FORECAST: new book to be published tomorrow!

That's right, my new book, the cover of which you've seen off to the right of this blog for some time now, will FINALLY be in bookstores in the US tomorrow, March 26. Of course, it is also available at Amazon and other likely outlets on the web. Who knows when reviews and such will begin trickling in. The book was featured in Nature on Thursday in their "Books in brief" section (sorry, you'll need a subscription), but the poor writers of those reviews (I've been one) really have almost no space to say anything. The review does make very clear that the book exists and purports to have some new ideas about economics and finance, but it makes no judgement on the usefulness of the book at all.

Anyone in the US, if you happen to be in a physical bookstore in the next few days, please let me know if you 1) do find the book and 2) where it was located. I've had the unfortunate experience in the past that my books, such as Ubiquity or The Social Atom, were placed by bookstore managers near the back of the store in sections with labels like Mathematical Sociology or Perspectives in the Philosophy of History, where perhaps only 1 or 2 people venture each day, and then probably only because they got lost while looking for the rest room. If you do find the book in an obscure location, feel completely free -- there's no law against this -- to take all the copies you find and move them up to occupy prominent positions in the bestsellers' section, or next to the check out with the diet books, etc. I would be very grateful!

And I would very much like to hear what readers of this blog think about the book.

Quantum Computing, Finally!! (or maybe not)

Today's New York Times has an article hailing the arrival of superfast practical quantum computers (weird thing pictured above), courtesy of Lockheed Martin who purchased one from a company called D-Wave Systems. As the article notes,

... a powerful new type of computer that is about to be commercially deployed by a major American military contractor is taking computing into the strange, subatomic realm of quantum mechanics. In that infinitesimal neighborhood, common sense logic no longer seems to apply. A one can be a one, or it can be a one and a zero and everything in between — all at the same time. ...  Lockheed Martin — which bought an early version of such a computer from the Canadian company D-Wave Systems two years ago — is confident enough in the technology to upgrade it to commercial scale, becoming the first company to use quantum computing as part of its business.

The article does mention that there are some skeptics. So beware.

Ten to fifteen years ago, I used to write frequently, mostly for New Scientist magazine, about research progress towards quantum computing. For anyone who hasn't read something about this, quantum computing would exploit the peculiar properties of quantum physics to do computation in a totally new way. It could potentially solve some problems very quickly that computers running on classical physics, as today's computers do, would never be able to solve. Without getting into any detail, the essential thing about quantum processes is their ability to explore many paths in parallel, rather than just doing one specific thing, which would give a quantum computer unprecedented processing power. Here's an article giving some basic information about the idea.

I stopped writing about quantum computing because I got bored with it, not the ideas, but the achingly slow progress in bringing the idea into reality. To make a really useful quantum computer you need to harness quantum degrees of freedom, "qubits," in single ions, photons, the spins of atoms, etc., and have the ability to carry out controlled logic operations on them. You would need lots of them, say hundreds and more, to do really valuable calculations, but to date no one has managed to create and control more than about 2 or 3. I wrote several articles a year noting major advances in quantum information storage, in error correction, in ways to transmit quantum information (which is more delicate than classical information) from one place to another and so on. Every article at some point had a weasel phrase like ".... this could be a major step towards practical quantum computing." They weren't. All of this was perfectly good, valuable physics work, but the practical computer receded into the future just as quickly as people made advances towards it. That seems to be true today.... except for one D-Wave Systems.

Around five years ago, this company started claiming that it was producing and achieving quantum computing and had built functioning devices with 128 qubits. It used superconducting technology. Everyone else in the field was aghast by such a claim, given this sudden staggering advance over what anyone else in the world had achieved. Oh, and D-Wave didn't release sufficient information for the claim to be judged. Here is the skeptical judgement of IEEE Spectrum magazine as of 2010. But more up to date, and not quite so negative, is this assessment by quantum information expert Scott Aaronson just over a year ago. The most important point he makes is about the failure of D-Wave to really demonstrate that its computer is really doing something essentially quantum, which is why it would be interesting. This would mean demonstrating so-called quantum entanglement in the machine, or really carrying out some calculation that was so vastly superior to anything achievable by classical computers that one would have to infer quantum performance. Aaronson asks the obvious question:

