The Red Pill of Management Science

Further Into the Matrix

Management Science hasn’t changed much in the mainstream for decades, and people have become exceptionally skilled at navigating a system and command structure that is not always fit for purpose, but has come to be used to try to resolve everything.

I felt it of value to take a further look into some thoughts on systems, organisations, company culture, and decisions via knowledge management matrix.

Traditional and “modern” management science methods are mostly based off Mintzberg’s 10 Strategy Schools, with an expected hybrid outcome of consistent, transferable, repeatable and rigidly controlled performance with an alignment to mission statements and values which are predictable and usually single perfect goals. When this is applied out of context, problems can result.


The Cycle of Woe

Many organisations large and small are trapped in a loop of trying to remediate fallout from this approach to everything, whilst continuing to apply it. This produces a cycle which typically lasts 6-12 months, although it can be longer or shorter, and roughly follows this order:

This is not only woeful for the company, but the individuals creating the value streams for the company, and links into crisis management, weak signal detection, S-Curves and Complex Adaptive Systems as well as a whole raft of other subjects.


Understanding what lies behind the Cycle

An organisation, and the people that make it up, are complex, as are many situations. Complexity is by nature unordered and therefore not linearly causal (unlike complication or obviousness, where if I do x, I will always get y). It has dispositional states, where you can estimate, even simulate what is likely or unlikely to happen, but you cannot predict with certainty – and that’s an important distinction: prediction is not the same as simulation.

In Fearing Change & Changing Fear, I talked about the matrix below – a core precept of Cynefin, created by Dave Snowden of Cognitive Edge:










Cynefin Knowledge Management Matrix (Cognitive Edge)


…where Order and Unorder are ontologies (definition of causality) and Rules and Heuristics are epistemologies (knowledge in terms of action).

This time, I’ve added colour to show the relationships between the elements:

Systems dynamics (Systems Thinking) and computational complexity (Mathematical Complexity) take a MODELLING approach which, in most of the popular forms of Systems Thinking, essentially removes human judgement through models and predictive process.

Scientific management (Process Engineering) and anthro-complexity (Social Complexity) take a FRAMEWORK approach, which look at things from different perspectives, and also respect human judgement.

It is important to note that I am by no means saying that Process Engineering and Systems Thinking have no place – Contextual Complexity is the idea that humans can operate in and move between all 4 quadrants of this model, either accidentally or deliberately. In some cases Process Engineering and Systems Thinking are the applicable approach, but when we move outside those quadrants and don’t realise it, their application actually damages success.

Instead, this is about understanding when they have their place, where you currently are in Cynefin’s domain model, and acting appropriately to the context you find yourself in. If you are amidst uncertainty and you cannot resolve conflicting issues within a feasible timeframe based on the evidence… you are probably in the complex unordered domain, and it’s understanding when you are and how to act that is crucial.

This is where things can become a serious problem and catalyse the Cycle above, because the two Ordered quadrants are prone to simplified “recipe” thinking, prediction based on perfect outcomes, and the unthinking application of order in unorder.


The worries of modern Management

Many organisations are now in a market/landscape they have no prior experience of or reference for, and this causes fear and concern because we are being forced to change at both a personal and industrial level. They push back against this by acting as they always have using the cycle of woe, but the simple procedures that once worked do not produce new benefits past the very short-term now.

Does any of this come to mind with current or past companies you are aware of?

One of the key reasons for these responses may be because of the still-existing and long-term investment in structures based in Taylorism (which dates back to the 19th century, yet is still a core of today’s management science), a root of Process Engineering. This can be interpreted as the belief and (and action upon the belief) that an organisation is a machine with people as cogs or components that will consistently deliver the exact same output in quality and quantity – or, that an organisation is both inherently ordered and conforms exactly to rules.

Despite the realisation for decades that Taylorism is actually detrimental, because that just isn’t how people work, and supposedly eschewing it in favour of a more Systems Thinking approach and a shift from a perception of “machine” to “human” (Peters, Senge, Nonaka), businesses have not changed it fully.

There has been an effort to balance the Mintzberg et al Process Engineering-centric Schools of Strategy:


Designing Planning Positioning


and the Systems Thinking-centric Schools:


Entrepreneurial Cognitive Learning
Cultural Environmental Configuration


but this is still an attempt to balance mechanical efficiency with modelled semi-utopia, and the value of people – and thus the organisation’s own value-streams – can tend to get lost along the way. In my own experience of companies I have found a leaning to the Process Engineering side with some nods towards System Thinking, in many cases taking the worst of each to form an organisation in the likeness of a machine with an optimum goal, fresh, dynamic values that aren’t as humanly achievable as they sound, pride in a pseudo-innovative approach, and an inability to sense or react correctly to situations no longer being as desired.

Organisations often use the modified concepts of Taylorism because it is trusted and traditional, despite being proven ineffective for decades, and act as if it will forever output the exact same quality and quantity towards an outcome they are certain they can reach. When forced to drastically change, there is a tendency to jump onto a new orthodoxy or bandwagon of the latest management fad that “worked wonders for x company”. Unless scientifically investigated or proven in context, be wary of “hacks” and “secret methods” – especially if novel, yet already in a new best-selling book!

