The same logic can earn you millions

This Is What I Predicted About Trump Following His Inauguration

J. Verweij
03 Apr 18

Using the approach of Decision Free Solutions to predict behaviour.

For a PDF file of this article click the download-icon on the left.

What this article is about (and what is in it for you)

On March 23, 2017, I published an article on LinkedIn titled ‘The Huge ‘Trump Decision Making’ Experiment -The LOGIC behind Trump not being able to change, to achieve his aims, or have others achieve them for him’.

To predict that Trump won’t change, won’t achieve his aims, and won’t have others achieve them for him is an easy thing to do today. When I published the article nine weeks after the inauguration, however, the common thread among editors and analysts of newspapers like the Washington Post and New York Times was that the weight of the office would have its impact on Trump. They assumed that the mere weight of responsibility would cause Trump to change his ways, that in this he would be guided by a Republican Congress and aided by experienced cabinet members. With Trump in the White House and Republicans in the majority in both House and Senate plenty of conservative legislation was going to be passed.

But also merely nine weeks after the inauguration it was an easy prediction to make. In fact, I had planned to finish and publish my article before the inauguration. But alas, with my youngest daughter born eight days before the election there was always something that got in between.

It was an easy prediction to make because it was a prediction based entirely on logic and observation. In this article I will provide a shorter and somewhat simplified explanation of this logic and the observations than in the original article (which is too abstruse even for my standards).

What is more, I will also try to bring across how along the same lines this logic will help you to distinguish between performers and non-performers, between experts and non-experts. In the world of business this can directly translated into earnings (indeed, up to many millions). If you can  distinguish between performers and non-performers you can have your aims achieved against minimal risk, using fewer resources and increasing benefits. All that needs to be done is avoiding decision making. In procurement, in management, in project management, in sales, in HR, in organisations as a whole.

But first Trump. Apart from this introduction and the very last section this article (especially the Trump sections) closely follow the original article, beginning with the same ten sentence summary as the original does (only somewhat shortened).

Ten Long-Sentence Summary

  1. If there is something you want to have achieved, you want to enlist an expert: an expert achieves an aim against minimal risk.
  2. To an expert the situation is transparent, and it is clear what the next step should be to achieve an aim.
  3. To become an expert, you must be able to steadily increase your level of understanding – a prerequisite for this is to be a ‘perceiver’: someone who has the ability to perceive and process information quickly.
  4. Measuring how much or how little information someone perceives (and how quickly he/she processes it) is practically impossible – but there are many easily observed characteristics which are directly related to perceiving information.
  5. To someone who perceives very little information situations will always remain unclear and the level of understanding hardly changes over time – instead of knowing what the next step should a ‘decision’ has to be made (Oxford dictionary: a decision is a conclusion or resolution reached after consideration – i.e. when the situation is not fully transparent).
  6. When someone makes a decision in a situation that is not transparent, the risk of not achieving the aim increases: decision making increases risk.
  7. Someone who perceives only little information has not choice but to make a lot of decisions – but there are many more, and more easily observable characteristics linked to perceiving very little information, as there are (from (Kashiwagi, 2016)): thinking in win-lose instead of win-win, trying to control and influence others, lack of transparency, lack of accountability, use of excuses, use of opinion instead of information, reactive instead of pro-active, short-term instead of long-term, reliance on relationships instead of merit, lack of vision, tactics instead of strategy, abusiveness, thinking of oneself instead of others.
  8. Based on the above logic, and simply by observing (not interpreting!) a range of characteristics of Donald Trump which have remained unchanged over three decades, it can be concluded he perceives very little information – this directly implies that president Trump has little to no capacity to change, as change follows from applying newly perceived and processed information.
  9. Someone who perceives very little information and who constantly has to make decisions, steadily increases the risk the aim will not be achieved – this is the logic behind president Trump not being able to achieve his aims.
  10. When someone perceives very little information, he will not be able to identify (and thus employ) experts, but will rely on relationships and loyalty instead, and at the same time this someone’s aims will remain vague and ambiguous – this is the logic behind why president Trump’s aims will not be achieved by others either.

Why Trump, and what is wrong with making decisions?

