scaled agile – Keener Strategy

Transformation is the systemic restructuring of the knowledge production processes and decision-making networks within an organization. While a precise history and understanding of the organization status quo is impossible, it is also foolhardy to begin a transformation without any respect for the system as we find it. Mature organizations adapt over extended periods of time to a unique pattern of decision-making. The individual workers change continuously, so the organization replicates knowledge of the decision-making processes as patterns of behavior increasingly distant from original context. The systemic understanding of the original context for the behaviors becomes separate from the decision-making units performing the behavior. The origin of organizational knowledge becomes increasingly distant from the processes using outdated knowledge.

Maturity produces stability at the expense of adaptability, just as the bones of an adult gain load-bearing capacity at the expense of the trauma-bearing malleability of the bones in a child. Children rarely need to lift heavy objects but frequently fall, while a young father may frequently lift and move heavy objects but falls that might break his adult bones are very rare. The goal here is not to contend that transformation is impossible or that maturity is superior to malleability. Instead, we should recognize from the outset that each have costs and benefits. The first consideration of any disruptive influence must be what purpose current adaptations serve.

We should “start at the beginning” then, and define what an organization is, why it survives, and what it means that it matures. Every organization is a combination of decision-making units that cooperate collectively in the expectation of individual benefit. However, the decision-making unit is neither the collective nor the individual. The totality does not make decisions independent of the individuals comprising it. The individual, though self-interested, never makes decisions in a vacuum. Therefore, the decision-making units within the organization could be pairs of individuals, formally identified groups, or informal teams who act together. Likewise, these individuals are not exclusively participating in the decision-making unit that performs within the boundary of the organization. The knowledge worker might make decisions within the organization as part of a jobsite decision-making unit, as a family-system provider, as an alumnus of a university, or as a thought-leader in a professional community. If we lose ourselves in consideration of the individuals, we may become convinced of chaos and uncertainty, never knowing if cooperative self-interest will optimize the family, fraternity, or career prospects at the organization’s expense. However, as we scale to include larger groups we find that emergent consistencies hold despite these individual differences. The operational “team” follows patterns of behavior even as individuals join or exit. We should thereby place our consideration of the decision-making unit at this “team” level.

The decision-making unit is not the individual, while each of these individuals participate in a multitude of decision-making units. We need not apply a hard constraint to the number of individuals a decision-making unit may contain in practice, though we may say with confidence that one of two constraints limit this size. First, beyond 10 individuals it will become evident that a subset of members is the informal decision-making unit within the formal collective. These leaders must agree or a decision fails, while the remainder provide knowledge but will defer to group decisions. The ability to remain silent altogether increases as the diffusion of responsibility, whether economic, social, or psychological, spreads across a larger collection. Second, beyond 10 individuals, diminishing marginal returns make it increasingly difficult to ensure that each member is producing the maximum effort on behalf of the group. A large formal group then creates informal smaller groups that ensure their own expectations of cooperative effort and protect their own group from outsiders. In the mature enterprise, there typically exists a mixture of formal hierarchy and informal group dynamics. The formal hierarchy develops each time a costly situation makes the benefit of observers that ensure the productivity of subordinates outweigh the cost of trusting individuals to optimize their own productivity. The informal groups that form as decision-making units distinct from the formal hierarchy do so to participate in the spread of beneficial knowledge that the formal hierarchy cannot provide alone.

To answer the question, “Why do organizations form?” we should rely on an economic definition of value creation as the combination of inputs with knowledge. Value increases through many mechanisms, but knowledge is what makes value increase exponentially for a linear increase of inputs. Moreover, this value is subjective but aggregate. The “owners” of an organization do not own much at all, if ownership is the freedom to dispose of inputs according to any desire. For instance, the owner of an airplane is not free to land on an interstate highway, and the owner of a lake is not free to restrict air traffic or the orbit of satellites overhead. Property is not only material, but also intangible. Property ownership is not freedom of disposal, it is the legal privilege to constrain the use of a mutually identifiable resource. Those who form an economic organization do not create property that they may own it and dispose of freely. Instead, they cooperate to constrain and guide the use of resources to maximize value through the addition of knowledge. All value creation is part of a knowledge process. The “owners” of an organization, whether a sole proprietor, partnership, or the shareholders of a publicly-traded corporation, are the residual claimants to any value leftover.

We will adopt the terminology of the “residual claimant” to maintain strict honesty that the organization is not profit seeking nor the owner of property. Each socioeconomic organization is collection of individuals with knowledge, engaging in cooperative self-interest, making decisions that maximize the incremental subjective value of outputs. The residual claimants receive both the profit and the loss of such value-add activities. The residual claimants invest in a production process, but their residual claim at any time boundary exists as a positive or negative return.