This is representative of something called The Hawthorne Effect (Snowden), which you can read more about in my post The Secret Shortcuts to Innovation, and is a good example of the trend of applying novel, popular, simplified fads to innovate and fix that are not actually applicable to your organisation, dropping you back into the Cycle of Woe, when your value usually already lies within; it just needs context and sense to emerge.

So… how to get the best value output? This will depend entirely upon your organisation’s unique situation and context.


You only get out what you put in… right?

Not necessarily. Whilst you see this quote around a lot, and in certain circumstances it’s true, it isn’t an immutable law, certainly not in business:


Complex Output



Simple Output



Simple Input

Complex Input

Cynefin Knowledge Management Output Matrix (Cognitive Edge)


Process Engineering is best considered as something automatable, rigid, controlled, with people as components in the process; a machine. This is a simple input/simple output scenario.

Systems Thinking is best considered as the determination of a desired (often semi-utopian) outcome, with a system set up around achieving that goal that is controlled, predicted and measured; an organism, if you like, or often more accurately the desirable model of an organism. This is a complex input/simple output desirability.

Mathematical Complexity is best considered as simple rules being modelled to demonstrate complex behavioural patterns from agents within a system; an algorithmic or simulation approach (remember, simulation ≠ prediction. The former is designed to see what could happen, the latter tries to guess what will happen). This is a simple input/complex output model.

Anthro- or Social Complexity is best considered as trying to understand the dispositional state of the present (or what is likely to happen), then trying to guide the future state by modulation instead of driving (guiding emergence instead of forcing desire) and using vector measurement (feedback defining the parameters of the journey forward) to monitor for new, better opportunities, and basing all of this on all agents within the system; an ecology approach, flexible, innovative and reactive. This is a complex input/complex output emergence.

The required output of organisational value has drastically changed. Once, a local artisan may have arisen to make shoes as a basic human requirement. It required simple or obvious components, basic materials and practices going back perhaps thousands of years, and a complicated element in the form of an expert craftsman (certainly once competitors arrived). Eventually this would grow, as people need new footwear, and become a company, or trade. People had to take a set number of steps in a certain order to reproduce the quality of shoe preferred; gradually, they then expanded the quantity produced in line with growing demand.

Once sufficient complexity and saturation of market/product/company is reached, there is no longer any guarantee of staying within the realms of order and cause and effect, or balancing both quality and quantity using the old methods; you also can’t effectively innovate by modelling, or total controlled rigidity.

Today, companies have grown, globalised, diversified, propagated and moved far into abstract realms providing services as a priority, and the once-simple production of shoes by specialists is a product mass-produced cheaply, efficiently, in multiple materials and at a cost of ethics and craftsmanship; the commodification of the process itself, rather than the product, and a mantra of being innovative. Almost all business today is exponentially more complex, in a likewise exponentially more complex world where knowledge and services have become a primary global economy, 24/7. Companies are finding that you cannot operate as you once could, bureaucratically and hierarchically, because everything has changed, and they need to catch up – fast.

Entrepreneurial SMEs and the EMEA market approach are good at dealing with this. More traditional company structures aren’t, and that’s a problem for huge corporations as well as everyone else.


Adding to Value Production

It may also help to understand something further – in simplest terms, each of these is an attempt to augment how we approach the production of value:


Complex Output

Cognitive Replacement

Cognitive Augmentation

Simple Output

Physical Augmentation

Cognitive Replacement

Simple Input

Complex Input

Cynefin Knowledge Management Augmentation Matrix (Cognitive Edge)


As you can see, the two labelled “Cognitive Replacement” are attempts to model ideals or outcomes and replace both productivity and distributed, real-time cognition with their practices or results (almost to force utopia), whereas Process Engineering produces value logically and restrictively (but is prone to bottlenecks) by adding or removing people, components or processes, and Anthro Complexity treats this as a parallelisation of human processing power to more effectively discover the best path in uncertainty and maintain constant feedback to do so. They all have their place dependent on situational context.


Collaboration and Culture

Of the four quadrants, collaboration and innovation are most likely to happen in Social Complexity. It’s real-world applicable, reactive and monitored, and the output emerges in a vector-based fashion; in other words, it doesn’t try to define an outcome, unlike Process Engineering and Systems Thinking. A Vector measures intensity and speed of travel from a point (usually the present), and allows you to modulate (guide with feedback) the progress until a viable path emerges. It also takes into account something critical to success, and that is company culture and sub-cultures.

Culture is created by and for the people within the system, but also by the actions and inactions of leadership. It can be beneficial, the glue that welds the company into a cohesive value delivery platform, or it can be incredibly toxic, losing vital agents, morale, collaboration, producing gaming behaviour, cynicism, policies that impede roles, nonsense politics, focus only on immediate reward structures – in short, losing the ability to be effective anything more than short-term. When we talk about real collaboration that is self-creating and sustaining, it is found here. Understand that over-competitiveness and overconstraint via rules/policy/demand of output can be contradictory to success (inducing cynicism and gaming behaviour merely to do the job), and you understand why a holistic ecology needs good culture to operate.

