Category: Agile

Incentive to Self-Organize and Scale

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To avoid the complexity of a socio-technical transformation in a mature publicly traded corporation, we may build by analogy in simpler terms. Suppose two lumberjacks have the property rights for adjacent properties. They have the option of working together of separately. Even if they are identical in strength, skill, resources, and tools, the processes available grow exponentially if they cooperate. The process option of each individual worker may continue, but entire sets of new options that only cooperation may accomplish become available. If we add two more lumberjacks, there is four times the land, four times the potential output of four individuals, plus the additional options that only groups of two, three, or all four may pursue cooperatively.

It is easy to assume, up to our rough limit of 10 members, that the lumberjacks gain from cooperation additional options as a decision-making unit, using each of their property and abilities in ways that acting individually would lack. However, the larger that group becomes, the more effort they require when attaining consensus on which of the processes to pursue. The group of 10 may elect a leader or vote democratically, but two primary feedback mechanisms will arise. Productivity when the workers act out of sight of the others will become judged on output. Productivity when all work as a group will become judged on direct observation. Once this company of lumberjacks grows beyond 10, there are obvious diminishing returns for direct observation, even an impossibility of observation. Once we collect a group of 40, 70, or 200 lumberjacks, managers who coordinate decisions, observe their team for performance, judged exclusively on productivity become an inevitable recourse. 200 lumberjacks simply cannot cooperate effectively in a single forest through reliance on direct informal interaction.

Likewise, even without introducing the complexity of a legal, accounting, tax, or government system, and even before we consider actual market demand or the possibility of competitors, divisions in the organization emerge to benefit every worker. Specialized knowledge on planting new trees, care for trees over multiple years, coordinating which areas to work, care for the tools, and the preparation and shipping of the logs are not only distinct processes from the original effort of the individual lumberjack, but are also increasingly important to the optimization of long-run residual claims. Therefore, even if we assume it possible for all workers to equally “own” the organization, meaning that all have an equivalent residual claim, knowledge and specialization will still drive the introduction of management and coordination based on the output performance of distributed decision-making units.

If we now add a single competitor to this logging industry, a very simple “game” becomes available. Suppose one the residual claimants of one organization decides to hire workers based on wages and the other remains an equal partnership with no wage employees. The incentive structure of the residual claimants and the wage employees are different, creating fractal changes at scale. While wage employees gain remuneration as they work, they do not bear the risk that that the residual claim is zero. While wage employees may need to know some skills for the job, they will not need to know all the processes of the organization. Also, while the residual claimants may only exit the partnership with some difficulty, the wage employee could exit any time to pursue a better opportunity. Therefore, while residual claimants have incentive to ensure the long-run sustainability of the processes, the wage employees have incentive to maximize the short-run behaviors prescribed by the wages.

Comparing these two competitors, we begin to see advantages to the use of wage workers who do not possess a residual claim. First, because they do not possess a long-run interest in the organization, wage employees may carry out the calculated risks of the managers without fear and hesitation that the collectively-owned organization would. If one of these risks produces a windfall gain, the organization gains competitive advantage. Second, because their focus is on short-run optimization of the behavior pattern demanded by the incentive structure, wage employees can perform tactical activities that require less knowledge of the organization as a long-run system. Wage workers make the workforce more malleable and responsive to incentive structures in aggregate. Third, because the optimal number of workers for any one specialty may change over time, the competitor with a variable pool of wage employees can respond more quickly and with less risk than an organization that is taking on a partner with an equal residual claim to assets they did not originally participate in earning. Fourth, the ability to grow the workforce with wage employees in a boom cycle without increasing the total residual claimants allows the organization to respond to the incentives of fleeting opportunities with a limited subset of the long-run disincentives.

Our first conclusion should be that some introduction of wage employees in each organization is inevitable. If we compare an organization with 10 residual claimants and a variable wage workforce of 5 employees, compared to an organization with any number of residual claimants fixed at a number between 10 and 15, the use of wage employees creates more options and some potential for competitive advantage. However, if we add more organizations, we can also see that the effectiveness in optimizing decision-making becomes as much based on knowledge of coordination and incentive structure as it is of the given hierarchy or individual value production. Where a production process was highly stable, predictable, and productivity easily measured on output, a larger number of residual claimants could cooperate as partners. Where the process is highly variable, requiring little knowledge, and productivity is the result of effect management rather than worker virtuosity, minimizing residual claimants while relying on wage earners will be more effective.

