Still On Automation: Of Algorithms, Biases, Cognitive Illusions and Dangers To Your Wealth

After writing the last article on my obsession to automate savings and investments for myself and many others, I picked up the 2011 bestseller ‘Thinking, Fast and Slow’, by the Nobel Prize winner Daniel Kahneman, and found some very useful tips. This article borrows heavily from his wisdom and also from another classic by Nassim N. Taleb’s ‘The Black Swan’, on why it is futile for humans – even supposed experts- to think we can always predict the future (better than a mathematical formula or algorithm).

What these authors -behavioural economists and psychologists- have come to find out from numerous researches in a nut shell is: ‘Your biases and illusions, heuristic beliefs and assumptions might be the greatest threats to you becoming wealthy’. My partner and co-founder ‘Bowale Banjoko will say “You are proud...and your pride will keep you poor!”

I lost over $2000

I always thought I could trade better than a robot so I did not bother getting one. But I learnt the hard way recently when I became overconfident and cocky about my Forex skills and transferred into my Forex accounts my highest amounts ever –disobeying my own rules and amount limit for trading. And then I lost it all while chasing other losses, thereby reinforcing the need for automation.

Efficient formulas and algorithms are the key to human and organisational effectiveness

I am convinced more than ever that what humans should bother (themselves) about is ‘supervising’ emotion-less formulas and algorithms, dispassionate computer systems and processes –tweaking them where necessary, and even changing them completely as time goes on in order to make them more efficient, and humanity more effective in achieving goals and set objectives.

It is no coincidence that regulators expect investment firms to put the label “Past success is no guarantee of future success” on their investment products. Not only is that very true, the three books that I quote in this article prove that statement to be true in all cases. And for any investment professional or institution to say otherwise is to attempt to play God, and fail proudly.

Nassim Taleb’s Black Swan Events

In The Black Swan, NNT shows us ‘experts’ often are not better than other humans in being able to correctly predict events, even though they have acquired knowledge in that field, and spent years practising (in) it. Yet, many of them think they are superhuman, and can foretell unpredictable events such as knowing which baby will die of sudden infant death syndrome (SIDS); which company will go on to become a Unicorn- the next Facebook or Uber or AirBnB; which child will be a genius and which one will end up as a recidivist criminal if not a serial killer; whether the shares of Apple or Microsoft will rise in the next quarter or be down this time next year, and so on.

Taleb called such unpredictable events ‘Black Swans’. According to NNT, the black swan is an event, positive or negative, that is deemed improbable yet causes massive consequences. And a famous illustration he used is the Chrismas Turkey: Nothing on December 24th prepares or tells the Turkey that was being fed religiously every day from January 1st that it was going to be killed on the 25th by the same hands that had been feeding it. 

The graph that was moving up perfectly, drops all of a sudden for that turkey without prior warning. And to believe we can always determine or correctly tell the future is to deceive ourselves.

adapted from The Black Swan by Nassim Taleb

Daniel Kahneman’s Thinking fast and slow

In Thinking, Fast and Slow, Daniel Kahneman discusses illusions in the section Overconfidence, including the illusion of understanding, the illusion of validity and the illusion of stock-picking skill.

Kahneman mentions the report ‘Trading is hazardous to your wealth’ by the finance professors Terry Odean and Brad Barber that showed that on average, the most active traders had the poorest results, while the investors who traded the least earned the highest returns. Other researches show that individual traders eventually lose all their money to established institutions that mostly trade through automated systems.

He continues in ‘The illusion of pundits’, “As Nassim Taleb pointed out in The Black Swan, our tendency to construct and believe coherent narratives of the past makes it difficult for us to accept the limits of our forecasting ability. Everything makes sense in hindsight, a fact that financial pundits exploit every evening as they offer convincing accounts of the day’s events. And we cannot suppress the powerful intuition that what makes sense in hindsight today was predictable yesterday. The illusion that we understand the past fosters overconfidence in our ability to predict the future.”

