Why Leaders Need to Think More Like Professional Gamblers

ONE OF the unfortunate side effects of living in an age of accelerating technology is having to deal with increased uncertainty. When faced with uncertainty, how should leaders react? Should they make a big bet, hedge their position, or just wait and see?

We tend to see situations in one of two ways: either events are certain and can, therefore, be managed by planning, investment, and reliable budgets; or they are uncertain, and we cannot manage them.

You can, however, embrace uncertainty by adjusting your views as new information becomes available. In order to do that, you need to learn something about Thomas Bayes, an English clergyman, and mathematician who proposed a theorem in 1763 that would forever change the way we think about making decisions in ambiguous conditions.

Bayes was interested in how our beliefs about the world should evolve as we accumulate new but unproven evidence. Specifically, he wondered how he could predict the probability of a future event if he only knew how many times it had occurred, or not, in the past. To answer that, he constructed a thought experiment.

Imagine a billiard table. You put on a blindfold, and your assistant randomly rolls a ball across the table. They take note of where it stops rolling. Your job is to figure out where the ball is. All you can really do at this point is to make a random guess.

Now imagine that you ask your assistant to drop some more balls on the table and tell you whether they stop to the left or right of the first ball. If all the balls stop to the right, what can you say about the position of the first ball? If more balls are thrown, how does this improve your knowledge of the position of the first ball? In fact, throw after throw, you should be able to narrow down the area in which the first ball probably lies. Bayes figured out that even when it comes to uncertain outcomes, we can update our knowledge by incorporating new, relevant information as it becomes available.

Many years later, French mathematician Pierre-Simon Laplace developed Bayes’s idea into a powerful theory, which we now know as the Bayes Theorem. Here is a simple explanation of it.

Beginning with a provisional hypothesis about the world, assign to it an initial probability of that event happening, called the prior probability or simply the prior. After collecting new, relevant evidence, recalculate the probability of the hypothesis in light of the new evidence. This revised probability is called the posterior probability.

You can find evidence of Bayesian thinking throughout modern history, from nineteenth-century French and Russian artillery officers adjusting their cannons to Alan Turing trying to crack the German Enigma codes. Bayes has even influenced the design of machine learning techniques, notably the naive Bayes classifier.

Bayes is relevant to modern leaders because it can help them develop an approach to uncertainty that is less deterministic and more probabilistic. Even when events are determined by an infinitely complex set of factors, probabilistic thinking can help you identify the likeliest outcomes, and so make the best decisions. Viewing the information probabilistically enables you to describe one of many possible outcomes, some more or less likely than others.

One of the key advantages of thinking probabilistically is that it equips you with a more critical perspective to evaluate new data as it becomes available. Data can be imperfect, incomplete, or uncertain. There is often more than one explanation for why things happened the way they did; by examining those alternative explanations using probability, you can gain a better understanding of causality and what is really going on.

Deterministic models produce a single solution that describes the outcome of an experiment given appropriate inputs; in other words, for every possible input, there is a single output. A probabilistic model distributes over all possible solutions and provides some indication of how likely it is that each will, might, or can occur.

The human mind is naturally deterministic. We generally believe that something is true or false. Either you like someone, or you don’t. There is rarely a situation when you can say that there is a 46 percent probability that someone is your friend. In fact, unless you are a teenager and have a lot of frenemies, you are probably quite deterministic about your social circle. Our instinct for determinism may well have been an evolutionary innovation. To survive, we had to make snap judgments about the world and our response to it. When a tiger is approaching you, there is really not a lot of time to consider whether he’s approaching as a friend or a foe.

However, the deterministic approach that kept our ancestors alive while hunting in the savannah won’t help you make good decisions in complex, unpredictable environments when your natural mental shortcuts and heuristics start to fail you. One of the best ways to embrace uncertainty and be more probabilistic in your approach is to learn to think like a professional gambler. Take, for example, Rasmus Ankersen.

Ankersen, a Dane living in London, originally came to the UK to look for an English publisher for his book on human performance, the writing of which had taken him from Kenya to Korea in search of why great athletes, whether they are runners or golfers, tend to come from the same small regions. One of the reasons he decided to stay in London was a chance meeting with a professional gambler named Matthew Benham.

Benham is a renowned, albeit somewhat inaccessible, figure in the British gambling world. After graduating from the University of Oxford with a physics degree, he went into securities trading, first at Yamaichi International and then at the Bank of America. This was followed by a stint working as a trader for Premier Bet with Tony Bloom, one of the most successful gamblers in the world. That inspired Benham to leave his day job and focus on gambling. He went on to start two successful gaming companies, Matchbook, a sports betting exchange community, and Smartodds, which provides statistical research and sports modeling services.

When Ankersen and Benham met, they started talking about how soccer (except, of course, they called it football) was a sport that was yet to be disrupted by data and probabilistic thinking. Benham was impressed enough to invite Ankersen to help run Brentford Football Club, which he had recently acquired. Soon after, Benham also bought Midtjylland, the soccer club in Ankersen’s hometown.

Ankersen’s insight was this: soccer is one of the world’s unfairest sports. Although there is a saying that “the league table never lies,” in Ankersen’s opinion that is exactly what it does. Because soccer is a low-scoring sport, the win/loss outcome of a game is not an accurate representation of the actual performance of a team, and therefore the intrinsic value of its players. From a professional gambler’s perspective, the key to placing a good bet is to continually update your position with relevant insights that impact the probability of an event occurring. Rather than trying to be right, gamblers try to be less wrong with time.

Benham and Ankersen started to use the scientific application of statistics—the “Moneyball” technique pioneered in baseball— when assessing the performance of a team. Their key performance metric became “expected goals” for and against a team, based on the quality and quantity of chances created during a match. The point of this exercise was to develop an alternative league table, which might serve as a more reliable predictor of results and a better basis on which to value and acquire players. As an algorithmic leader, you will also find having a probabilistic mindset useful, and not just when you want to place a bet in the office fantasy soccer competition. Let’s consider a few examples.

A probabilistic HR manager will examine the data about where a company’s best people come from and how they perform throughout their career to identify new sources of talent that may have been overlooked.

A probabilistic sales professional will be conscious that it’s not enough to simply close lots of deals; it’s important to also think about where leads come from. How many opportunities were created organically, as opposed to being fed through an existing pipeline? How many new customers churn after just a few months? By understanding the data around which leads go on to become great customers, a sales leader can then work closely with their marketing colleagues to figure out new sources of potential customer prospects.

Probabilistic risk managers will think about the future of how they work. While their job may have been setting or applying strict credit policies in the past, they may now start to wonder whether their traditional credit rating models are still effective. Are there low-risk segments in their customer base that they may have missed and that a new competitor may be able to target?

Developing a probabilistic mindset allows you to be better prepared for the uncertainties and complexities of the algorithmic age. Even when events are determined by an infinitely complex set of factors, probabilistic thinking can help us identify the most likely outcomes and the best decisions to make.

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This post is by Mike Walsh. Walsh is the author of The Algorithmic Leader: How to Be Smart When Machines Are Smarter Than You from which this article is excerpted. Walsh is the CEO of Tomorrow, a global consultancy on designing companies for the 21 st century.

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Posted by Michael McKinney at 07:16 AM
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