This article is not about president Trump. This article doesn’t vent any opinions either. This article is about why ‘decision making’ must be avoided, and how you can recognise whether someone or some organisation is capable of it. Trump’s presidency merely provides a unique opportunity to demonstrate the case in point. Every day. For as long as it lasts.

Decisions increase risk. This presents a paradigm shift and thus I hope you will forgive me for repeating myself: Whenever you need someone who is going to help you to achieve your aims, you want an expert. An expert minimises risk for you. An expert vendor, an expert project leader, an expert manager, an expert president. An expert knows what to do next. An expert is able to substantiate why the choice he or she is making will contribute to achieving an aim. An expert doesn’t make you think and wonder. An expert, also, doesn’t make decisions, where a decision is a choice which can not be substantiated to contribute to anything as it is always made in a situation which is not fully transparent (which follows from the very definition of ‘decision’). An expert doesn’t make decisions as decisions increase risk.

Experts minimise risk by minimising decision making (decisions which cannot be avoided are treated as risks). But how to recognise an expert? Experience and past performances help to identify an expert. However, in situations which are complex, dynamic, unique even, where achieving results depends on many factors, you want something else in your expert: ‘perceptiveness’. Perceiving the conditions and the ‘mechanisms’  which impact upon them which are relevant to achieving the desired outcome.

Perceptiveness, the ability to perceive, cannot be directly measured. But it is a characteristic, of an individual or of an organisation, which expresses itself in other, much easier to observe characteristics. If you are able to perceive changes, and are curious to learn how they interact, how they affect other aspects relevant to achieving an outcome, discovering the interrelatedness of conditions and mechanisms, you are able to see the bigger picture. You will see, for example, that win-win will benefit you or your organisation more than win-lose.

Where does Trump come into this?

Right here. On the spectrum of perceptiveness, from non-perceptive to all-seeing, Trump is on the extreme end of it. Trump demonstrates a range of characteristics, sometimes in extreme ways, which Dean Kashiwagi has linked to the absence of perceptiveness.

The clearest possible example of Trump not being able to perceive information, is his reliance on making choices which he can’t link to achieving a particular goal, i.e. ‘decisions’. Before Trump became president he used to go to work in the morning without a care in the world, totally unprepared, have people present something to him, and then he would use his gut instinct (in absence of an alternative) to make decisions. That is how he ran the family company, and logic has it that that is how he (would like to) run(s) the most powerful nation on planet Earth.

Many of the observable characteristics of president Trump are both very pronounced and entirely consistent with someone perceiving very little information (see number 7 of the ten sentence summary). Trump’s presidency provides the interested reader the opportunity to observe what happens when ‘the ultimate decision maker’ of the most powerful nation in the world has the interest, nor the capacity, to avoid ‘decision making’.

Why Trump won’t change

Kashiwagi’s Information Measurement Theory (IMT) is ‘a predictive theory that simplifies reality’. Based on the observation of a few characteristics the behaviour of individuals and organisations can be ‘predicted’.

In order to gain understanding, information needs to be perceived and processed. If the information is understood, it can be applied. This application results in change, and this change leads to the perception of more information.

Whether someone (or some organisation) perceives information cannot be measured. But perceiving information is correlated with making few to no decisions. If you perceive information you will be able to understand how things are connected. You are more likely to think in win-win, to take responsibility, be accountable, organised, non-abusive, etc. etc.

IMT does not predict the lottery or the weather, it predicts what can be logically expected within a given situation. Its predictive ‘power’ is increased with the number of characteristics that are observed, and especially with the ‘clarity’ of the observed characteristics. The more ‘clear-cut’ the observed characteristics, the more reliable the predictions.

In case a president would have shown a consistent and practically unchanging level of understanding over a long period prior to the presidency, then from IMT it follows that the president’s ability to perceive and process information would have to be small. Such a president would simply not be able to change, even if he or she wanted to.

The very public life of Donald Trump over several decades allows for such an observation. That is why it was so simple to make predictions on how Trump would be as a president. ‘Trump the president’ was always going to remain the same Trump for the simple reason he has no choice. For the very same reason you should never assume Trump to have a strategy with anything he does. He doesn’t. He never will. He simply can’t. I am not kidding.

