Understanding Artificial Intelligence by Stephen Duneier
Artificial intelligence and machine learning is suddenly all the rage, and for good reason. It is the future of this, and every other industry. If you’ve been paying attention to the evolution of technology over the past 2.6 million years, you knew it was coming. Wherever the bulk of the effort has been shouldered by human beings, we have always sought to replace us with technology that could do the job better, faster, more efficiently and, since the invention of capital, cheaper. It began with the most basic, brute force physical tasks and has progressively involved more nuanced, cognitive processes. Along the way, the progress has been exponential, not linear. Through AI and machine learning, technology is now attempting to improve on how we make decisions, and truth is, it won’t require much effort. Not because technology represents some sort of miracle, but because we do such a poor job of it. More than 60 years of research in descriptive decision theory (the field of study that explores how we actually make decisions) provides an abundance of evidence proving that humans are prone to mistakes at every stage of the decision making process. From defining the problem to be solved, to researching and predicting the relevant external factors that will likely affect our ability to achieve them, as well as assessing and implementing the actions we should take in order to improve our chances of realizing those objectives, we are vulnerable to systematic errors in judgment. Luckily, technology can help us overcome our shortcomings, but it will not come without a price, and like the hunters, gatherers, cotton pickers, and factory workers who came before us, we who make a living in finance will be called on to pay it. And likely, sooner than you think.
To conceptualize how this will work, let’s simplify all financial markets down to the game of poker. As it is with all others, there are two elements at work in the game, skill and luck. No player can purposefully capitalize on luck, leaving skill to separate the good players from the bad. In the good old days of the game, skilled players extracted value by bluffing and reading the actions of the other players. Experienced players identified basic behavior patterns (“tells”) exhibited by less accomplished opponents, and sought to take advantage of them. In other words, they were capitalizing on the mistakes of their opponents.
Then the “geeks” got involved, playing poker after work for hours, before convening at local bars to dissect the action until dawn. Over time, they had distilled the game down to what it is, a game of chance where the odds can be calculated and updated every step of the way. With the probability of every possible outcome being calculable, every decision along the way could be scripted according to a set of predetermined rules (see chart). When the number crunchers began appearing at the big tournaments, they paid less attention to the other players than to the probabilities. Very quickly, the old style of play and those who practiced it, were confined to the opening rounds of the major poker tournaments, essentially serving to bankroll an ever increasing purse for the number crunchers.
As good as they are, however, the new breed of poker players are still human. No matter how much they value the power of probabilities, occasionally they succumb to ego, emotion, fear, greed and other bias (mistake) inducing issues, not to mention the limited processing power of the human brain. In other words, as much as this new breed has improved on the old approach by reducing the number of mistakes they make, in the end they remain human. And by human, I mean flawed. In order to remove the potential for these flaws to occur, one of the six players at a poker table decides to bankroll his Cray supercomputer. He programs it to make decisions based on the probabilities of the game, while also incorporating information it gathers in real-time regarding facial ticks, posture, body heat, heartbeat, chemical levels and other physical embodiments of stress and excitement, while analyzing their correlation to betting behavior and the cards those players are holding relative to all of the others. You can imagine the advantage the computer has over the rest of the players, but let’s take a moment to truly understand what that advantage really is.
The computer has a competitive advantage, not because it does things better than the other players, but because it does them with fewer mistakes. It’s edge requires that there are players at the table who are making decisions in a way that is different from how they should be made, while simultaneously Cray makes them as they should be. After seeing the game’s pot gravitating toward Cray and away from the humans, one of the other players decides to bankroll his own supercomputer, leaving 2 computers and 4 humans at the table. The gains for the computers are limited to what the humans have left, meaning the potential for Cray #1 has just been reduced, because future earnings will be split between it and Cray #2.
Eventually, all of the players choose to step out of the game, and instead replace themselves with Cray supercomputers of their own. At that moment, every player (computer) is making decisions based solely on the probabilities. No more “reads” of the players are possible. The outcome is now exclusively a function of chance. Skill plays no role whatsoever. Think about that for a moment. When the humans were removed from the game, so too were mistakes. Without the mistakes, skill no longer plays a role. The game is reduced to purely one of chance or luck, like betting on the roll of fair dice. In other words, in order for one player to have a competitive advantage attributable to skill, at least one of the other players must be making mistakes. That mistake is what makes skill possible. Take away the mistake, and all that is left is luck.
