This is a chapter from Amplio Development: The Path to Effective Lean-Agile Teams

Abstract: Systems thinking tells us that the relationships between the components of the system are more important than the components themselves. The system is the behavior resulting from these relationships – not the sum of the parts. A system is more complex the more these relationships are so interrelated that we can’t see or understand them. Most systems with people are complex to some extent if only because improving it involves getting people enrolled in change.

Complexity is not the only characteristic of a system, however. There are relationships that are complicated (discernible but not obvious at first glance), coupled (meaning changing one relationship affects another), and susceptible to non-linear events (a small change in one place creates a significant change in another).

Looking at systems this way enables us to take advantage of what we know about them even if some aspects are impossible to see at the beginning. Amplio does not attempt to create an ontology of systems in general, but rather focuses on knowledge work. This enables us to take a three-prong approach. The first is to use first principles, the factors for effective value streams discussed earlier in this book, and any other insights we have, and to provide insights into what is happening. The second is to create tight feedback loops at all levels. The third is to make changes based on the factors for effective value streams. This results in an improvement or reveals a relationship we either hadn’t seen or understood. The result is improvement and learning.

Navigating complexity means making good decisions, not necessarily being able to make perfect predictions. The key is to avoid the waste of non-linear events while being able to respond to events beyond our control.

Amplio’s approach recognizes that there are things we know, things we don’t know, and things we don’t know we don’t know. We know all of these are embedded in our work. Our approach is not to worry about what we don’t and possibly can’t know but rather to be able to deal with the consequences of this reality.

There are four areas to contend with:

  1. Our workflow. (In our control) Here we can guide ourselves reasonably effectively. We improve, or we learn.
  2. The product to build. (Partially in our control). By attending to the customer journey and the values of our stakeholders, we can understand our customers’ needs and how we must innovate for them. At times we’ll have to run safe-to-fail experiments, but that should not be the standard approach.
  3. Forces outside of us. (Not in our control). Having an effective workflow (#1) and seeing customer needs (#2) enables us to pivot when Black Swan events occur. This creates a competitive advantage.
  4. The people involved. How people will behave in a system is clearly complex. But there are still many aspects we can predict. More important is that our best path to improvement is creating joint understanding and alignment.

Paraphrasing Neo and Morpheus:

Neo – What are you trying to tell me, that I can understand complexity?

Morpheus – No Neo. I’m trying to tell you that when you’re ready you won’t have to.

Many people in the Agile community have embraced complexity. This has had a disempowering effect. Complexity theories tend to dismiss systems-thinkers as being naïve. However, systems thinking does attend to complexity, just differently.

Dr. Eli Goldratt, the creator of the Theory of Constraints, says, in The Choice:

“The first and most profound obstacle is that people believe that reality is complex, and therefore they are looking for sophisticated explanations for complicated solutions. Do you understand how devastating this is?”

“If we dive deep enough, we’ll find that there are very few elements at the base—the root causes— which through cause-and-effect connections are governing the whole system.”

This is especially true in knowledge work.

In Normal Accidents: Living With High-Risk Technologies, Charles Perrow laid out a deep understanding of complex systems and how they will create unpredictable behavior. He classified two types of forces interacting with each other: complexity and coupling. How these interact is shown in figure 1.

Figure 1: Charles Perrow’s Interactions Vs. Coupling Table from Normal Accidents.

It is worth noting that when people think of complex systems, they think of Three Mile Island, the Titanic, etc. Those systems would be in the upper right-hand quadrant. But knowledge work (he calls it R&D Firms) are in the bottom right. In this quadrant, we can avoid system failures that are impossible to avoid in complex and tightly coupled systems. Dr. Perrow discusses this here:

Systems with interactive complexity (cells 2 and 4) will produce unexpected interactions among multiple failures. But while these are troublesome and unwanted, they need not bring about accidents—that is, damage to a subsystem or the system. Accidents will be avoided if the system is also loosely coupled (cell 4, universities, and R&D units) because loose coupling gives time, resources, and alternative paths to cope with the disturbance and limits its impact. But to make use of these advantages of loose coupling, those at the point of disturbance must be free to interpret the situation and take corrective action. Since the disturbances are generally (not always) likely to be experienced first by operators (which include first-line supervisors and other on-duty personnel such as technicians and maintenance), this means the system should be decentralized.

Perrow, Charles. Normal Accidents (p. 331). Princeton University Press. Kindle Edition.