... rather than constantly adding more qubits and issuing more hard-to-evaluate announcements, while leaving the scientific characterization of its devices in a state of limbo, why doesn’t D-Wave just focus all its efforts on demonstrating entanglement, or otherwise getting stronger evidence for a quantum role in the apparent speedup?  When I put this question to Mohammad Amin, he said that, if D-Wave had followed my suggestion, it would have published some interesting research papers and then gone out of business—since the fundraising pressure is always for more qubits and more dramatic announcements, not for clearer understanding of its systems.  So, let me try to get a message out to the pointy-haired bosses of the world: a single qubit that you understand is better than a thousand qubits that you don’t.  There’s a reason why academic quantum computing groups focus on pushing down decoherence and demonstrating entanglement in 2, 3, or 4 qubits: because that way, at least you know that the qubits are qubits!  Once you’ve shown that the foundation is solid, then you try to scale up.   

So there's a finance and publicity angle here as well as the science. The NYT article doesn't really get into any of the specific claims of D-Wave, but I recommend Aaronson's comments as a good counterpoint to the hype.

Third (and final) excerpt...

The third (and, you'll all be pleased to hear, final!) excerpt of my book was published in Bloomberg today. The title is "Toward a National Weather Forecaster for Finance" and explores (briefly) the topic of what might be possible in economics and finance in creating national (and international) centers devoted to data intensive risk analysis and forecasting of socioeconomic "weather."

Before anyone thinks I'm crazy, let me make very clear that I'm using the term "forecasting" in it's general sense, i.e. of making useful predictions of potential risks as they emerge in specific areas, rather than predictions such as "the stock market will collapse at noon on Thursday." I think we can all agree that the latter kind of prediction is probably impossible (although Didier Sornette wouldn't agree), and certainly would be self-defeating were it made widely known. Weather forecasters make much less specific predictions all the time, for example, of places and times where conditions will be ripe for powerful thunderstorms and tornadoes. These forecasts of potential risks are still valuable, and I see no reason similar kinds of predictions shouldn't be possible in finance and economics. Of course, people make such predictions all the time about financial events already. I'm merely suggesting that with effort and the devotion of considerable resources for collecting and sharing data, and building computational models, we could develop centers acting for the public good to make much better predictions on a more scientific basis.

As a couple of early examples, I'll point to the recent work on complex networks in finance which I've touched on here and here. These are computationally intensive studies demanding excellent data which make it possible to identify systemically important financial institutions (and links between them) more accurately than we have in the past. Much work remains to make this practically useful.

Another example is this recent and really impressive agent based model of the US housing market, which has been used as a "post mortem" experimental tool to ask all kinds of "what if?" questions about the housing bubble and its causes, helping to tease out better understanding on controversial questions. As the authors note, macroeconomists really didn't see the housing market as a likely source of large-scale macroeconomic trouble. This model has made it possible to ask and explore questions that cannot be explored with conventional economic models:

 Not only were the Macroeconomists looking at the wrong markets, they might have been looking at the wrong variables. John Geanakoplos (2003, 2010a, 2010b) has argued that leverage and collateral, not interest rates, drove the economy in the crisis of 2007-2009, pushing housing prices and mortgage securities prices up in the bubble of 2000-2006, then precipitating the crash of 2007. Geanakoplos has also argued that the best way out of the crisis is to write down principal on housing loans that are underwater (see Geanakoplos-Koniak (2008, 2009) and Geanakoplos (2010b)), on the grounds that the loans will not be repaid anyway, and that taking into account foreclosure costs, lenders could get as much or almost as much money back by forgiving part of the loans, especially if stopping foreclosures were to lead to a rebound in housing prices.

There is, however, no shortage of alternative hypotheses and views. Was the bubble caused by low interest rates, irrational exuberance, low lending standards, too much refinancing, people not imagining something, or too much leverage? Leverage is the main variable that went up and down along with housing prices. But how can one rule out the other explanations, or quantify which is more important? What effect would principal forgiveness have on housing prices? How much would that increase (or decrease) losses for investors? How does one quantify the answer to that question?

