When collaborative filtering goes wrong. Curiously, this was the only part of the concerto that made it to my Amazon recommendation list.
Constructive Economics is a blog about Computer Science, Artificial Intelligence, Economics, Finance, and Market Design. Here is something like a statement of purpose. It's written by Abe Othman, a PhD student in Carnegie Mellon's Computer Science Department.
August 15, 2010
August 10, 2010
The Three Minds of an Anonymous HFM
A bit strangely, n+1 released a book of a series of extremely lucid conversations with an Anonymous Hedge Fund Manager (HFM) written before, during, and after the 2008 economic crisis. As you might expect from anything bearing the n+1 imprint, Diary of a Very Bad Year is well-written and deep while being approachable. In fact, it reminded me most of David Lipsky’s recent DFW travelogue Although Of Course You End Up Becoming Yourself. Both books are dips into smart minds grappling with events small enough for them to effect but far too large for them to control.
What I thought was interesting was the way Anonymous HFM seemingly juggled three contradictory (or, at the very least, conflicting) perspectives on markets and what was swirling around him. The first perspective is the standard one taught in beginning macroeconomics (and maybe microeconomics?) The world is a place of rational agents and curves constantly shifting towards equilibria, except for that damn stickiness always getting in the way. As Anon HFM says on pages 173 and 174, after the worst of the crisis has passed:
We are going to see a major change in the savings behavior of the American people…That’s good in the long term, but these dynamics are always an issue. Adjusting to that new equilibrium is painful. I don’t think adjusting to that new, real equilibrium is going to be instantaneous…and we may wind up with another recession down the line before things are finally restored to equilibrium.
The focus on equilibrium — and in particular in the restoration of equilibrium after an unexplainable (within the paradigm) shock — is clearly evident.
The second perspective, which only appears briefly, is one of neuroeconomics. As Anon HFM says on pages 85 and 86:
It sounds so crazy, that a huge economy, I mean bricks, and mortar, and steel, works or doesn’t work because a few people have some deficit or excess of neurotransmitters in the brain. It sounds crazy! But that’s exactly what it is…there was a misallocation of resources, because people had too much of that neurotransmitter in their brain, that then caused them to have too little of it, and now all they want are risk-free assets, and that causes the machinery of finance to really shudder to a halt.
This is, I think, the only place in the book this perspective appears and I think it would have been interesting to have it extrapolated at length. The neuroeconomics stuff is possibly the future of the whole discipline, though I have my own doubts about the empirical basis of utility. To me, coming up with a reliable measure of utility seems to involve the construction of a structure that can simulate the brain, making it AI-hard. Then again, it’s not like regular-type economics has any problem being founded on computationally intractable ground.
The final perspective is Minskian — that the principal mover in financial interactions is the retention of present capital into the future, and that this impetus left unchecked and unregulated inevitably spirals into a giant Ponzi scheme. To me, this perspective provides the clearest glimpse into the truth of what happened. It is interesting that this is really the last explanation Anon HFM comes up with, at the very end of the book. Here he is in the “Farewell” section, on page 233:
[The] machine doesn’t work unless it’s expanding. In a sense it’s a bit of a Ponzi scheme. I mean, a lot of economics has the dynamic of a Ponzi scheme — it only really works when you’re expanding. Once it goes into reverse, then you get the experience that we had in 2008.
Just as a postscript, for such an intriguing trip the book ends on a sour note. Anon HFM moves from NYC to Austin to avoid paying state income tax, which just strikes me as silly and venal.
August 2, 2010
Horseflesh and Hypocrisy
T. Boone Pickens, from his autobiography The Luckiest Guy in the World:
I believe the greatest opportunity lies in a free marketplace. There are powerful forces afoot trying to restrict that freedom in the interests of the vested and already wealthy.
T. Boone Pickens, in congressional testimony on a bill to prevent the slaughter of horses for food:
The whole thing, it’s a boondoggle on the American people…People that are for the slaughter should be forced to go down on that kill floor…The brutal slaughter of horses for consumption by wealthy diners in Europe and Japan cuts against our moral and cultural fiber — it’s just plain un-American.
Remember, if they can come after the horse slaughterers, they can come after the hedge funds. So if you really believe in free markets, have some horse today!
July 23, 2010
Sarah Connor Comes to Wall Street
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.
July 19, 2010
Should There Be a AAAI?
One of the (very valid) complaints about how I determined the best AI school was that I only looked at publications from AAAI/IJCAI. Each of the major subfields of AI have their own particular conferences that people will often prefer to send work to over AAAI. ML, probably the most dynamic and exciting part of today’s AI landscape, is better served by NIPS and ICML. Robotics, by ICRA. I’ve heard the case made that agent stuff is better at AAMAS.
So, what’s the point of AAAI? Arguments about fostering interdisciplinary work go out the window when there are a dozen parallel tracks. What we call AI is so vast and tenuous that interdisciplinary work might be a fool’s errand anyway. Furthermore, why attend a track that just has “pretty good” work in robotics?
The only thing I can think of in AAAI’s favor is that it’s important (for job searches, &c.) that there’s a recognizable AI conference that everyone knows is “good”. But if that ML paper was so important, why didn’t you publish it at NIPS?
July 9, 2010
The Real Money HSX is Bad Policy
Recently the HSX, a longtime favorite of prediction market advocates, was approved to start trading in real-money derivatives. The HSX using artificial currency has been used for many years to correctly estimate box-office receipts among a diehard but non-professional crowd; along with wikipedia, I think it’s one of the best examples of how “the crowd” can perform excellently without any financial inducement.