And in the chapter ‘Intuitions vs Formulas’, I quote directly;

"Why experts are inferior to algorithms

One reason, which the psychologist Paul Meehl suspected, is that experts try to be clever, think outside the box, and consider complex combinations of features in making their predictions. Complexity may work in the odd case, but more often than not it reduces validity. Simple combinations of features are better. Several studies have shown that human decision makers are inferior to a prediction formula even when they are given the score suggested by the formula! They feel that they can overrule the formula because they have additional information about the case, but they are wrong more often than not.

According to Meehl, there are few circumstances under which it is a good idea to substitute judgment for a formula. In a famous thought experiment, he described a formula that predicts whether a particular person will go to the movies tonight and noted that it is proper to disregard the formula if information is received that the individual broke a leg today. The name “broken-leg rule” has stuck. The point, of course, is that broken legs are very rare—as well as decisive. Another reason for the inferiority of expert judgment is that humans are incorrigibly inconsistent in making summary judgments of complex information. When asked to evaluate the same information twice, they frequently give different answers. The extent of the inconsistency is often a matter of real concern. Experienced radiologists who evaluate chest Xrays as “normal” or “abnormal” contradict themselves 20% of the time when they see the same picture on separate occasions.

A study of 101 independent auditors who were asked to evaluate the reliability of internal corporate audits revealed a similar degree of inconsistency. A review of 41 separate studies of the reliability of judgments made by auditors, pathologists, psychologists, organizational managers, and other professionals suggests that this level of inconsistency is typical, even when a case is reevaluated within a few minutes. Unreliable judgments cannot be valid predictors of anything.

The widespread inconsistency is probably due to the extreme context dependency of System 1. We know from studies of priming that unnoticed stimuli in our environment have a substantial influence on our thoughts and actions. These influences fluctuate from moment to moment. The brief pleasure of a cool breeze on a hot day may make you slightly more positive and optimistic about whatever you are evaluating at the time. The prospects of a convict being granted parole may change significantly during the time that elapses between successive food breaks in the parole judges’ schedule. Because you have little direct knowledge of what goes on in your mind, you will never know that you might have made a different judgment or reached a different decision under very slightly different circumstances.

Formulas are unemotional and consistent

Formulas do not suffer from such problems. Given the same input, they always return the same answer. When predictability is poor—which it is in most of the studies reviewed by Meehl and his followers—inconsistency is destructive of any predictive validity.

The research suggests a surprising conclusion: to maximize predictive accuracy, final decisions should be left to formulas, especially in low validity environments. In admission decisions for medical schools, for example, the final determination is often made by the faculty members who interview the candidate. The evidence is fragmentary, but there are solid grounds for a conjecture: conducting an interview is likely to diminish the accuracy of a selection procedure, if the interviewers also make the final admission decisions. Because interviewers are overconfident in their intuitions, they will assign too much weight to their personal impressions and too little weight to other sources of information, lowering validity.

The most important development in the field since Meehl’s original work is Robyn Dawes’s famous article “The Robust Beauty of Improper Linear Models in Decision Making.” The dominant statistical practice in the social sciences is to assign weights to the different predictors by following an algorithm, called multiple regression, that is now built into conventional software. The logic of multiple regression is unassailable: it finds the optimal formula for putting together a weighted combination of the predictors. However, Dawes observed that the complex statistical algorithm adds little or no value. One can do just as well by selecting a set of scores that have some validity for predicting the outcome and adjusting the values to make them comparable (by using standard scores or ranks). A formula that combines these predictors with equal weights is likely to be just as accurate in predicting new cases as the multiple-regression formula that was optimal in the original sample. More recent research went further: formulas that assign equal weights to all the predictors are often superior, because they are not affected by accidents of sampling.

The formula for marital stability

The surprising success of equal-weighting schemes has an important practical implication: it is possible to develop useful algorithms without any prior statistical research. Simple equally weighted formulas based on existing statistics or on common sense are often very good predictors of significant outcomes. In a memorable example, Dawes showed that marital stability is well predicted by a formula:

        frequency of lovemaking minus frequency of quarrels
You don’t want your result to be a negative number.” (lol)

APGAR SCORE: the algorithm that has saved millions of babies

“The important conclusion from this research is that an algorithm that is constructed on the back of an envelope is often good enough to compete with an optimally weighted formula, and certainly good enough to outdo expert judgment. This logic can be applied in many domains, ranging from the selection of stocks by portfolio managers to the choices of medical treatments by doctors or patients. A classic application of this approach is a simple algorithm that has saved the lives of hundreds of thousands of infants. Obstetricians had always known that an infant who is not breathing normally within a few minutes of birth is at high risk of brain damage or death. Until the anesthesiologist Virginia Apgar intervened in 1953, physicians and midwives used their clinical judgment to determine whether a baby was in distress. Different practitioners focused on different cues. Some watched for breathing problems while others monitored how soon the baby cried.