There is significant incentive to assure that an organization builds itself not through the exclusive dichotomy of long-run residual claimants that bear all the risk and short-run wage workers without systemic constraints or incentives. The pressure for stability from wage employees and the necessity of management incentives that more closely align to the optimization of long-run residual claims combine to create a gradation of fixed-claim wage earners with specific performance constraints. The salaried employee and the manager compensated partly in company stock can optimize the preservation of an externally directed mid-run. Neither feel the freedom to leave the company due risk and sunk cost. Neither feel the full freedom to exploit new opportunities held by a partner with equal residual claims.

The clear trade-off in a corporation, reliant on salaried employees with slow aggregation of a small percentage of residual claims, is the increasing and widespread hesitation to act, combined with diffusion of responsibility for emergent decisions. When growth remains strong and fitness of solution to market context remains stable, bureaucratic rationalization continues to preserve the organization rather than the optimization of the residual claim. Once growth stagnates, it becomes clear the organization became fine-tuned to internal signals of political disputes while placing layers of noise between decision-making units and the external signals of the market. Transformation is a paradigmatic shift from a structure no longer adapting its knowledge production to its changing market context, to a new paradigm in its place. The challenge of transformation, primarily, is the development of new knowledge networks that can create sufficient benefit to entice the bureaucrat to make the shift to the new paradigm of systemic incentives and constraints

The nature of the production process and the structure of the industry shape the way market forces reward variations in what is otherwise an identical number of inputs. A tax accounting firm or a law firm may rely primarily on equal partnership or tiered partnership with few wage employees relative to the residual claimants. In an oil or mining endeavor with major risk that only requires capital to pull wage employees and the tools of production from other opportunities, such as applying recent technology to previously unexplored mineral rights, the fewest number of residual claimants necessary to raise the necessary capital optimizes the use of a much larger organization of wage employees, vendors, and contractor firms. Many publicly traded corporations are some mix of options, allowing different residual claims in the form of preferred stock, common stock, bonds, pensions, etc. The selection of incentive structures and systemic constraints provide the administrative context for an organization. Decisions regarding the internal context and the selection of an external context with which it must integrate is the realm of competitive strategy.

Economic Knowledge Organization

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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.

The Thought Police

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His eyes re-focused on the page. He discovered that while he sat helplessly musing he had also been writing, as though by automatic action. And it was no longer the same cramped, awkward handwriting as before. His pen had slid voluptuously over the smooth paper, printing in large neat capitals –

DOWN WITH BIG BROTHER

DOWN WITH BIG BROTHER

DOWN WITH BIG BROTHER

DOWN WITH BIG BROTHER

DOWN WITH BIG BROTHER

over and over again, filling half a page.

He could not help feeling a twinge of panic. It was absurd, since the writing of those particular words was not more dangerous than the initial act of opening the diary, but for a moment he was tempted to tear out the spoiled pages and abandon the enterprise altogether.

He did not do so, however, because he knew that it was useless. Whether he wrote DOWN WITH BIG BROTHER, or whether he refrained from writing it, made no difference. Whether he went on with the diary, or whether he did not go on with it, made no difference. The Thought Police would get him just the same. He had committed — would still have committed, even if he had never set pen to paper — the essential crime that contained all others in itself. Thoughtcrime, they called it. Thoughtcrime was not a thing that could be concealed for ever. You might dodge successfully for a while, even for years, but sooner or later they were bound to get you.

It was always at night — the arrests invariably happened at night. The sudden jerk out of sleep, the rough hand shaking your shoulder, the lights glaring in your eyes, the ring of hard faces round the bed. In the vast majority of cases there was no trial, no report of the arrest. People simply disappeared, always during the night. Your name was removed from the registers, every record of everything you had ever done was wiped out, your one-time existence was denied and then forgotten. You were abolished, annihilated: vaporized was the usual word.

George Orwell’s 1984

What you need: Complexity Mindset

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Organizations that are too rigid cannot adapt to changing economic conditions, demand, prices, interests, shocks, and crises. Problematically, an enterprise can grow quite large while preserving its rigidity in the medium-run, delaying the need to adapt until the industry as a whole has a crisis (airlines, automobiles, etc).

Thus, my first book focused heavily on the need to build and cultivate tension within an organization to ensure that continuous experimentation preserves adaptive complexity.