The chief mover behind the change was Cantor Fitzgerald, the new owners of the HSX. Cantor seems to be using its cachet as one of the largest 9/11 victims to push the limits of the “gambling versus trading” debate. I think Cantor’s motives here are transparent — they want to be playing bookie, and ideally exclusive bookie, to as many markets as possible. It doesn’t matter to Cantor whether those markets are on government debt, sports betting, or movie ticket sales.
The largest obstacle in moving to real money was the approval of the CFTC — a screening process by real adults who wear suits. There’s quite a bit of information on the HSX proposal scattered around the CFTC website, including all sorts of briefs and drafts and reports. Of all the stuff of it I’ve read, the MPAA brief against the real-money HSX made the most sense to me. Their two points that struck hardest for me were
The contracts will not be used for hedging. The most prominent story about why these contracts should be traded is that certain parties (mostly the studios but also the actors and movie houses, I suppose) have exposure risk to the success or failure of a film. Of course, on any contract you can always construct a theoretical hedger. Even for betting on a coin flip or a dice game; maybe someone has a substantial psychological revulsion to seeing George Washington’s face or the number four. However, the MPAA argues that the parties with material hedges on these events will not participate in their trade. I think given their status as an industry trade group this is a persuasive argument. The MPAA further alleges that because these contracts will not be used for hedging, they will only be used for gambling, and are therefore in violation of sundry statutes scattered about the states.
The data the contract settle on are manipulable. The data used to settle the contracts explicitly state that they are not intended for use in settling financial contracts. This is ultimately because they’re estimates based on assumptions, not a nitty-gritty verified and audited source.
Ironically, the one argument I don’t buy at all in the MPAA’s brief is that the HSX will impact the film-making industry at all. At worst, the markets will fizzle and have no impact, and at best they could provide free exposure for films. Then again, the MPAA doesn’t exactly have the best track record with knowing what’s good or bad for it.
Now, this all brackets the issue of whether the real-money HSX will actually work. I view it as very unlikely that the trading volume on these markets will come anywhere close to a significant fraction of movie revenue, and I view it as even more absurd that the HSX could somehow provide and IPO-style market for financing filmmaking outside of the traditional studio system.
And what we seem to be coming to, then, is a fundamental existential question. I ultimately believe that a real-money HSX is unnecessary. These markets are supposed to provide a way to hedge risk on people seeing films (of course, shouldering such calculated risks should be the whole purpose of a studio in the first place, but regardless…), but if I were a studio with exposure risk on box office returns I would shop that risk around to creative hedge funds with good models willing to take an equity gamble. The two big things a market provides, consolidation and anonymity, are just not necessary in this scenario. On every contract there’s just one movie and one seller of exposure risk.
Given that I’ve done some research in (and implementation of) prediction markets, my position might be a surprising one. Ultimately though, I think that speculative trade should always be secondary to real concerns; speculative trade for the sake of speculative trade is silly and non-productive at best and systemically dangerous at worst. The HSX decision is a bad policy and a worse precedent.
July 2, 2010
Bets vs. Hedges, revisited
From the Daily Finance article on Cantor Fitzgerald becoming a bookie:
(Cantor Fitzgerald employee Andrew) Garrood seems less concerned. He makes the in-running operation sound easy for a company like Cantor, which he says handles $150 trillion in Treasury business each year. “This is an application of what Cantor has always understood,” he says. “It’s just dressing up the emperor in new clothes.”
He wouldn’t dare be implying that the finance industry exists to facilitate betting, would he?
June 29, 2010
Two EC Papers I Enjoyed
EC 2010 (nominally stands for “Electronic Commerce”) was recently hosted by a dedicated team at Harvard. I had a great time as an undergrad there and it was nice to be back for a few days, revisiting places like Felipe’s and People’s Republik. The conference itself took place in the skylighted underground piazza of the Northwest Science Labs, which was really the ideal space for a small conference. The building itself is an enduring symbol of the Summers era at Harvard — shiny, science-focused, and intrinsically endowed with the hubris that comes from constructing such a costly structure without even bothering to secure a naming donation.
My favorite talk was from Ramesh Johari for “Congestible services and network effects” (joint work with Sunil Kumar). Unfortunately, the paper does not seem to be available openly online and even the ACM Digital Library copy is just a one-page “extended abstract” (UPDATE: the paper is available here). Despite the awfully throwaway title, the work is a clever analysis of two opposing factors in networks: network effects, people benefiting from the presence of others (think Facebook), versus congestion effects, where your utility decreases as more people use the service (think traffic). I disliked the use of Nash equilibrium in the paper, but from what I could tell it looked like these were potential games and so simple dynamics should reinforce these equilibria. A robust model drawn in broad strokes and with qualitative insights — to me, the ideal theory paper.
The other talk I particularly enjoyed was from Sharad Goel for his paper (with Dan Reeves, Duncan Watts, and Dave Pennock) “Prediction Without Markets”, an analysis of just how well prediction markets do their job by comparing against alternatives. I just really liked how much data went into this project — the authors evaluated so many distinct datasets and went into a good deal of depth to contrast various predictive techniques. I thought the prediction vs. probability graphs with circles to reflect the number of samples were a clever way to present a complicated data analysis cleanly.
June 27, 2010
June 24, 2010
Flash Crash Analysis
The language is a bit hyperbolic and unscientific, but it’s certainly an interesting dataset.
I’ve been busy; real posts will return soon.
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