Conventional economic analysis attempts to answer these kinds of questions by building equilibrium models with a representative agent, or a very small number of representative agents. Regressions are run on aggregate data, like average interest rates or average leverage. The results so far seem mixed. Edward Glaeser, Joshua Gottlieb, and Joseph Gyourko (2010) argue that leverage did not play an important role in the run-up of housing prices from 2000-2006. John Duca, John Muellbauer, and Anthony Murphy (2011), on the other hand, argue that it did. Andrew Haughwout et al (2011) argue that leverage played a pivotal role.

In our view a definitive answer can only be given by an agent-based model, that is, a model in which we try to simulate the behavior of literally every household in the economy. The household sector consists of hundreds of millions of individuals, with tremendous heterogeneity, and a small number of transactions per month. Conventional models cannot accurately calibrate heterogeneity and the role played by the tail of the distribution. ... only after we know what the wealth and income is of each household, and how they make their housing decisions, can we be confident in answering questions like: How many people could afford one house who previously could afford none? Just how many people bought extra houses because they could leverage more easily? How many people spent more because interest rates became lower? Given transactions costs, what expectations could fuel such a demand? Once we answer questions like these, we can resolve the true cause of the housing boom and bust, and what would happen to housing prices if principal were forgiven.

... the agent-based approach brings a new kind of discipline because it uses so much more data. Aside from passing a basic plausibility test (which is crucial in any model), the agent-based approach allows for many more variables to be fit, like vacancy rates, time on market, number of renters versus owners, ownership rates by age, race, wealth, and income, as well as the average housing prices used in standard models. Most importantly, perhaps, one must be able to check that basically the same behavioral parameters work across dozens of different cities. And then at the end, one can do counterfactual reasoning: what would have happened had the Fed kept interest rates high, what would happen with this behavioral rule instead of that.

The real proof is in the doing. Agent-based models have succeeded before in simulating traffic and herding in the flight patterns of geese. But the most convincing evidence is that Wall Street has used agent-based models for over two decades to forecast prepayment rates for tens of millions of individual mortgages.

This is precisely the kind of work I think can be geared up and extended far beyond the housing market, augmented with real time data, and used to make valuable forecasting analyses. It seems to me actually to be the obvious approach.
 

Second excerpt...

A second excerpt of my forthcoming book Forecast is now online at Bloomberg. It's a greatly condensed text assembled from various parts of the book. One interesting exchange in the comments from yesterday's excerpt:

Food For Thought commented....Before concluding that economic theory does not include analysis of unstable equilibria check out the vast published findings on unstable equilibria in the field of International Economics.  Once again we have someone touching on one tiny part of economic theory and drawing overreaching conclusions. 

I would expect a scientist would seek out more evidence before jumping to conclusions.

to which one Jack Harllee replied...

Sure, economists have studied unstable equilibria. But that's not where the profession's heart is. Krugman summarized rather nicely in 1996, and the situation hasn't changed much since then:
"Personally, I consider myself a proud neoclassicist. By this I clearly don't mean that I believe in perfect competition all the way. What I mean is that I prefer, when I can, to make sense of the world using models in which individuals maximize and the interaction of these individuals can be summarized by some concept of equilibrium. The reason I like that kind of model is not that I believe it to be literally true, but that I am intensely aware of the power of maximization-and-equilibrium to organize one's thinking - and I have seen the propensity of those who try to do economics without those organizing devices to produce sheer nonsense when they imagine they are freeing themselves from some confining orthodoxy. ...That said, there are indeed economists who regard maximization and equilibrium as more than useful fictions. They regard them either as literal truths - which I find a bit hard to understand given the reality of daily experience - or as principles so central to economics that one dare not bend them even a little, no matter how useful it might seem to do so."

This response fairly well captures my own position. I argue in the book that the economics profession has been fixated far too strongly on equilibrium models, and much of the time simply assumes the stability of such equilibria without any justification. I certainly don't claim that economists have never considered unstable equilibria (or examined models with multiple equilibria). But any examination of the stability of an equilibrium demands some analysis of dynamics of the system away from equilibrium, and this has not (to say the least) been a strong focus of economic theory.   

New territory for game theory...