One example that humanity has struggled to grasp, is the adaptive complexity of a system that relies on individuals who need to be a mix of selfishness and altruism. Standard economics is largely built on the axiom of rational self-interest, that individuals have static preferences and will optimize marginal returns on margin investment. For instance, when I have $10 to spend, I will maximize the utility or value of my spending, based on information, rational self-interest, and prices.

Behavioral Economics, however, has shown us that the choices we make are often quasi-rational at best. Any parent who has gone grocery shopping with a young child knows that gut-feel, intuition, snap decisions, and a desire to get the complex decisions of a stressful shopping trip over with, leads to less-than-perfect spending decisions. Anecdotes aside, research has proven a growing list of fallacies and biases that are consistent across gender, race, culture, and intelligence–from anchoring our conclusions based on whatever information we receive first, to an optimism bias that our success is more likely than the average success rate.

As Hoff and Stiglitz review, there is strong evidence for treating individual actors within a system as encultured actors, partly depending on social context and expectations to determine the best decision.

We can each fit the standard model of classic economics during “slow-thinking”. Under observation, we act rationally self-interested as long as we have perfect information and sufficient time for deliberation. The rest of the time, we are swept up in our action-packed schedules, engaged in thousands of quasi-rational decisions. We copy what has been successful for others, act agreeable when the impact of a decision is unclear, and rely on our past experiences to maintain the habits that have worked so far. This “fast-thinking” is not static, however. The research is very clear that we can be manipulated in very subtle ways, toward selfishness, mistrust, polarity, and dishonesty. Likewise, with enough changes to social context, education, and time, the weak can be strong, the forgotten can be outspoken, the rigid can grow again.

So, who would you like to be?

While I will present the science behind complexity thinking in lean-agile organization development in future posts, the real question that I am left with is, “Who would I like to be; who should I hope to become?” Naturally, there is no perfect answer to this. Personal identity is a question of strategy, in its own way; you can only choose so many vocations, specialties, social contexts, and roles. To be enormously successful in one arena is a trade-off against other opportunities.

One thing, however, is entirely clear. To the extent that our quasi-rational behavior allows us to rely on several “identities” based on mental schema, role models, behavioral narrative, and social norms, isolation within any one institution and ideology is a dangerous prison. We are only free to determine our own path to the extent we know those paths exist. We can only adopt the best mental schema for an unknown decision by having as many modes of thinking as possible at our disposal. We can only carve out the best self-identity as the exposure to new options, cultures, and role models permit.

Frankly, if we are all very honest, we find it easiest – because is simple, familiar, and less scary – to remain stuck in the simplistic modes of thinking we developed as children. Good-baby/Bad-baby, Good-mommy/Bad-mommy, Good-worker/Bad-worker… and yet, when we view the world as a complex adaptive system, it is true of humanity that the health of the forest is so much more complex than can be observed tree by tree. Sometimes we need mother-soldiers, brother-florists, teacher-friends and so on. So my call to action is not to choose a single destiny and blindly pursue it; my imperative to you is see all the paths, invent a hundred identities, meet every kind of person, think through the lens of your worst enemies. Only by expanding your vocabulary, experience, and exposure to the full complexity of the world can you hope to say, in the end, “I chose who I have become.”

Cited:

Hoff K, Stiglitz J. “Striving for balance in economics: Towards a theory of the social determination of behavior” Journal of Economic Behavior & Organization, 2016, vol: 126 pp: 25-57

 

The “Priority” field in JIRA

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The Legacy We Build Upon

The topic of lean metrics requires an understanding of the influence of Kanban and the Toyota Production System. Scrum, as an agile process framework, was built as a lightweight version of the TPS Kanban practices that anyone could use, while Atlassian developed JIRA to enable visualization of any process, regardless of its complexity.

The “priority” field originated as part of Jira’s oldest legacy as an issue-tracking system. Greenhopper was a plug-in that enabled the issue-tracking database and web services to be used for Lean-Agile teams. Greenhopper created a new front end for the data in JIRA – namely, the work-in-progress board and the product backlog – so that a Scrum team or Kanban team could use JIRA for agile.  Ultimately, this became synonymous with JIRA and the today it ships with the agile front end OOTB.

In Scrum, the Product Backlog is used for prioritization, so the “priority” field has little meaning. In Kanban, the priority field is used to create swim-lanes based on Classes of Service.