Without a standardized procedure, danger signs were often missed, and many newborn infants died.

One day over breakfast, a medical resident asked how Dr. Apgar would make a systematic assessment of a newborn. “That’s easy,” she replied. “You would do it like this.” Apgar jotted down five variables (heart rate, respiration, reflex, muscle tone, and color) and three scores (0, 1, or 2, depending on the robustness of each sign). Realizing that she might have made a breakthrough that any delivery room could implement, Apgar began rating infants by this rule one minute after they were born. A baby with a total score of 8 or above was likely to be pink, squirming, crying, grimacing, with a pulse of 100 or more—in good shape. A baby with a score of 4 or below was probably bluish, flaccid, passive, with a slow or weak pulse—in need of immediate intervention. Applying Apgar’s score, the staff in delivery rooms finally had consistent standards for determining which babies were in trouble, and the formula is credited for an important contribution to reducing infant mortality. The Apgar test is still used every day in every delivery room. 

Atul Gawande’s recent A Checklist Manifesto provides many other examples of the virtues of checklists and simple rules.”

 [According to the book, ‘thinking fast’ refers to  that part of our brain denoted by the psychological term System 1 which operates automatically and quickly, with little or no effort and no sense of voluntary control. While ‘thinking slow’ refers to System 2 which allocates attention to the effortful mental activities that demand it, including complex computations. The premise of the book is that it is easier to recognize other people’s mistakes than our own.]

Tony Robbins says don’t take the Money game more seriously than you should

In the book Money Master the Game, which I already reviewed extensively here, Tony Robbins talked about the myth of believing the $13 Trillion lie (of ‘financial investment experts’): ‘Invest with us, we will beat the market’. Only very few, rare people have the luck of having beaten the market consistently, the others are only ‘lucky’ occasionally and fail often. And Tony went on to explain that though individual investors always seek the next ‘Star’ investment fund to put their money in, it is always better to invest as part of a system/low-priced mutual fund that mimics the market.

Individuals cannot on their own (‘expert judgment and skills’) beat the market overtime especially trading against organised, well established institutions with deep pockets and advanced algorithms. It is even foolhardy to try.

Automations already exist, find the best one for you

Finally however, I must state that there are out there in the world so many automated systems and apps for savings, investments, and budgeting, and there are thousands of corporations/societies/networks that help people make the right choices.

Many, I have mentioned on this blog in the past including Acorns, Mint, Digit, YNAB, America'sbest401k, Lifetimeincome, Money Master the Game’s financial app, and so on.

The problem is that many of them do not take into account people living in Africa and other developing economies, or do not have bespoke solutions for our unique environment.

So while I am trying to do my bit for the developing world, I urge you to become obsessed with finding a system that helps make your life easier where you are presently.

So if you want to invest, find and invest with a good low-priced Funds management team, or if you are looking to trade online in Forex or stocks or binary options or sports betting, you can buy/rent/install a tested software or robot, or employ a good programmer to automate your strategy for you.

Also, if you want to utilise the power of a network, then find and join a good savings and loans society (or cooperative society if you are in Africa) with objectives similar to your life values. For those in the developed world, such websites as Lending club, Prosper, Upstart and a host of others already offer these easily, you will do well to use them. Read up reviews on popular websites and blogs so that you are armed with adequate information about them.

I wish you all the best. Cheers!

N.B: The APGAR score is not the only algorithms used in Medicine today. By the time I graduated from medical school, all hitherto-subjective clinical exams had been replaced by a more objective OSCE, in which the expected answer to clinical questions was graduated against scores. Now, the medical student has to perform the clinical examination the right way and could score all the marks available or no mark at all. Gone is the subjective victimisation by professors of their students due solely to (past) biases, halo effect or personality idiosyncrasies and illusions.