We all know that decision-makers fight with many cognitive biases. For some reason, we believe just “the other guy” has a problem with this. What we believe feels so true. It turns out that if you know what goes wrong in your thinking, then you can circumvent its downsides.
The 5 deadliest mistakes are:
#1 Use your gut and common sense when dealing with small or large likelihoods
The first mistake is the tendency of humans to underestimate small percentages and to overestimate the impact of large percentages.
This is based on the work of “Daniel Kahnemann” (who is a winner of the Nobel Price in Economics for it) His work together with Tversky found the incapacity of humans to handle small and large percentages. It proves that we are loss averse (pay more for an unlikely loss (insurance)) and risk-taking (pay for an unlikely high gain (lottery effect)) at the same time.
Let´s take this: What happens if you are in price negotiations?
While negotiating, there is always a risk of not getting a deal. The typical decision of negotiators now is to lower the price to raise the likelihood of winning the deal. The phenomenon behind this is the (irrational) loss-aversion of the negotiator.
Actually, our brain is quite bad with numbers. Do you know how much more dangerous is to drive a car than flying an airplane?
Why are 42% of humans then afraid of flying but not to drive a car?
Be aware of the loss aversion and risk-seeking tendency of human. Instead, don’t give the decision to the gut. It is biased. Develop a decision calculus.
#2 Believe that true risk can be measured in past data
The second mistake is a misconception of risk. Risk management is seen as a procedure for taking past data and calculating likelihoods from it.
When done without software, we over or underestimate the likelihoods.
But the true mistakes happen in the belief that past data CAN measure future risks.
50% of the stock market changes of the past 50 years happened in 10 days. The financial crises in 2007 was so obvious — just after it happened.
Look at the famous “Kodak” or “Nokia” cases. Things are happening where you can´t think of.
This is the risk. A true risk is something unknown — not expected.
It is easy to protect against threats that happened in the past. Because of this: Its not a risk anymore.
Unexpected shocks instead can be managed to become anti-fragile and robust against unknown challenges. Any living creature is made that way. If you loose an eye, an ear, a finger or a lung-wing, you still can survive.
If you need to run 10 miles every day, you get stronger or more robust.
This is what businesses need to do to prevent risk — becoming that robust and strong to withstand any weather.
#3 Considering selected facts and case studies as proof
The third mistake is all about case studies. How can you convince decision-makers? Sure, use case studies.
To prove a theory, it makes sense to provide evidence, give facts, and show case studies.
The problem: anyone can cherry-pick those facts and case-studies that fit the theory. If you believe in facts, you will likely become a victim of snake-oil storytellers.
You see it in the actual vaccination debate — both sides -pro and contra vaxers are showing examples — people died because of the virus or the vaccine. If you see victims laying in a hospital or doctors fighting for life’s, you often don’t need a second “case study”.
It is dangerous to use singular cases to prove a hypothesis.
Instead, it takes a validation on a larger sample that is representatively sampled.
#4 Being unaware that your world view is biased.
The fourth mistake is called the “Truman Show Synonym”. It can be described as the tendency of human to overestimate their own opinion. Science refers is as the confirmation bias.
People are trying to search for examples and specific data. In this search our unconscious brain brings information to our attention that are “relevant” to us (Cocktail party syndrome).
Relevant is everything that is in our favor or supports our own theories. It validates your existing believe and makes it even stronger the more you inform yourself — without being aware of the effect.
As a result, humans are basically a “Truman” in its own show. The real world is different.
Humbleness about your own opinion can be useful, because at the end you can manage your future more successfully if you know the truth — not just you feel great about your opinion.
#5 Infer Causality from Correlation
The fifth mistake is the famous correlation. Humans are growing up by using the methodologies of correlation. In many cases, this works perfectly well.
If you have a nail and hit it with a hammer — there is one cause you can even control by yourself. As a result, you see the impact right away. Correlation proves causality.
In this case, where you have a limited number of causes and you see the results shortly after the cause, the correlation is a perfect methodology for finding out what works and what not — Try and Error!
Unfortunately, the business world is different. There are plenty of important causes and context variables. Even worse, business decisions can take a long time until they show impacts.
Learning about cause and effect in these circumstances takes data about drivers, context, mediators, and outcomes. And it takes a causal modeling analysis.
Your pathway to better decision making
No matter what you do in marketing and sales, if your assumptions and insights are biased all your work, strategies, tactics, and implementation work can be wasted.
Wise business leaders know the cognitive biases and this is what they do:
#1 — Trust a decision calculus, not common sense when treating low (and high) likelihoods
#2 — Know that the true risk are threads you are not aware of (Black Swan effect)
#3 — Avoid case studies and selective facts and seek for analyzing representatively sampled sets of facts
#4 — Review your information seeking process and actively seek challenging theories
#5 — Avoid concluding from correlations and instead aim to perform causal modeling — most practically use Causal Machine Learning
There is an emerging technology readily available and already intensively tested. It provides a solution to those challenges: Causal Machine Learning and Causal AI.
It requires a causal mindset to make use of it. It requires you to understand that everything decision-makers are looking for is causal insights — the invisible link between actions and outcomes.
What are your thoughts on this?
Do you want to engage in an exchange? Reach out, and let’s meet on a virtual coffee chat: book your spot here.
Frank (connect here)