Attempting a culture via Process Engineering, which relies heavily on human involvement, can fail because those humans are individuals and complex, not components in a machine. It is a framework approach, but a heavily constrained one which doesn’t allow for individuality and feedback, and although it does allow for human judgement it expects mechanical efficiency and does not allow for a lack of order within the system. It still expects rules to be adhered to, even if they impede progress and value production.

At the same time, Culture from Systems Thinking, whilst based on some good ideas, has a fundamental flaw of being a model – so whilst ostensibly Systems Thinking says “we allow this is a system of humans with individual traits”, and allows feedback, it removes human judgement in favour of prediction and order, and still expects firm adherence to that order whilst heavily measuring and metricising humans against a perfect vision.

This is a real problem with most popular Systems Thinking – it instils a habit of thinking where you want to be in an ideal world and then trying to close that gap, in other words using outcome based measures – which may have no actual basis in reality. Depending on these then for forecasting, culture, and organisational direction can be dangerous, as can attempting to then control and apply policy to humans, who live in a real complex world not an ideal world, and act accordingly. Systems Thinking does not always apply well to HR, for instance, because measuring complex agents on outcomes which may be unrealistic or require gaming of the system to reach, or demanding people map to a model when they are all individuals, is a decidedly failure-prone way to try to make sense of knowledge, achieve job satisfaction or good morale, or deliver value.

Where both of these approaches often fall down is that they still assume that circumstances and organisations are ordered, even if this is not the case. Forecasts, company message, and guaranteed output heavily rely on a firm goal that must be achieved whatever the cost, or after a tipping point reassessed; all of these induce an initial tunnel-vision that then cannot be seen outside of.

For me, one of the most unforgivable aspects of popular Systems Thinking is that positivity and adherence to the perfect desired outcome is far valued over realism and the achievable – think about how many times management is unhappy with a forecast because “it seems negative” – and the mavericks and heretics who suggest other approaches are often suppressed or punished.

I’d rather a realistic prediction than a comfortable one; and these are the people most likely to spark innovation in an organisation.


Why Social Complexity is so effective in uncertainty

Social Complexity takes a different approach, saying that this is an ecological framework, based on individuals working as collaborative agents within an unordered system, and that real-world feedback is critical to assess and modulate goals which may change significantly. The flexible vagary of human input (including outliers) can be harnessed positively instead of suppressed to produce productive, innovative, beneficial output, which may even improve from original preferences. It accepts that predictions cannot be accurate, and allows looser constraints to allow the system to achieve the contextual coherency required to achieve an appropriate goal, find new goals, or spark innovation. This is the closest we get to a cohesive ecosystem delivering the most effective output and value, self-monitoring and constantly feeding back and adjusting.

Contrary to popular management approaches, it says if you find yourself in an uncertain scenario, you must identify where you are NOW, and then see where you can make changes (via probes and contextual constraints), and then monitor vectors as close to real-time as possible as you go forward, allowing for all agents within the system. If you find a path of coherency where you dampen negatives and amplify positives, you may be able to then stabilise this emergent path until you have discovered or even created causalities, transitioning through a liminal domain into linear causality (Complication), whereupon you can breathe a little more easily and create governing constraints.

How you get here and where you get to may not be where popular Systems Thinking had you start out, trying to attain an idealistic outcome, because you have probed for new possibilities to close the gap between here and a REAL outcome – which could be even better than the original goal, and is likely to be more realistic.

In other words, the journey, which never ends and allows novelty, serendipity, and new paths to be discovered en route, is really more important than a goal where you are so set on the target that you don’t see alternatives – or the fact you might never actually be able to get there.


What does this mean for an Organisation?

Well, not that you have to drop everything and instantly decide to attack every situation as complex, remember. It’s more about understanding internally to the company the cultures, departments, and people making them up as reactive pieces of a holistic ecological whole, and learning how to divine what is a complex situation and how to make sense of it; Contextual Complexity and appropriate action. The simple fact is you probably aren’t experiencing issues if you are within the ordered quadrants you think you are. It’s when you think you’re still there and you’re not that problems rapidly arise.

As mentioned in a few posts on this blog, there are a number of reasons many organisations today (and many of those don’t understand they have issues yet) simply don’t seem to understand their markets, their employees, where they are going, or how or if they can get there.

There are several ways to approach these issues, and as companies become aware of them they get inevitably caught on buzzwords and “certified approaches”, but one of the best is simply engaging a multi-methodology consultant who – rather than come in as an expert in one specific popular approach to do disassociated work for the company then leave – advises, jiggles, and helps the organisation learn how to sense-make (instead of reflexively categorise) for themselves, then change from within using a mixture of appropriate methodologies and frameworks. This helps create a sustainable, learning organisation one step closer to a collaborative ecology, and lets them focus on the value they deliver instead of the internal struggles they faced.

It’s all about dowsing for context and coherency – decoding where you are in the matrix and acting accordingly – and that’s what we’re here for – nudging, education, and paradigm shifts.

…I don’t recommend the blue pill. It leads to a Cycle of Woe.

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