Transformation has a clear economic definition with these concepts as a foundation. Transformation is a paradigmatic shift in decision-making processes needed once an organization can no longer attain the knowledge required to maximize value creation. The resistance to such transformation comes from many sources. The benefits of the new paradigm are often unknown while the cost to the individuals that comprise the organization are often high. The changes necessary for one set of decision-making units may undermine the performance of other decision-making units. The benefits of the new paradigm may benefit newcomers, while incumbents rely on the formal and informal networks to resist this challenge to the processes that benefit them.

A System

A system – as we will define it – consumes resources and energy to produce something that is more than the sum of its parts. Not only does is produce value it does so in a way that sustains its own existence. If we consider Henry Ford’s early Model-T production system that assembled automobiles, the raw materials – rubber, coal, plastic, steel – were meaningless as an unformed heap. Along the way, the “intrinsic” economic value of the raw materials were destroyed and could no longer be sold for their original price as raw materials. At the time, there would have been no resale value for many of the assembly pieces, because Ford created an entirely new value network and disruptive business model to create a market that could properly assess the value of the non-luxury automobile. Yet, once assembled, the assembly line put these pieces together to create value greater than the sum of its parts.

An example of a relatively simple organic system is a single-celled organism like some species of Plankton our oceans. A plankton lacks sophisticated embryogenesis, there is no differentiation of multiple tissue types, no embedded systems, and no coordination mechanism across cells. Nevertheless, the simple biochemical processes and the internal workings that complete these processes have continued for billions of years by not only producing its own self-maintenance, but also by managing to reproduce. There is a surprising large amount of DNA for such a simple, small, organism – but why did this legacy of code begin amassing in the first place? Whether we venture to call it “divine” or not, there was certainly a spark of some kind that began an explosion that has yet to collapse back into chaos and the dark.

Even with these simple systems, where we can trace each exchange in the value-transformation process, including materials, structures, energy, and ecological context, the sum total of the Model T and the factory that produced it is more than its parts heaped separately in a pile. Our difficulty in understanding such systems is a problem of multi-fractal scaling. For now, let it suffice to say that making a variable in a system better may not result in a linear change in outcome.

A Complex System

We have major issues understanding how (or worse yet, why) a system consumes resources and energy to produce value in excess to the sum total of the elements and energy amassed in the absence of the system that produced it. This problem is only compounded when we begin embedding specialized sub-systems within an organism. In the example of an automobile factory, we could say that every cell of every person is a system, that each person is a system, and that each distinct functional area, separated by distance, is a system. The accounting and finance “system” and the inventory and assembly “system” must interplay as part of Ford Motors, a system in its own right.

So we can define a complex system as having embedded sub-systems, causing the observer to not only see that the whole is greater than the sum of its parts, but the observer may also slip into a “confusion of levels” if they attempt to manipulate a part of a system to shift the outcome of the whole. Worse yet, confusion of levels can have disastrous, non-linear results that are the opposite of the intended change due to confusion of cause and effect. When sub-systems are embedded within each other, their interrelationships may act on differing scales, either in time or place. So we must careful when attempting to improve a complex system. We must use empirical process control to chart the change in systems outcomes rather than simply optimizing subsystems in isolation.

Multi-Fractal Scaling

A fractal is a pattern that repeats self-similarly as it scales. One of the most common fractal scaling patterns in nature is branching. From the trunk of a tree, to major its major limbs, to twigs, and finally leaf structures, this fractal scaling pattern enables a lifetime of growth cycles. Leaves can bud purely based on opportunism, in a relatively disposable manner. This is because the tree, as a seed, has all the legacy of generations of trees locked inside it. The tree does not aspire to be “the perfect tree” or assume that it will grow in perfect sunlight, humidity, soil pH, and water availability. The tree does not get angry when a major branch is broken off in a storm or struck by lightning. Instead, its fractal scaling pattern is prepared for intense competition for sunlight in the sky and resources from the ground. The tree’s scaling pattern has risk mitigation “built in” because it grows the same in the middle of a field with frequent rain as it does in a dense forest.