Why Trump will not achieve his aims, or have others achieve them for him

In the Oxford dictionary an aim is defined as ‘a purpose or intention’. By asking the question ‘what is the purpose or intention’ you will learn about the aim. By observation, president Trump has defined very few aims. Increasing homeland security and increasing the number of jobs, are among those.

Logic has it that president Trump is very unlikely to achieve his aims, because he operates by decision making. By observation he does not create the conditions to avoid decision making. There is no clear effort made to provide transparency or to be objective. He is preoccupied with details, drives solutions by defining requirements (e.g. a border wall), and he appoints based on relationships.

President Trump also is very unlikely to have his aims achieved by others, because he does not identify experts (has no interest in them) and he has no unambiguous aims. In absence of an unambiguous aim i) the expert who is to achieve it cannot be identified and ii) even if the expert happened to be available he/she wouldn’t be able to achieve it because it is in the nature of an ambiguous aim that it is unclear when it is achieved.

Let’s take the building of ‘the border wall’ as an example. Perhaps, at first sight, this appears to be a clear aim to achieve. But from the above follows that the wall is a decision, not an aim. The aim of the wall is related to ‘increased security’ (which, one way or another, can be measured). In absence of a substantiation how the wall will contribute to ‘increased security’, it is merely a ‘requirement’ defined by a non-expert. The wall thus increases the risk that this aim will not be achieved.

If we assume that building the border wall is an actual aim, then this aim, by observation, is still ambiguous. What exactly is the wall to achieve, how to measure its success? Regardless, some agency will be appointed to get the wall built. In absence of transparent aims decisions must be made each time a choice presents itself. Each decision increases the risk that the border wall will not be achieved, to the point that, in all likelihood, it won’t.

Unless, of course, president Trump decides the border wall has, in fact, been achieved.

How to minimise risk in practice (and allow you to make your millions)

The logic in this article stems from Information Measurement Theory as developed more than 25 years ago by Dr. Dean Kashiwagi (Kashiwagi, 2016), and has been used and turned into the generic and systemic approach of Decision Free Solutions (DFS) (Verweij, 2016, 2017). This approach consists out of four steps and five principles to minimise risk in achieving a desired outcome. These principles have resulted in the methods of Decision Free Organisations, Decision Free Management, Decision Free Procurement, Decision Free Sales and Decision Free Birthing, to name only a few (Verweij 2016a).

Millions can be earned by identifying the right vendor, the right product, the right solution to achieve your aims. By having organisations work towards transparent and measurable aims, aligning their employees’ expertise with the tasks at hand, and avoiding spending large amount of resources on layers of management to control, steer and manage a workforce that is entirely boxed in by the effects of decision making. By doing away with the need for departments filled with staff functions and unlocking your workforce’s creativity and expertise, improving the work atmosphere, and lowering turnover and illness related absenteeism.

Decisions can be avoided by following the four steps of DICE (Definition, Identification, Clarification, Execution) and by applying, at all times, the five principles of TONNNO (Transparency, Objectivity, No details, No requirements, No relations). By first identifying and then trying to avoid decisions you identify the areas where expertise is missing and where action is required. You will also identify the areas where expertise is sufficiently available, and you refrain from over-managing and controlling, and ensure the expertise is maximally utilised instead. The concept that follows from avoiding decision making is called ‘Risk Minimisation’: Risk Minimisation minimises risk which Risk Management can’t manage.

In procurement this logic has been applied thousands of times, on several continents, by private firms and public ministries alike, under the name of Best Value Procurement (Verweij 2016b). Now this same logic is  about to be taken a step further.

If you want to be a part of it, drop me a line.


Kashiwagi, D. [2016], 2016 Information Measurement Theory, Kashiwagi Solution Model (KSM), Mesa, Arizona, ISBN 978-0-9850496-8-3

Verweij, J. (2016), Introducing Decision Free Solutions – a generic systemic approach to minimize risk by avoiding decision making. Journal for the Advancement of Performance Information and Value, Vol 8. No 2, PDF available at

Verweij, J. [2016a], Services provided by Trees with Character: Decision Free Management, Decision Free Sales, see

Verweij, J., Kashiwagi D, [2016b], Introducing the Best Value quality checklist in procurement, see

Verweij, J. (2017). Making expertise matter – a simple introduction to Decision Free Solutions, an original Trees with Character column, January 3, 2017, PDF available at

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