To state this in another way, we can avoid disasters if we can avoid coupling and dampen what might become a non-linear event. Disasters happen when a simple event becomes non-linear because things are coupled, and there is no visibility of what is happening. Complexity is the culprit, primarily because it hides that this is happening.

In knowledge work, it is essential to realize that much of what is going on can be explained through fundamental theories of Flow, Lean, and the Theory of Constraints.  However, many relationships are obscured by the complexity of the system. Much of the waste in knowledge work is due to this obfuscation.

We can improve our way of working by creating visibility, adding feedback, & decoupling events to avoid a cascade of errors. While often complex, we can see the relationships between people’s actions when they are doing knowledge work. We can’t assume a predictable path forward because we can never be sure how people will react. But exploring the causality that is present creates an opportunity for improvement.

Examining this causality is essential for another reason. When you disavow cause and effect because the system is “complex,” you also weaken the likelihood that people will want to change. Why should they if it’s all a guess? Exploring what cause & effect there is reveals other relationships that can make a difference. Learning and improvement is an emergent process.

The factors for effective value streams  is one way of looking at the cause and effect in the system. When one does an informal analysis of challenges Agile teams and organizations are having, I believe the causes of their challenges are (in order of both frequency and impact):

  • lack of knowledge of first principles
  • lack of systems thinking
  • the above results in a lack of attention to the value stream
  • non-linear events due to slow feedback, lack of visibility, and coupling

A non-linear event (also called a “chaotic event”) is when a small action causes a large output. On a graph, we would see what starts as a line and then jumps up at some point – non-line-> non-linear. “The straw that broke the camel’s back” is an example.  In knowledge work, this is often the impact of a slight misunderstanding of a requirement.

Complexity does cause problems, but mostly because when something unexpected happens, the above failures make it difficult for a team or organization to respond. We should consider dealing with internal complexity and the fact that teams and organizations are embedded in other complex systems. We can reduce most of the waste caused by internal complexity by attending to the above. While we can’t predict what will happen in the market or the world, we can respond quickly when needed by being effective internally.

We can mitigate complexity by recognizing that much of the waste present is due to non-linear events. That is, minor errors create big waste. But we can dampen this exponential waste. Virtually all disasters due to complexity are caused by these four factors – complexity, non-linear events, coupling, and lack of visibility. Think of disasters caused by complexity as gun powder blowing up. Gun powder consists of a fuel (charcoal), an oxidizer (saltpeter or niter), and a stabilizer (sulfur) to allow for a constant reaction. Complexity represents the fuel. If you don’t provide the oxidizer and stabilizer, you won’t get an explosion.

You don’t need to manage complexity; you manage what makes complexity risky. Use feedback to mitigate the risk of non-linear events. Avoid coupling when possible and always make it visible.

The claim is not that the world is not complex; the approach to complexity can be what stops us more than the complexity itself. Remember that our systems are more about the relationships between the components than the components themselves. In complex systems (such as knowledge work), some of these relationships are poorly seen or misunderstood.  But if we attend to first principles, use feedback to create visibility, and de-couple our work, we can avoid the waste complexity causes. We can also improve our approach in an emergent way. Make predictions based on first principles and the factors for effective value streams. These will either be reasonably accurate or will clarify a relationship we either weren’t aware of or which was misunderstood.

We do not need to let complexity impede us.

Amplio and Cynefin

Amplio doesn’t use Cynefin. While Cynefin can be used in any of the spaces above, Amplio has been designed to take advantage of what’s known about how knowledge work is done. This is very important. Amplio takes advantage of the context and constraints knowledge has. When approaches are made to be more generic as Cynefin is, it often becomes abstract to some people. This is good in academia where one is trying to prove general theories but is often not as effective in solving problems pragmatically.

Before going into the differences between Amplio and Cynefin, it’s worth reading some insights from Dr. Eli Goldratt’s The Choice.

“The first and most profound obstacle is that people believe that reality is complex, and therefore they are looking for sophisticated explanations for complicated solutions. Do you understand how devastating this is?”

“When I left physics and started to deal with organizations, I was astonished to see that the attitude of most people is that the more sophisticated something is, the more respectable it is. This ridiculous fascination with sophistication also causes people to altogether avoid using their brain power. You see, since complicated solutions never work, people tell themselves that they don’t know enough. That a lot of detailed knowledge is needed before one can even attempt to understand an environment.”

“The key for thinking like a true scientist is the acceptance that any real-life situation, no matter how complex it initially looks, once understood, is actually embarrassingly simple. Moreover, if the situation is based on human interactions, you probably already have enough knowledge to begin with.”

