Three important Intelligence Thinking Patterns to deal with Occurrence and Probability

 

“What has been, is what will be, and what has been done is what will be done, and there is nothing new under the sun” (Ecclesiastes 1:9)

This citation from the Bible may have more than one interpretation. If asking a religious scholar or a Kabala expert, we can expect a comprehensive and long lecture about the time-line of history, the upper and lower worlds, etc. But, I prefer the simple explanation saying that everything that has the potential to happen in the world, either had happened before or might happen in the future. For me, as a human being, in my lifetime, all of this might occur depending on its probability.

I argue that this insight is one of the most significant thinking patterns we, as intelligence thinkers, must adopt. I would like to elaborate on the two key-factors of this insight and then to suggest a thinking pattern paradigm.

The range of possible occurrences — The things that have the potential to occur, distributed in a certain logic. Whether it is a flat curve or the curve of a normal distribution, we do not know which is true. I agree that it is beyond our grasps and our human limitations, so we have to guess. But, before referring to that, I want to emphasize that the deep meaning of it is the acknowledgment that anything that can happen, will.

Distribution and probability — Things happen, but some things happen many times in a time-unit while other things are rare to see. It brings me to the assumption that occurrences distribute in a normal manner. This influences and reflects the probability of occurrences. There is a high probability for things that occur frequently, and there are occurrences that have a lower probability, some of them adjacent to zero (long-tail). My point here is that everything has a probability, and we can not dismiss it as if it would never happen.

For us, dealing with intelligence, these two key-factors are in the foundations of our business and our way of thinking. The problem here is that some of us, if not most of us, tend to round-corners. Not from being unprofessional or lazy rather, from having an objective, and many times, justifiable reasons.

If a client asks for research about something that might occur in the market, it usually comes with a time limitation, a limited budget, and we know not to make more than three actionable suggestions. This is the nature of our job. We do not have the time, nor the energy, to explore every single outcome and try to figure out its probability.

What if we do have enough time and energy to scan the whole range and calculate the probability for A to occur?

I’d like now to discuss three thinking patterns dealing with this situation. The first two I will discuss shortly, and I intend to elaborate on the last one.

Instinct — This a very powerful tool for intelligence researchers, and it is based mainly on experience and observations. A good instinct can lead us to the right solutions and ‘sense’ the probability of A to occur. Instinct is based, many times, on primary research, and it reflects our insights. But, it is also very subjective and tends to be biased. Although a good and powerful tool, it can not be used solely.

Common Knowledge — This term, taken from the game theory, indicates that I know that you know that I know that you know something. We all know the same thing and with full transparency. Since all there is to know is known by all, it is much easier to calculate the probability of A to occur since all the relevant DB are open and known, frequency of A along the time-line is known, and much research had been done about almost everything. This pattern usually includes data-scientists and statisticians.

It also deals with AI, ML, and statistical algorithms. The emerging field of forecasting derives from this pattern, and it is based also on secondary research. The two main problems with this pattern are: it sounds like a utopia, and there are too many figures and insights based only on quantitative calculations.

Backward Induction — This is the most sophisticated and complex pattern, in my opinion. I sometimes call it a Pre-mortem analysis of occurrence. Whereas the term Backward Induction comes from the game-theory, the term Pre-mortem analysis comes from project managing and it usually refers to the theoretical analysis of failed projects before they fail.

Either way, the concept here is to start with an outcome, as marginal as can be, and try to unfold the sequence of occurrences which led to that outcome.

Given that A has recently, theoretically, happened with an unknown probability, the first step would be to go back in time and try to figure out what occurred (or what were the occurrences, causes) resulting in A.

Now it is time to ask a set of questions about the causes. Do we know the probability of the causes? If yes, then we write it on the side and remember it. If not, then we ask another question. Do we have any data on the causes, which could help us assess its probability? If yes, then write and remember. If not, then proceed to the next unfold step.

Each one of the backward unfolding steps, gets us nearer to the root-occurrences, (root causes), resulting in A. And every time we reveal the previous occurrences, we ask the same set of questions.

By the end of the Induction process, we might reveal the full sequence of occurrences from the root-occurrence to A.

Finishing the Backward Induction process might provide us with a list of probabilities of occurrences, gathered along the way, to be able to calculate or assess the probability of A to occur.

The next table shows the process visually

To sum up this article, I started with two key-factors that, in my opinion, are in the foundation of Intelligence Thinking Patterns. Everything might happen in our lifetime, and everything has its probability to happen. Then I suggested three main thinking patterns to take when trying to deal with uncertainty and vague reality when in research.

Last, I tried to demonstrate the adoption of processes, taken from ‘Game Theory’ and from PM, to unfold and reveal the sequence of occurrences from a theoretical outcome A to its root-occurrence and, by that, try to calculate or assess the probability of A to occur.

(always at your service https://www.webintelligency.co.il/)