This new paper in PLoS looks fascinating. I haven't had time yet to study it in detail, but it appears to make an important demonstration of how, when thinking about human behavior in strategic games, fixed point or mixed strategy Nash equilibria can be far too restrictive and misleading, ruling out much more complex dynamics, which in reality can occur even for rational people playing simple games: 

Abstract

Recent theories from complexity science argue that complex dynamics are ubiquitous in social and economic systems. These claims emerge from the analysis of individually simple agents whose collective behavior is surprisingly complicated. However, economists have argued that iterated reasoning–what you think I think you think–will suppress complex dynamics by stabilizing or accelerating convergence to Nash equilibrium. We report stable and efficient periodic behavior in human groups playing the Mod Game, a multi-player game similar to Rock-Paper-Scissors. The game rewards subjects for thinking exactly one step ahead of others in their group. Groups that play this game exhibit cycles that are inconsistent with any fixed-point solution concept. These cycles are driven by a “hopping” behavior that is consistent with other accounts of iterated reasoning: agents are constrained to about two steps of iterated reasoning and learn an additional one-half step with each session. If higher-order reasoning can be complicit in complex emergent dynamics, then cyclic and chaotic patterns may be endogenous features of real-world social and economic systems.

...and from the conclusions, ...

Cycles in the belief space of learning agents have been predicted for many years, particularly in games with intransitive dominance relations, like Matching Pennies and Rock-Paper-Scissors, but experimentalists have only recently started looking to these dynamics for experimental predictions. This work should function to caution experimentalists of the dangers of treating dynamics as ephemeral deviations from a static solution concept. Periodic behavior in the Mod Game, which is stable and efficient, challenges the preconception that coordination mechanisms must converge on equilibria or other fixed-point solution concepts to be promising for social applications. This behavior also reveals that iterated reasoning and stable high-dimensional dynamics can coexist, challenging recent models whose implementation of sophisticated reasoning implies convergence to a fixed point [13]. Applied to real complex social systems, this work gives credence to recent predictions of chaos in financial market game dynamics [8]. Applied to game learning, our support for cyclic regimes vindicates the general presence of complex attractors, and should help motivate their adoption into the game theorist’s canon of solution concepts

Book excerpt...

Bloomberg is publishing a series of excerpts from my forthcoming book, Forecast, which is now due out in only a few days. The first one was published today.

Secrets of Cyprus...

Just something to think about when scratching your head over the astonishing developments in Cyprus, which seem to be more or less intentionally designed to touch off bank runs in several European nations. Why? Courtesy of Zero Hedge:

...news is now coming out that the Cyprus parliament has postponed the decision and may in fact not be able to reach agreement. They may tinker with the percentages, to penalize smaller savers less (and larger savers more). However, the damage is already done. They have hit their savers with a grievous blow, and this will do irreparable harm to trust and confidence.

As well it should! In more civilized times, there was a long established precedent regarding the capital structure of a bank. Equity holders incur the first losses as they own the upside profits and capital gains. Next come unsecured creditors who are paid a higher interest rate, followed by secured bondholders who are paid a lower interest rate. Depositors are paid the lowest interest rate of all, but are assured to be made whole, even if it means every other class in the capital structure is utterly wiped out.

As caveat to the following paragraph, I acknowledge that I have not read anything definitive yet regarding bondholders. I present my assumptions (which I think are likely correct).

As with the bankruptcy of General Motors in the US, it looks like the rule of law and common sense has been recklessly set aside. The fruit from planting these bitter seeds will be harvested for many years hence. As with GM, political expediency drives pragmatic and ill-considered actions. In Cyprus, bondholders include politically connected banks and sovereign governments.  Bureaucrats decided it would be acceptable to use depositors like sacrificial lambs. The only debate at the moment seems to be how to apportion the damage amongst “rich” and “non-rich” depositors.

Also, much more on the matter here, mostly expressing similar sentiments. And do read The War On Common Sense by Tim Duy:

This weekend, European policymakers opened up a new front in their ongoing war on common sense.  The details of the Cyprus bailout included a bail-in of bank depositors, small and large alike.  As should have been expected, chaos ensued as Cypriots rushed to ATMs in a desperate attempt to withdraw their savings, the initial stages of what is likely to become a run on the nation's banks.  Shocking, I know.  Who could have predicted that the populous would react poorly to an assault on depositors?

Everyone.  Everyone would have predicted this.  Everyone except, apparently, European policymakers....