Companies and superlinear scaling

I am about 100 pages into Geoffrey West’s book, Scale, and am having a hard time not just skipping ahead to the parts about cities and companies.


Cities, West says, scale superlinearly (aka increasing returns to scale) whereas companies scale sublinearly (aka economy of scale). Which is why cities typically last a long time, and companies (and animals, for that matter) typically die young.

What if you could structure your company to scale superlinearly? Is it possible? If so, how would you go about making that happen? Would you even want it to happen, or is it a good thing that companies “die” young?

Back to the book….



You should always follow the rules (except when you shouldn’t)


Note: This post references concepts explained in the Cynefin framework

The typical organizational decision making process treats most operational issues as if they are Ordered, a complicated (or obvious) problem that needs to be solved. Based on your understanding of the situation you develop several courses of action, based on rules or “good practices” that have worked to some degree in the past, and implement a course of action with the belief that you can accurately predict the outcome of implementing the course of action based on past experiences. This assumes, in general, either a relatively static (non-adaptive) situation or a situation that develops in a predictable manner.

Many situations we face today, however, fall increasingly in the complex domain. In this case, you respond to the situation without any separate and discreet analysis or planning. If your actions don’t achieve the desired results, you take what you’ve learned (sense) and respond again. You don’t know what you are going to need to do until the situation presents itself, and you don’t know if it will work until after you’ve tried.

To dip into a pop culture reference, an episode of the TV show “The Last Ship” presented a scenario in which both obvious, complicated, and complex challenges presented themselves, all as part of the same situation. This is a very condensed description tailored to fit this conversation.

The ship is hunting – and being hunted by – an enemy submarine.

The Captain is on the bridge and his staff is providing him the information he needs to decide the appropriate course of action. A well defined task, with years of training and experience, he knows exactly what he needs to do, and his staff know exactly how to respond to the Captain’s orders to achieve the desired result. Obvious.

The ship’s sonar was damaged in a previous action. The Chief Engineer have a sensor that they can adapt to act as a sonar-like device to acquire the target, but have many technical, operational, and other practical considerations they must consider to make this happen. They know the constraints they have and what they need to do to make it work. The Chief coordinates each person’s actions to bring their experience to bear to plan and achieve the predicted results based on past experience. Complicated.

A land team comes across an unexpected gun emplacement threatening the ship. They don’t know exactly how many enemy personnel are manning / guarding the guns, nor do they know the terrain beyond the cover and concealment from which they will begin their assault. When asked the plan, the team leader responds simply with, “Win.” Each member of the team then executes the plan, responding as they learn more about the number of enemy personnel, the lay of the land, etc. Complex.

Organizations tend to look at all problems as if they are obvious or complicated, that we can simply apply a known rule or process and get the predicted / desired outcome. Which is great for when the problem you face is actually obvious or complicated. Too often, though, organizations prematurely try to reduce a complex problem to the point that they are obvious so that we can standardize and automate as much as possible.

When you try to solve a complex problem as if it were obvious, you are just begging for trouble.

image credit: Dave Snowden, retrieved from Wikipedia on 10 July 2017 

Complexity, Chaos and Creativity: A Journey beyond System Thinking

System thinking is goal-oriented: there are always pre-defined goals and objectives, which system must achieve, and there are always prescribed requirements and criteria, which system must satisfy. As the achievement of any goal happens always in the future, system thinking is obsessed with prediction and generating plans, blueprints, time-schedules and scenarios.

Complexity and chaos focus their attention on the present, because even tiny perturbations in the process of self-organization occurring at present can have enormous impact on the further development of this process. It is an impossible task to make the ‘butterfly effect’ follow any goal-oriented strategy and any targets’ setting anchored in the future.