“Inherent Simplicity. In a nutshell, it is at the foundation of all modern science as put by Newton: ‘nature is exceedingly simple and harmonious with itself.’”

If we dive deep enough, we’ll find that there are very few elements at the base – the root causes – which through cause-and-effect connections are governing the whole system.”

From his daughter, Efrat -“The hardest thing to do is to struggle to find an answer to a problem when we believe that there is a high chance that it doesn’t have an answer; it is so easy to give up. That is why Father recommends starting with the conviction that a better solution exists for sure.”

“The difficulty is that if I’m not sure, really sure that a second effect does exist, I might stay inside the box; it is always safer to stay within the comfortable boundaries of a box than to jump out into the unknown. Since the other effect is not within that box, I will not find it. I’ll give up searching and remain stuck with a tautology (circular logic).”

“A comfort zone has less to do with control and more to do with knowledge.”

These insights create awareness of how people are going to react to any approach to complexity.

These are the biggest differences between Amplio’s and Cynefin’s approaches:

  1. Amplio only concerns itself with knowledge work, not all types of work as shown in Figure 2.
  2. Amplio views that knowledge work has aspects of simple (Cynefin calls this clear), complicated, complex, and chaos in it. Systems thinking tells us we can’t decompose into parts and still understand the system. What varies is how much of each of these is present.
  3. Amplio pays particular attention to non-linearity, the cause of most rework and waste in knowledge work. Cynefin doesn’t mention non-linearity but talks about chaos as a state.
  4. Amplio is based on Inherent Simplicity, the concept that what appears to be complex can be viewed as the result of just a few concepts. Amplio has identified these in its factors for effective value streams.
  5. Amplio suggests taking actions based on the factors for effective value streams knowing that it may be ineffective due to complex relationships that aren’t seen or understood. But when that happens, learning will take place and better prepare us for future improvement.
  6. Amplio has its approach to complexity integrated into its approach for general improvement. This means you don’t need to take additional training or add a new model to use it. In a nutshell, Amplio has us work on the cause and effect we can see to improve our methods and understanding. When we don’t get the response expected we improve our understanding by seeing some relationship we hadn’t seen before.

We want to keep Edgar Schein’s mantra “We don’t think and talk about what we see, we see what we are able to think and talk about.” Amplio keeps us focused on actions that usually make a difference. We want to learn more about where these work and where they don’t. This lets us act on those areas we have control over (our organization) and prepare for events outside of our control – that is, enabling us to pivot to black swan events.

Many people find Cynefin’s attending to different types of domains useful. Many executives don’t appreciate the difference between complicated (many interactions but all can be understood), and complex (many interactions can’t be understood, and that true understanding must emerge. I find using VUCA – volatility, uncertainty, complexity, and ambiguity to be effective in doing this.

Instead of focusing on understanding what domain we’re in we presume all domains have a combination of simple, complicated, complex, and susceptible to non-linear events. Doing this enables us to see how to eliminate much of the waste that occurs in knowledge work. In addition, by enabling quick pivoting, Amplio facilitates responding to Black Swan events that we, by definition, can’t foresee.

This has a track record of success.

This focus is illustrated in Figure 2.

Figure 2: Charles Perrow’s Interactions Vs. Coupling Table from Normal Accidents with comments added.

Examples Of Potentially Non-linear Events

Many small errors can each turn into big waste.

  • a slight misunderstanding in requirements that leads to building the wrong thing
  • a one-off coding error that causes intermittent problems
  • a tight schedule and one person’s schedule changes and then is not available when they need to be and it causes other problems
  • doing analysis on too many items, then they don’t all get started in a sprint, they get started on the next sprint, but no one took advantage of what was learned

A Sidenote on Why It’s Helpful That Amplio’s Approach Inherently Deals With Complexity

While this chapter explains how Amplio deals with complexity, it is important to note that this is integrated into how Amplio works. That is, quick feedback, decoupling, and being guided by the factors for effective value streams, the driver for Amplio also deals with the challenges that complexity in knowledge work presents to us. This is by design – that is, the normal moment-by-moment working of Amplio inherently deals with the complexity of knowledge work.

This is important for two reasons. First, the risks of complexity can strike at any time. The way teams work must deal with it at all times. Second, by integrating it into Amplio teams have to learn less to be effective. While additional training may be useful, it’s not required to deal with complexity at the start.

 

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