So if you haven’t read it, Joseph Fuller’s The Terminator Comes to Wall Street is a well-written policy-advocating gloss. However, its technical merit is exactly what you’d expect from the PBK pimps whose most significant recent article involved an English professor nicknamed “Cockmaster D” advocating for “erotic intensity” between professor and student. I’ll stick to my problem sets, thanks.
So, anyway, the article is bifurcated nicely between well-meaning policy suggestions (“Executives should take more time to understand what’s going on”) and technical ignorance (“At the cutting edge of modeling science, researchers are trying to move away from relatively crude rules-based models toward models that approximate the processes of human reason.”) The crux of Fuller’s argument is that computers are bad — and they’re bad for three reasons. Briefly:
- Modelers don’t understand markets - especially the psychology of markets.
- Managers don’t understand modelers - and even if they did, computers can easily get too complicated for anyone to understand.
- Models don’t understand each other, especially how they interact with one another.
I’ll move through these in reverse order. Number three is an easy one — you could make an identical argument for human traders. Computers don’t introduce any kind of systemic risk that isn’t already there — we still have a multi-agent system with a bunch of profit-seekers interacting with one another. It’s an unfair standard to insist that computers have to have some kind of global, system-wide knowledge while people can trade ignorantly and unfettered.
Number two is a sound point about better management being needed, but Fuller loses me here:
The problem here goes beyond comprehension. Even if the executives were Quants, they might well not understand as much as they would like about the programs running their businesses. The models themselves—and particularly the interaction among models—has grown so complex that it may have become impossible for any human to fully grasp the types and volumes of derivatives traded in this way or to predict how the models will interact with each other.
This is a strange point to make, because if the models were simple enough to fully grasp, then there really wouldn’t be a point for them in the first place. Put another way, the whole reason you’re using computers operating off of complex relationships is to make something you can’t fully understand.
And Fuller’s first point, about computers not understanding “psychology”, is irrelevant. If real phenomena like trend-following or whatever exist meaningfully, they’ll be found and exploited. Instead, this line of reasoning smacks of an NBA announcer declaring a player who happens to hit a couple jump shots to be “on fire”. It’s people that see patterns and symbols when they’re not actually there, not computers that have trouble detecting regularities.
Ultimately, I think much of Fuller’s argument is based around misconceptions from Classic AI: computers as deterministic automata, blindly and unceasingly following some fixed source code, with human intelligence as the ultimate standard. It sounds like a pre-Moravec’s Paradox view of the world, where anything people find hard must be a “true test” for computers. In reality, it’s entirely appropriate to expect that when there are well-defined objectives and computers have the complete access to the policy state (no falling over!), computers will outperform people. Comparatively, making great trading bots is easy. Opening doors is hard.