Source: Complexity, Chaos and Creativity: A Journey beyond System Thinking (Dr. Vladimir Dimitrov)

Jurgen Appelo – Complexity vs Lean the Big Showdown

Lean software development promotes removing waste as one of its principles. However, complexity science seems to show that waste can have various functions. In complex systems things that look like waste can actually be a source for stability and innovation; Lean software development preaches optimize the whole as a principle, and then translates this to optimization of the value chain. However, I believe that complexity science shows us a value chain is an example of linear thinking, which usually leads to sub-optimization of the whole organization because it is a non-linear complex system.  — Jurgen Appelo

Exactly. Somewhat reflects my own thoughts and is something that has been on my mind quite a bit of late amidst an organization and projects hell bent on removing not just the optimum amount of waste from a process but removing all white space from the environment in pursuit of maximum efficiency toward the achievement of what they already know how to do. (breathe, Brett…)

As I wrote in KM vs LSS vs CPI, too often “improvement” is seen as requiring a single, all or nothing approach. When, in fact, improvement and optimal performance comes from a mix of techniques. Sometimes waste is a hindrance, and sometimes it’s where you find the gold.


Systems thinking and complexity

One of my earliest blog posts was a simple reference to complex adaptive systems. The concept was (is) fascinating to me, on many levels. Not the least of which is my unquenchable curiosity about the connectedness of everything, and an early realization that the world can be seen as a collection of systems. A systems thinker, in other words.

I think I first came across the formal concept of systems thinking in The Fifth Discipline. I was a young Army officer in the Signal Corps, responsible for leading and training young soldiers and for planning and executing communications support missions. Many of my colleagues approached the role from a very rigid, very structured, very “mechanical” perspective. Not unexpected, of course, since military units in general are very highly structured and driven from the top down by command and control – “Here’s what you should do, and I’m going to watch you to make sure you do it so we achieve this very specific outcome.”

As if anything ever works out the way you plan. Understanding my job, the role of my unit, as a component of a larger system that could be manipulated helped me to provide the best support I could to the units that depended on what we provided. (The beginnings, perhaps, of my understanding and application of user-centered design and service design, perhaps?)

I really don’t remember what triggered my interest in complexity. This, I think, is something that has always lingered just below the surface in my mind. If I had to pinpoint a single starting point for the beginning of my slow hunch about complexity it would have to be Douglas Hofstadter’s Godel, Escher, Bach – A Golden Eternal Braid. I came across this book in my latter years of high school and made my way through it as best I could. Though I didn’t really understand much of it at the time, it primed my thinking to be more receptive to a different way of viewing the world.

Then came James Gleick’s Chaos and Michael Crichton’s Jurassic Park. My interest in the science and philosophy of Richard Feynman led me to Murray Gell-Mann and the Santa Fe Institute. Eventually I found my way to the work of Dave Snowden and his insights into the application of systems thinking and complexity science to the world of work, however broadly or narrowly you might define this. (Though I have some of this documented in my notebooks from the time (90’s), I wish I had been blogging back then so I had a better record of my thinking.)

Systems thinking and complexity have thus spent a lot of time in my mind, side by side as I try to make sense of them and understand how to apply them to life and work. To be sure, I have often simply treated them as “basically the same thing”, without much effort to distinguish between them. Though they share some key characteristics they are, of course, different. But what are those differences, and why does it matter? Heading in to the new year seems to be a good time to delve into this.

Fortunately for me in this regard, I recently discovered an article from 2013 by Sonja Blignaut that has pointed me down a good path for this exploration. Titled appropriately enough 5 Differences between complexity & systems thinking, the article is a summary of her notes and thoughts from some time spent with Dave Snowden as he presented workshops and worked with clients.

In the coming days I’ll be looking at those 5 differences in detail.

Welcome to The Emergent Era – Emergent Era – Medium

As our information moves faster, we move faster. And as more of humanity comes online (2.3 billion more people in 2016–2017 alone), it’s causing a fundamental and spontaneous restructuring of our collective behavior. The overlay of our evolving planet wide digital nervous system has taken the perennial drivers of change — human needs, politics, geography, culture — and woven new patterns from them. All of us, especially those who are guiding businesses, need a new framework to understand and adapt.

Source: Welcome to The Emergent Era – Emergent Era – Medium

Coherence through shared abstraction – Cognitive Edge

Scaling in a complex system is fractal, or self similar in nature. In effect we decompose to an optimal level of granularity then allow coupling and recouping of the granules to create new patterns all of which have a self-similar relationship to each other. One of the things we are doing with our adaptation of fitness landscapes within SenseMaker® is to allow the same source data to represent itself for different identity structures within an organisation in contextually appropriate ways. Nothing in a complex system is context free, everything is context specific.

Source: Coherence through shared abstraction – Cognitive Edge