This is the nature of improvement in general. While we tend to think of improvement as additive, the reality is that it is reductive. All gains are a function of efficiency, which are reductive. In order to improve speed, you must reduce inefficient, wasteful movement. Ever wonder what someone coaching an athlete who competes in the 50 yard dash could possibly offer, day in and day out? The answer is, suggestions for making fewer mistakes. If you move your elbow out to the side even a millimeter, it forces an adjustment in another part of your body to keep you both upright and moving forward. So, keep the elbow flowing in the correct direction, it makes you faster. Reduce friction between your legs, between your skin and the air. Reduce your weight. Reduce your need to breath harder. Gains are all a function of reduction of mistakes. When one runs faster than anyone else, we define them as “superhuman”. Therefore, being superhuman means free of mistakes.
Imagine for a moment, one of the players who bankrolls a Cray, decides that he can improve on the computer’s performance by overriding it when he senses an opportunity that the computer isn’t programmed to capitalize on, or perhaps turns it off for a bit when the machine is in the midst of a bad run. While either of these decisions may appeal to our intuition (because we have been taught to take action like this), they are a weakness. We might rationalize the action by saying this is a combination of “art” and science, but when you do, what you are really saying is that this is a combination of decisions driven by unfounded beliefs and those supported by evidence.
This applies to markets as much as it does to a poker game. What skilled managers are capitalizing on are the mistakes of others. Remove humans from the markets, and you remove the ability to create a competitive edge. Skill ceases to exist. Everything we do at Bija, whether it’s delivered via publications like Seeds or in coaching sessions, is designed to help decision makers close the gap between normative decisions (how they should be made, how computers make them) and descriptive decisions (how humans make them), by reducing the potential for mistakes. In other words, the objective is to make human intelligence behave more like artificial intelligence. Remember, if you can’t spot the sucker at the table, it’s probably you.
“Luck may or may not smile on us, but if we stick to a good process for making decisions, then we can learn to accept the outcomes of our decisions with equanimity.” The Success Equation by Michael Mauboussin
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About the Author For nearly thirty years, Stephen Duneier has applied cognitive science to investment and business management. The result has been the turnaround of numerous institutional trading businesses, career best returns for experienced portfolio managers who have adopted his methods, the development of a $1.25 billion dollar hedge fund and 20.3% average annualized returns as a global macro portfolio manager.
Mr. Duneier teaches graduate courses on Decision Analysis and Behavioral Investing in the College of Engineering at the University of California. His book, AlphaBrain, is due to be published in October 2017 (Wiley & Sons).
Through Bija Advisors' coaching, workshops and publications, he helps the world's most successful and experienced investment managers improve performance by applying proven, proprietary decision-making methods to their own processes.
Stephen Duneier was formerly Global Head of Currency Option Trading at Bank of America, Managing Director in charge of Emerging Markets at AIG International and founding partner of award winning hedge funds, Grant Capital Partners and Bija Capital Management. As a speaker, Stephen has delivered informative and inspirational talks to audiences around the world for more than 20 years on topics including global macro economic themes, how cognitive science can improve performance and the keys to living a more deliberate life. Each is delivered via highly entertaining stories that inevitably lead to further conversation, and ultimately, better results.
His artwork has been featured in international publications and on television programs around the world, is represented by the renowned gallery, Sullivan Goss and earned him more than 60,000 followers across social media. As Commissioner of the League of Professional Educators, Duneier is using cognitive science to alter the landscape of American K-12 education. He received his master's degree in finance and economics from New York University's Stern School of Business.
Bija Advisors LLC In publishing research, Bija Advisors LLC is not soliciting any action based upon it. Bija Advisors LLC’s publications contain material based upon publicly available information, obtained from sources that we consider reliable. However, Bija Advisors LLC does not represent that it is accurate and it should not be relied on as such. Opinions expressed are current opinions as of the date appearing on Bija Advisors LLC’s publications only. All forecasts and statements about the future, even if presented as fact, should be treated as judgments, and neither Bija Advisors LLC nor its partners can be held responsible for any failure of those judgments to prove accurate. It should be assumed that, from time to time, Bija Advisors LLC and its partners will hold investments in securities and other positions, in equity, bond, currency and commodities markets, from which they will benefit if the forecasts and judgments about the future presented in this document do prove to be accurate. Bija Advisors LLC is not liable for any loss or damage resulting from the use of its product.
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