Decision making in a time of uncertainty: part 2

Statistics, Probability, Newton and The Old Bailey

Newton's laws are wonderful heuristics for our brains to understand and manage risk.

To paraphrase:

• Things keep going unless you get in their way
• If you get in their way its going to hurt
• The amount it hurts depends on how fast they were going and how big they are

We can all see when a car is going to hit us and quite quickly figure how to get out of its way. The immediacy of this decision is made easier because our brains have evolved to understand Newtonian events that unfold linearly. The little statistician numbskull in our head does his calculation and our feet do the walking (or running).

Despite our intrinsic ability to avoid being run over by cars, most humans struggle to understand the probability of events taking place and in particular the impact of outliers.

The 2016 presidential election that resulted in Donald Trump becoming President is a good case study in this respect. Every media poll showed Hillary Clinton winning. The most authoritative poll just prior to the election gave Clinton a 71% chance of winning—and Trump a 29% chance. Most people saw these odds as so overwhelming as to be a certainty. But looking at it in reverse this meant Trump had an almost 30% chance of winning.

The colloquial language of risk doesn't help either. For example psychologists at the University of Oslo found that events deemed as "improbable" are perceived to have close to 0% chance of happening whilst "unlikely" events come in between 10% and 30%. This makes Jury service interesting when looking for "reasonable doubt".

I once asked a Judge at the Old Bailey where I served as foreman on a murder trial "what percentage probability do you mean when you say "reasonable doubt". He gave the Jury the following guidance "if in your view the likelyhood of an event happening is neither fanciful nor fantastic then it is probable". Needless to say this clear guidance was less than helpful.

The biggest challenge with an environment of constant change however is that the odds keep changing and the numbskull statistician in our brain is limited in the number of calculations he (or she) can do. Even grandmaster numbskulls have limits. Moreover many of the changes that we are contemplating in the century we live in are are neither linear, nor statistical. 

Monty Python Risk

Many complex phenomena (such as global warming, pandemics or population dynamics) are probabilistic and result in exponential change. This means that there appears to be no danger at all for a very long time and then suddenly it is upon us. I call this the Monty Python risk profile – after a scene in the Holy Grail when a castle is stormed by Knights that appear to be very far away but then suddenly are upon their target enacting a whirlwind of surreal butchery. Our brains are not wired for this type of change and we assess it poorly. Accordingly it catches us unprepared every time. This is why Black Swan events are in hindsight entirely predictable – because our monkey brained numbskulls lack the equipment to do the calculations.

Resistance, Resilience and Reversion

In a linear statistical world things tend to exist within fairly narrow boundaries of change or change that happens slow enough that we can see it coming and plan for it. Typical strategies for survival are therefore based on building resistance, resilience and reversion. In biological systems homeostatic mechanisms regulate the body to maintain constant optimal conditions in the face of moderate stress. Resilient businesses withstand choppy markets to bounce back. Earthquake engineers build structures from steel and design tall towers to move around dissipating the kinetic energy of tectonic plate movements. However outside of a fairly narrow range of volatility our coping mechanisms are overwhelmed and become brittle. Resistance and Resilience have limits and Reversion only holds whilst there is a mean to revert to.

Anti-fragility

In times of volatile, constant exponential change our coping and planning mechanisms fail us. To survive and prosper in these time we require something else. Nassim Taleb has proposed the answer.

A prophet of probabilistic exponential risk Taleb is a former Wall Street trader who made his fortune betting on crashes. He is a formidable student of philosophy and sharer of epithets of wisdom.

Taleb describes the ideal organisational strategy for a universe of constant uncertainty as one that is Anti-fragile. Simply put an Anti-fragile entity thrives on chaos (and if that sounds familiar it is because Tom Peters coined the phrase back in 1987 publishing his book of the same name the same day as the first great modern stock market crash). 

In a nutshell Anti-fragile systems benefit from shocks; they thrive and grow when exposed to volatility, randomness and disorder. Anti-fragile people love adventure, risk, and uncertainty. They come alive in the emergency room.

Appropriately this phenomenon is well studied in medicine, where for example Wolff's Law describes how bones grow stronger due to external loads. Hormesis as exemplified by Rasputin is the anti-fragile process whereby small doses of a poison can make the recipient more resistant to its effects. Taleb discussing this point in depth notes that our attempts to create Maginot lines of risk protection may actually make us weaker. This is the same philosophical position taken by those who call the millenial offspring of helicopter parent baby boomers "snowflakes".

Snowglobes, ER and Skin in the Game

This brings me to the inescapable conclusion that in times of great uncertainty we must think differently about our lives, about the organisations and systems we build and how we operate. The optimal approach for such times is one that works better when shaken up (like a snow globe) are staffed with people that love uncertainty (like ER doctors) and build structures that adjust and adapt to change forming new and better bonds as a result of volatility (networks and ecosystems).

Taleb channels Immanuel Kant when describing the absolute key in risk management during such turbulent times as having "Skin in the Game". Kant offers the example of a doctor giving a diagnosis. He asks if the doctor had to bet his salary on the diagnosis would he give a different one?

These days this is precisely what we are doing every time we make a strategic decision. In times of uncertainty the fact is that we all have skin in the game.