Expedite Anything That Blocks Value Creation or Capture

“Blocker” would indicate that WIP limits can be violated and workers should interrupt their current work in favor of Expediting the blocker through the system so it doesn’t not cause long-running damage to continuous flow. The equivalent in Scrum is building out a process for stories, tasks, or bugs that are allowed to violate the Sprint commitment. In either case, the goal is to recognize that this is costly and unhealthy, an exception to normal rules. Unless it is a production defect, some element of the expedite cost should levied against the person demanding special treatment.

Flush the System of Any Critical Items that Disturb Continuous Flow

“Critical” would indicate that a card should “jump over” all other items at each step in the process, but it should not interrupt work or violate WIP limits. These items get to cut to the front of the line, but are not allowed to interrupt completion of existing efforts. The equivalent in Scrum is building out a process for what items, if any, are immediately prioritized to the top of the Product Backlog. It is typical for this to be a “Fixed Date” class of service – because fixed dates are the most consistent destroyer of sustainable flow, we want to get those things out of the way quickly so that the system can return to normal.

Everything Else Follows the “Normal” Process

“Major” and “Minor” and “Trivial” are typically part of the same Standard (aka normal) Class of Service. If used in Scrum, it is primarily for the benefit of the Product Owner to visualize previous conclusions about prioritization. In Kanban, these are meant to respect all WIP limits and follow a First-In-First-Out (FIFO) method at each step in the process.

Scaling Like Organic Systems

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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.

Have you failed at dual-track Scrum?

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Dual-track Scrum is a red flag that no part of your organization is practicing lean agility in any way shape or form. It preserves the transactional, finite, short-sighted project mindset.

Cadence improves internal signalling, but layering staggered cadences means you missed the underlying economic factors that make Scrum so effective. 

To be transformational – to dramatically shift your business model, disrupt your industry, or move to long-run economic optimization – requires an understanding of multi-fractal scaling and how time, distances, investment, and exchange differs based on their scale. 

For an in-depth look at time-cycle scaling in a typical digital value stream, check out my playlist on YouTube:

Time Cycle Scaling Economics

Orienting is Essential to Agility

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Responsiveness and disruptive influence are the cornerstone of agility, because change through continuous experimentation is fundamental to life. Healthy and viable systems maintain their complexity far from equilibrium, relentlessly fighting collapse and death. After all, “poised” on the brink of chaos, there is an obvious business definition for agility:

Responsiveness to signals in a market with imperfect information and imperfect competition.

This context necessitates process control that keeps identity and novelty in constant tension – even against our most brilliant ideas. Thus, our tactical principles for general preparedness, quick orientation, and powerful responsiveness will be rooted in the need to orient faster than the enemy system, our ideological competition. Only the working product of our efforts can provide a pragmatic judgment of the value we have created, so the ultimate measure of our success as a Disruptive Influence is the actual change in behavior we have caused.

Because individuals and interactions are inherently complex, adaptive, and difficult to predict in the reality of socioeconomic competition, we value knowing them directly, studying them and interpreting their position ourselves. We value this over relying on their predictability, likelihood of adherence to an agreed-upon process, or correct use of the best possible tool for any given job. Although we assume processes and tools taken at face value will deceive us into a false sense of stability, we also recognize that individuals and interactions cannot always be taken at face value either.

Because responsiveness, both in decisiveness of action in an unexpected situation and as adaptation over a long-term investment horizon, will consistently be awarded with asymmetric payoffs, we can only trust a plan to the extent it includes contingencies, delays commitment, and distributes control to the individual with the best understanding of the situation at the time a decision must be made.

Because compromise is the inevitable and unsavory outcome of “contract” negotiation, while creative endeavors in contradistinction rely on the energy of tension, cognitive dissonance, intra-organizational paradoxes, and conflicting interpretations, we invest our time and effort in social exchanges while delaying formalization. A contract relies on an external locus of control for its power and validity, whereas we must prioritize a social and socioeconomic view of the complex system we hope to lead into adaptation.

Because a socioeconomic “factor of production” is defined by its output, evaluated on how much more value “the whole” can add in excess of its “parts”, and because digital products are continuously created and maintained but never mass-produced, we take the tangible product of our endeavors as the only valid measure of its worth.  However good the product design looks on paper, however well-defined the future state is documented, only exchange in the marketplace can determine the economic value of the product we have actually created

Drawing the Line Between PO and BA

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The Scrum Business Analyst

I have heard more than once “There is no BA in Scrum.” Imagine how your BA’s feel when a transformation starts!  At best, they are uncertain what their role ought to be. At worst, it is made clear by everyone else in the process that the BA is no longer needed or wanted.