We see this branching strategy throughout nature, from ferns to human blood vessels. However, an even more effective approach to self-similarity comes from multi-fractal scaling. The ability to adaptively select between more than one repeating pattern or differentiated patterns based on scale requires a different kind of fractal: time-cycle. It is not just the branches of a tree that result in an environment-agnostic strategy for growth, it is the adaptation to cyclical daily growth, scaled to cyclical annual growth, than scaled to multiple generations of trees that grow. This final step is an important one. Multi-fractal scaling is not only the source of novelty and adaptiveness “built in” for a single tree, it repeats at an even larger scale as a species competes for dominance of a forest. Multi-fractal scaling encourages “just enough” opportunism to enable small-scale experiments that can be forgotten without loss at a greater scale, or thrive when conditions change.

Adaptive Multi-Fractal Scaling

The strength of multi-fractal scaling, from branch to tree to forest, is its total reliance on empirical process control. The legacy code is a confusing jumble of competing messages that a human mind, attempt to “engineer a perfect tree” would attempt to simplify and beautify. That legacy code, however, wasn’t written with any intention of crafting a perfect tree. That code was written to create a minimally viable reproductive system. It is built for one thing: continuous experimentation.

Continuous experimentation happens at each level of multi-fractal scaling, risking economics appropriate to its scale to find asymmetric payoffs. An Oak tree risks very little per leaf that grows over the entire course of its life. In a dense forest, however, that continuous experimentation of growing leaves higher and more broadly opportunistically based on local returns on investment can suddenly break through the forest canopy or unexpected fill the hole left by another tree’s broken limb. An Oak tree does not require centralized control of where leaves will grow or which limbs to invest in. Instead, the legacy of continuous experimentation enables multi-fractal scaling that competes locally and opportunistically.

Again, we do not need to understand what spark set this fire ablaze, we only need to see that it is still spreading and we are a part of it. Over-simplification of superficial outcomes will lead to poor decisions about inputs. Organic leadership relies on context, structure, and enablement of continuous experimentation. Organic leadership is a “pull” system that relies on scaling patterns for decentralized empirical process control. Artificial “push” systems force requirements and attempt to bandage the inevitable inefficiencies of a non-adaptive system.

A Complex Adaptive System

A complex adaptive system does not merely take in resources and energy to produce itself and reproduce itself as a unified “whole” that is greater than the sum of its parts. It does not merely embed subsystems with multi-fractal scaling and decentralized control. A complex adaptive system also operates with a continuous experimentation system built in to its normal framework of activities. When we make the leap from an Oak tree to the human body (or any other mammal on Earth), we can truly appreciate just how complicated it is to improve the health of an individual, or an entire population, when we observe the interrelationships of various physiological and socioeconomic systems and sub-systems. Creating lasting change is not only complicated in terms of finding the correct level and understanding the full ramifications across the entire system, each complex adaptive system is also continuously experimenting and will adjust against such changes based on short-run, local, decentralized opportunism.

To care for a complex adaptive system requires not only an understanding of inputs, processes, and outputs, but also the multi-fractal scaling of continuous experimentation that maintains long-run viability. When short-run economics are working against long-run viability, it is not sufficient to reward “correct” behavior to counteract short-run opportunism. Instead, we must shift the context of local decisions so that short-run opportunism serves long-run viability.

Accidents Will Happen

Accidents may seem to the observer to be unintentional, but continuous experimentation is built to test the boundaries of success, to ensure that precise empirical process data is also accurate for the needs of viability. In other words, if you’ve ever accidentally tripped and fallen, or accidentally loosened your grip on an egg and dropped it on the kitchen floor, this was a natural element of complex adaptive systems quietly running experiments.

Embedded in our own human code, our sub-systems are all built for continuous experimentation as a method of calibrating precision to accuracy, using multi-fractal scaling on short, long-short, long, and distributed cycles. A short cycle is an immediate reference point for an event, using data held in working memory, and is reactive to immediate changes. A long-short cycle compares current data to immediately recognizable patterns of events, more embedded memory or conditioned responses that have proven useful over time even if we assume the event is an occasional outlier. More significant, painful events can skew our “normal” for decades and even become passed to the next generation as part of our genetic code. A long cycle has been stored to our genetic hard drive for future generations. A distributed cycle is a socioeconomic artifact that requires a medium of exchange and may last for centuries.

As humans, our multi-fractal scaling of continuous experimentation results in the creation of complex adaptive socioeconomic systems. Our legacy code drives us toward exchange, tooling, building, and reproduction because the experiments that are in motion are far from complete.

Like our occasional fumbles and falls, our social systems produce results that appear to be accidents with no guilty party, pure coincidences of circumstance, which occur due to failed experiments. Organic leadership harnesses this natural propensity for decentralized opportunistic experimentation by encouraging it but setting boundaries for it, feeding it but ensuring checks-and-balances from opposing interpretations, and guiding it by changing context and opportunity rather than directly managing outcomes.