Executive Leadership Briefing. Is the data presented to you, enabling a real choice?
By: Tony Fish
Tags: Leadership, Data Science, Data, Decision Making, Directors

Decision: The action or process of deciding something or of resolving a question. A decision is an act of or need for making up one’s mind. Whereas Choice: Is an act of choosing between two or more possibilities. It requires a right, agency, or opportunity to choose.

The origins of the two words add context. The word decision comes from “cutting off” while choice comes from “to perceive.” Therefore a decision is more about process orientation, meaning we are going through analysis and steps to eliminate or cut off options. With choice, it is more of an approach, meaning there is a perception of what the outcome of a particular choice may be. Because of this, let’s run with choice rather than a decision.

A Decision is about going through analysis and steps to eliminate or cut off options. Choice is an approach, meaning there is a perception of what the outcome may be.

Does energy have a choice?

We are using energy as represented by a magnet field.

Two magnets, north and south. Irrespective of position, they have to attract. Do they have a choice?

Three magnets north, north, south. They have a more complicated relationship as of position and distance now matter as they influence the actual outcome. But there is no choice; the rules define the outcome.

At the majority of starting positions for the three magnets, there is only one outcome as such choice is predetermined. However, there are several situations when many magnets are sufficiently far apart and that there are only small forces at play (far-field). In this case, the result of movement may appear to be more random between possible outcomes. Any specific outcome is based on an unseen small momentary influence. The more magnets exerting small forces, the more random a positional change or choice may appear, as the level of complexity of the model increases beyond the rational.

Therefore, at a simple model of say three magnets, there is no choice. Whereas in a complicated model, with many magnets, it would appear that a degree of randomness or chaos is introduced (entropy). The simple model does not exist in nature as it is impossible to remove small signals even if they are hidden because of large-close forces. The point is that at this level of abstraction, energy itself does not have a choice, and the outcome is predictable, as there indeed a fixed number of possible outcomes, which can be modelled.

Stick with me here; we are exploring something that we don’t often want to face up to as leaders; we do not make decisions that we are accountable and responsible for, as there is no choice.

Expanding as there are only three fundamental forces of energy, each governed by their own rules.

Gravity. There is only one kind of charge: mass/energy, which is always attractive. There’s no upper limit to how much mass/energy you can have, as the worst you can do is create a black hole, which still fits into our theory of gravity. Every quantum of energy, whether it has a rest mass (like an electron) or not (like a photon), curves the fabric of space, causing the phenomenon we perceive as gravitation. If gravitation turns out to be quantum in nature, there’s only one quantum particle, the graviton, required to carry the gravitational force. Based on maths and models, gravity suggests there is no choice as it is always attractive. However, as we know from our study, of say, our galaxy the Milkyway, a single force introduces many patterns and an appearance of randomness. But with enough observations and data, it can be modelled, it is predictable.

Electromagnetism. A fundamental force that readily appears on macroscopic scales gives us a little more basic variety. Instead of one type of charge, there are two: positive and negative electric charges. Like charges repel; opposite charges attract. Although the physics underlying electromagnetism is very different in detail than the physics underlying gravitation, its structure is still straightforward in the same way that gravitation is. You can have free charges, of any magnitude, with no restrictions, and there’s only one particle required (the photon) to mediate all the possible electromagnetic interactions. Based on maths and models, there is no choice. However, as we know from our study of, say, light waves, we get many patterns and an appearance of randomness.

The strong nuclear force is one of the most puzzling features of the universe. The rules become fundamentally different. Instead of one type of charge (gravitation) or even two (electromagnetism), there are three fundamental charges for the strong nuclear force. Inside every proton or neutron-like particle, there are at least three quark and antiquark combinations, but how many is unknown as the list keeps growing. Gluons are the particles that mediate the strong force, and then it gets messy. It is worth noting that we don’t have the maths or a model, but it appears that there is still ultimately no choice as you cannot have a net charge of any type, but how it balances is well beyond me. However, as we know from our study at CERN using the Large Hadron Collider, the strong nuclear force is quantum in nature and has a property that means it only exists when observed.

In nature, we have one, two or many forces, and each can create structure and randomness but can anything in nature truly make a choice or decision?

Extending, does information have a choice?

Two magnets, north and south, but information now defines distance and strength. Therefore information determines that there can only be one outcome. The observer knowing the information can only ever observe the single outcome — three magnets sort of facing off: north, north, south. A complicated relationship but position, distance and field strength are known; therefore, the outcome can be modelled and predicted.

Further, we can now move to a dynamic model where each of the magnets rotates and moves during the period. What happens when information includes the future probability position of the magnets. Does information enable the magnets not to move right now, as they know from information that it is not worth doing as it will not change the outcome and could conserve energy? (This being a fundamental law of thermodynamics.)