The irony, for an agile coach viewing this as an outsider, is that numerous individuals throughout the value stream who are also struggling to cope with the shifting sands of transformation, frequently report that mistakes, lack of prioritization, failure to clear dependencies, and miscommunication are due to “being too busy.”

Obviously, just from this “too busy” problem, there are two important things the BA ought to do as an active member of a Scrum Team in a scaled environment:

  1. Act in a WIP-clearing capacity to the extent their t-shaped skills allow.  To whatever extent they do not have T-shaped skills, the moment they are not clear on how to utilize their time is the perfect opportunity to develop these skills.
  2.  Capture the very broad “reminders of a conversation” about a story that, in a large enterprise, occur across a larger number of individuals, over a longer time period, and in more geographically distributed locations than “core scrum” implies.

Roles and Accountability

Now we can draw the line between the Product Owner and the Business Analyst.

The Product Owner is accountable for decomposing an Epic or expressing a single enhancement as User Stories.  The Product Owner creates a Story card in JIRA for this initial Story list that includes a JIRA Summary and the User Story in classic format:

As a {user persona} I want {action} so that {expected value to the user}.

This is an expression of “Commanders Intent” and represents why the story is being developed and who cares whether or not it is developed.  Thus, the User Story is an expression of product strategy, and represents trade-off choices and prioritization.  The decision to expend finite on and expiring resources – time, energy, money, and talent – on one product change versus another is the most critical accountability of the Product Owner.

Although the what and how is negotiable, the intention of the Product Owner serves as a litmus test for all subsequent decisions.  The what and how are the realm of operational effectiveness rather than strategy.  It includes the framework of economic decision making and the processes, practices, and tools that streamline communication and align strategic direction of a distributed control system.

The Business Analyst uses the Description to succinctly express the what and how that has already been determined so that no context is lost in subsequent decisions.  The what and how remain negotiable to the extent these better serve the “Commanders Intent” of the User Story.

In an analog Scrum board, there is typically an agreement on “front of the card” and “back of the card” content that serves as the “reminder of a conversation” for the team.  In a scaled environment relying on a digital board like JIRA, the Summary and Description fields serve a similar purpose.  As the number of individuals contributing to the value stream increase, the need to detail the conversations that have already occurred increases as well.

In the process of detailing each Story Description, it will often be apparent – due to test data or testing scenario coverage – that a Story ought to be split into two or more stories.  The Business Analyst completes this activity and is accountable for communicating the split to the Product Owner.

 Stories may also be further split during Backlog Refinement or Sprint Planning based on additional insights from the team. Attendees should collaboratively decide who will capture this decomposition within the tool, but the Product Owner is accountable for prioritization decisions (if the split impacts this).  

Purpose of the Story Description

So, to meaningfully define the role of the Business Analyst, we need an understanding of what value is created if one individual “owns” capturing the elements of a Story Description as the number of these predetermined elements continue to grow. To the extent at scale that the team is unable to economically interact with every other value add activity in the value stream, the purpose of the Description is a succinct expression all value-add activities and decisions that have influenced the User Story prior to development. While we want to express these in the fewest words possible, and work toward distributed control of decisions, we do not want previous insights “hidden” unnecessarily from the Scrum Team.

Several important activities have likely occurred prior to our Sprint:

  1. Business decisions fundamental to the economics of our interaction with the customer.
  2. Funding based on an overarching strategic initiative.
  3. Customer research and analysis of product metrics.
  4. User Persona definition and Empathy Mapping.
  5. UX Proofs of Concept and/or A/B Testing.
  6. Stakeholder meetings.
  7. Success Metrics defined.
  8. Technical dependencies fulfilled (such as a new or updated web service API).
  9. User Story decomposition.
  10. Other Stories already developed related to the feature.

Thus, many details needed “downstream” should be easily expressed in advance of the Sprint:

  1. Why are we building this story?
  2. Who is the User?
  3. How is this User unique in our Product (i.e. relate persona to an account type)?
  4. What Test Data will need to be requested to test the story?
  5. What steps does the User follow to obtain the value of the story?
  6. What will the User see when they finish the story?