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| Photo: John Mannes |
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| Photo: TechCrunch |
From hedge funds to venture capital firms, everyone in finance has some idea about how data and quantitative analysis will reshape their industry. Firms like Signal Fire track engineers as they move from company to company to draw attention to growing startups. And funds like Numerai and Quantopian are putting faith in quants to determine optimal trading strategies.
Bridgewater Associates, one of the world’s most robust and reliable money making machines, is going as far as to attempt to automate its internal management processes to ensure the longevity of its $150 billion under management. But unlike most other approaches of applying AI to moneymaking, Bridgewater’s tactic isn’t about anomaly detection, it’s about mechanization.
It was about the people before AI
To understand Bridgewater, you have to understand Ray Dalio. To Dalio, broken frameworks and excess emotion are the enemy. Success comes from a curated set of rules he refers to as Principles in his book of the same title.
The field of behavioral economics is dedicated to studying the myriad of ways that psychology and neuroscience influence decision making. Traditional economics makes basic assumptions about human rationality but research in behavioral economics has shown us that people tend to do very strange things outside the paradigm of homo economicus.
There are hundreds of known cognitive biases — confirmation bias (we often only see info that validates our prior assumptions) , hyperbolic discounting (we’re really poor at valuing things with respect to time) and the bandwagon effect (we attach too much value to herd behavior).
Dalio says that rules help him to notice his biases and account for them. Whenever a conviction he has contrasts with what a computer model says it prompts reflection that can help to settle the dispute and lead to a better outcome.
The key is ensuring that you don’t overcompensate with your own emotions or do something just because a computer instructs you to. No number of algorithms can fully insulate a person from bias but they can aid in discipline and habit formation.
It will be about the people after AI
Decades ago, Dalio says he would write down his criteria for making a trade and then work to see if those criteria could be converted into an algorithm.
“When I think hard I can convert qualitative problems to quantitative problems,” Dalio noted. “I ordered a Cobb salad. If I could slow down I’d write down my criteria for a Cobb salad — qualitative judgment for liking a Cobb salad.”
This expert systems approach is antithetical to today’s conceptions of deep learning whereby a machine learning model is trained on massive quantities of data to produce a conclusion based on inductive reasoning.
“I don’t like the term machine learning because what I’m doing is not learning,” Dalio emphasized.
The distinction might seem petty, but it’s far from it. Many of the machine learning models in use today operate as black boxes — data enters and conclusions are spit out. If you want to ask what drove the model to come to those conclusions, you’d be unable to find any paper trail.
“If a machine comes up with an algorithm and you don’t have a deep understanding of the appropriate cause and effect relationship, than things get very dangerous,” Dalio explained. “If the future is different from the past, you’ll probably crash.”
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Source: TechCrunch