However, as with unpacking the onion, this is overly simplistic as gravity and electromagnetism are defined and bounded by the “Laws of Relativity and Thermodynamics”.” In contrast, the strong nuclear force is defined and bounded by the Laws of Quantum. Gravity and electromagnetism are deterministic in nature as there is no choice as per the laws. The interaction of a complex system can make something look random, but when removed from time and point observation, the laws define the pattern. Whereas the strong nuclear force being quantum means we don’t know its state until we observe it, which fully supports chaos/ randomness and perhaps something closer to being presented with a choice, aka free will. It is not so much you can do anything, more than you can pick between states rather than just a defined or a predetermined flow from start to this point bounded by the foundational laws of relativity.

Does information have a quantum property? Insomuch that it is only when the observer looks and can act, does it become that state? Think carefully about this in a context of bias.

Can information or knowledge enable choice?

Does information require energy as if so, does the very nature of an informational requirement changes the outcome? (Heisenberg Uncertainty Principle.) Can something determine that to minimise energy expenditure it should wait as a better less energy requirement with the better outcome that will come by later? How would the information know to make that decision or choice? What rule would it be following?

We are asking that, based on information, the general rules are ignored. This idea means we would step over the first outcome or requirement, preferring to take a later option. Has information now built an experience which feeds into knowledge? But what is information in this context? Consider the colour of petals or leaves in autumn. Science reveals that colour is a derivative of visible light. A red leaf reflects wavelengths longer than those of a green leaf. Colour is a property not of the leaf but how the leaf interacts with light and also the eye and how we then determine how we will describe it as a common sound (words). Assuming the observer has the right level of vitamin C and brain structure — which all adds further dimensions. What we think of as intrinsic properties (information) of the world are merely effects of multiple causes coinciding, many small signals. Reality, in this sense, is not so much physical things, but interactions and flow. The same applies to touch and smell.

intrinsic properties (information) are merely effects of multiple causes coinciding

Remember we are asking how we get to make a choice, based on the idea that if it does not appear in nature, it is probably not natural. Have we convinced ourselves that complexity creates free-will?

Free-will, can you make a decision?

Reflecting on the title question. Is the data presented to you, enabling a real choice? Given that choice and free-will have a predication, that you have agency and can choose or decide, then we have a 2nd question. How is free-will possible when the foundations (energy types) you are building on does not appear to create choice? Yes, the appearance of randomness, yes only exists on observation — but does that create choice?

We have to admire those tiny signals which present themselves as choices at scale, as nothing has an overall significant effect. Everything has a flow. Does this lack of dominant signal create an illusion of free-will or ability to make a choice? When the signals are big, loud and noisy, drawing out small signals — is choice taken away?

Executive leadership

In the context of leadership, it is not that we are programmed, but is it that great leaders are highly tuned, and responsive to small signals that most of us don’t know are there because we are too busy or following instructions.

Leadership demands access to small signals to be able to exercise judgment. However is our love of traffic light dashboards, summaries, 1-minute overviews, elevator pitches, priorities, urgency, fast meetings, short pitches, executive summaries and KPI’s creating management signals that are driven by data which can only focus on the priority loud, noisy signals? The more layers and filters that data passes through both smaller signals are lost, and there is an increasing loudness to one path, no decision and removal of choice.

Does prominent signal notification mean we reduce our leadership’s sensitivity only to see the obvious? The same leadership we then blame for not sensing the market signals, or not being responsive, nor following their lead when they do!

Decisions (choice) or judgement

Human brains are constructed or wired to create and discover patterns, to which we ascribe meaning and learning. Signals help us form and be informed about forming and changes in patterns and how they align or otherwise to a previous pattern. Therefore we love signals that help us form or manage patterns which we equally call rules and heuristics.

Management theory teaches and rewards us on prioritising signals, especially the loud, noisy, obvious ones that are easy to see and understand. Using the example of a cloud (one in the sky, not a server farm), it is an unmistakable signal. A cloud is right here, right now. It is big and obvious. Clouds are a data point; observing clouds provides us with highly structured single-source data. The data we collect about clouds in front of us is given to our data science team who will present back insights about the data that is collected, giving us all sorts of new information and knowledge about the data we have. Big signals win. The statistics team takes the same data set and provides forecasts and probabilities based on maths, inferring insights based on data that is not there. The outcome from both teams may be different, but they both present significant overriding signals telling us what decision to make, based on the clouds data.

