detailed that study might be. Complexity Science provides tools that assist in thinking about, describing, modeling, and assessing these elements and relationships.

Why Study Complexity?

Federal Government agencies are complex adaptive systems, operating at different scales, and deal with complex problems every day at different scales. Yet, there is little awareness and understanding of complexity, and how that science might apply to our organizational designs, issues, plans, operations, policies, and practices.

As a result, we tend to get stuck in a linear, mechanistic, reductionist paradigm, producing results that address only surface symptoms, create unintended consequences, or make the original problem worse. This lack of understanding of complexity has a profound effect on our organizational agility and our ability to keep pace in a rapidly changing world.

This prototype workshop will:
  • Provide attendees an with overview of complexity;
  • Use tools for intuitively thinking about and learning to recognize complexity in real life, and;
  • Enable participants to synthesize workshop activities and themes together to consider how one might practice design differently based on these insights.
Bio:

Garth Jensen is the Director for Innovation at the Carderock Division of the Naval Surface Warfare Center. Mr. Jensen is responsible for initiating, designing, prototyping, assessing, and scaling cultural interventions that create and sustain a culture of innovation. Mr. Jensen was instrumental in creating, piloting and implementing mmowgli(Massive Multiplayer Online Wargame Leveraging the Internet), a crowdsourcing and gamification platform.now used regularly to harvest fresh insight from it’s more than 16,000 players worldwide.

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