Another approach is to look at the system: how and why did the cloud form? Where did it appear? Where is it going? By gathering lots of data from different sources and seeking many signals, we can look at systems. Sensors are detecting light level, wind direction and speed, ground temperature, air temperature for 100 KM round and 25 miles KM high — lots of delicate low signal data. It is unstructured data. Feeding the data into the teams, the data analytics team brings knowledge of the system, its complexity and what we know based on the data. The statistics team can provide forecasting and probability about clouds and not clouds. Small signals that in aggregate creating choice and allowing for judgment. Our small signals give confidence that our models work as we have cloud data and that cloud data confirms that our signals are picking up what our environment is saying.

Side note, the differences between “data analysis” using data science and statistics. Whilst both data scientists and statisticians use data to make inferences about a data subject, they will approach the issue of data analysis quite differently. For a data scientist, data analysis is sifting through vast amounts of data: inspecting, cleansing, modelling, and presenting it in a non-technical way to non-data scientists. The vast majority of this data analysis is performed on a computer. A data analyst will have a data science toolbox (e.g. programming languages like Python and R, or experience with frameworks like Hadoop and Apache Spark) with which they can investigate the data and make inferences.

If you’re a statistician, instead of “vast amounts of data” you’ll usually have a limited amount of information in the form of a sample (i.e. a portion of the population); data analysis is performed on this sample, using rigorous statistical techniques. A statistical analyst will generally use mathematical-based techniques like hypothesis testing, probability and various statistical theorems to make inferences. Although much of a statistician’s data analysis can be performed with the help of statistical programs like R, the analysis is more methodical and targeted to understanding one particular aspect of the sample at a time (for example, the mean, standard deviation or confidence interval).

T hese data analysis approaches are fundamentally different and produce different signals; for a full story, you often need both.

Does a leadership team choose or decide?

As a senior leader, executive or director, you have to face the reality of this article now. Right now, you have four significant noisy signals to contend with: Critical parts of your company are presenting your with large signals using:

statistical analysis based on an observable point

data science analysis based on an observable point

statistical analysis based on a system

data science analysis based on a system

Do you know what type of significant loud signals you are being given and are they drowning out all the small signals you should be sensing? Who sits around the table is sensing small signals? Are you being presented with a decision, or are you being guided to a favourable outcome based on someone else’s reward or motivation? How do you understand the bias in the data, analysis and where are the small signals? Indeed to quote @scottdavid “You have to hunt for the paradoxes in the information being presented because if you cannot see a paradox you being framed into a one-dimensional model.”

Further, have you understood that data is emerging outside of your control from your ecosystem that has different ontologies, taxonomies and pedagogy, meaning that you will probably only discover signals and patterns that don’t exist.

Decision-making skill based on sensitivity

I wrote about Leadership for “organisational-fitness” is different from the leadership required for “organisational- wellness” in Sept 2020. The article explored the skills needed by executive leadership in decision making to help a company be fit and well ( different things)

The chart below highlights how skills should be formed over a period to create individuals who can work together with other professionals who can deal with highly complex decision making (judgment). The axes are ability and expertise level on the horizontal axis (x) and the decision environment on the vertical axis (y). The (0,0) point where the axis’s cross is when you first learn to make decisions. Note this has nothing to do with age or time. Starting from the Orange zone — this is where we make simple decisions. A bit like gravity, there is only one force and one outcome. You are encouraged to find it and make the right choice (even though ultimately there is no choice.) The grey areas on either side are where the “Peters Principle” can be seen in practice; individuals act outside of their capacity and/or are not given sufficient responsibility and become disruptive. The Pink area is where most adults get to and stay. We understand, like electromagnetic forces, there are two options or more. We search out the significant signals and those that bring us the reward to which we are aligned. We develop and hone skills at making binary choices. The yellow/ mustard zone is where many senior executives get trapped as they are unable to adapt from acting in their own interests to acting in the best interests of the organisation and eco-system as all their training is how to perform better in their own interests and rewards (KPI’s linked to bonus). In the yellow zone, you have to create and build a whole new mental model. Like John Mayard Keynes as you learn more, you do U-turns, adapt your thinking, change your philosophy and adapt. Never stop learning. At this point, you wrestle with quantum decision making and find you are looking for the small signals in the chaos and need trusted advisors and equal peers. You seek out and find a paradox, never believing the data, not the analysis nor the steer that someone else is presenting. This is hard work but leads to better judgment, better decisions and better outcomes.

Take Away

Decisions are often not decisions; the choice is not always real, especially when the foundations of them are simple and binary. Leaders need to become very sensitive to signals and find the weak and hidden ones to ensure that, as complexity becomes a critical component of judgement, you are not forced to ratify choices. Ratification is when choices are not understood, the views are biased, and the decision likely fulfils someone else’s objectives.

As a director, we are held accountable and responsible for our decisions; we must take them to the best of our ability. As automation becomes more prevalent in our companies, based on data, we have to become more diligent than ever if we are making judgment, choices or decisions or just ratifying something that has taken our choice away to fulfil its own bias and own dependency using big signals.