The future of economics?

Ali Wyne at the blog "big think" asked eight notable young (under 40) economists about the future of their profession and key topics for future research. Their responses make for interesting but not really surprising reading. The research frontier, in their eyes, faces its key challenges in 1) understanding the nature of economic development and growth, and how the world's poor can be brought out of poverty, 2) learning how our growing understanding of human behavioral psychology can be used to replace the inadequate framework of rationality in economics, 3) gaining a much better perspective on macroeconomics, including bubble and herding phenomena, 4) building a new theoretical perspective to handle the vast influence of new information technology on human economic decisions and 5) learning to deal with massive data.

All in all, these seem like worthwhile goals and I'm encouraged that at least two of the economists make semi-explicit their view that economics dearly needs to explore new kinds of models going beyond the equilibrium framework.

I'm also struck, however, by something a little more depressing, which is the rather narrow, conservative scope expressed in their comments. Perhaps this is to be expected from young economists hoping to find stable jobs for coming decades, but not one of them even mentions the need for a deeper understanding of the nature and long term consequences of economic growth. Such growth is -- still -- simply assumed to be an absolute good to be pursued always and as rapidly as it can be. Given the alarming picture painted by studies such as this one -- in Nature a few months ago, it reviewed how human economic growth has significantly altered virtually all global biological and geophysical processes -- you might think that young economists would be scrambling to develop ideas about human society in a post-growth world, or at least one in which growth has to be strongly constrained and managed.

That seems to be a step too far. The idea of growth forever, unconstrained by any physical laws or biological realities, still seems to be a core belief even of the next generation of economists.

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Stability through simplicity

I gave a talk last week at Oppenheimer Funds in NYC. I met some great people there, really creative and open minded. I spoke on the general theme of this blog -- natural instabilities in finance and economics and ideas we need to understand them.

One question asked afterwards was "what can we do as far as regulations to cope with these kinds of instabilities?" As I recall, my answer was pretty lame. I tried to suggest (vaguely) that the answer probably lies not with highly complex regulations, but with simpler ones, but I didn't say much more. I've been thinking about that point since, and thought it might be worth writing a few things down.

Essentially, the first lesson I think we should draw, once we acknowledge the existence of pervasive instabilities in finance, is the need to deal with persisting uncertainty. We will never understand the terrain so well that we can reduce the future to a set of known possibilities to which we can assign specific probabilities (which standard economics assumes we can). We can and should work hard to explore the space of what might happen, and so gain some forewarning of dangers, but we will still encounter surprises and we should expect to do so. So our approach to regulation ought to be centered on that premise -- that we face a world of uncertainty.

I should have read Andrew Haldane's wonderful essay The Dog and the Frisbee before giving my talk, but I only got around to that this morning. He makes some hugely important points on this very topic. The essay is one extended argument for why financial regulation is now too complex, and why our best hope at achieving financial stability in the future probably lies in a vast simplification of the regulatory apparatus and system of rules.

He begins from the observation that, in many settings where decision making involves weighing up many conflicting factors, simple rules often out-perform more complex ones:

Among physicians diagnosing heart attacks, simple decision trees beat a complex model. Among detectives locating serial criminals, simple locational rules trump complex psychological profiling. Among investors picking stocks, simple passive strategies outperform complex active ones. And among shopkeepers understanding spending patterns, repeat purchase data out-predict complex models.

The general message here is that the more complex the environment, the greater the perils of complex control. The optimal response to a complex environment is often not a fully state-contingent rule. Rather, it is to simplify and streamline (Gigerenzer (2010)). In complex environments, decision rules based on one, or a few, good reasons can trump sophisticated alternatives. Less may be more.

In complex environments, tallying strategies have been found to be superior to risk-weighted alternatives. Take avalanche prediction. Avalanches are difficult to predict, as they are drawn from a fat-tailed (Power Law) distribution. Yet simple tallying of a small number of avalanche indicators has been found capable of predicting over 90% of historical accidents. It has also been found to be superior to more complex decision methods (McCammon and Hägeli (2007)).

Haldane goes on at length to consider this in the context of financial regulation, where the legal framework has really exploded in complexity over the past few decades. The pursuit of ever-more complex models to assess risks, whether used by regulators or by banks and financial institutions independently, has led to a proliferation of models that create an overwhelming fog of complexity and can blind us all to obvious risks:

During the 1990s, the bluntness of the risk judgements embodied in Basel I came increasingly to be questioned – and arbitraged. Basel I was perceived as lacking risk-sensitivity, at least by comparison with the new wave of credit and market risk models emerging at the time. Change came in 1996 with the Market Risk Amendment. This introduced the concept of the regulatory trading book and, for the first time, allowed banks to use internal models to calculate regulatory capital against market risk. ...With hindsight, a regulatory rubicon had been crossed. This was not so much the use of risk models as the blurring of the distinction between commercial and regulatory risk judgements. The acceptance of banks’ own models meant the baton had been passed. The regulatory backstop had been lifted, replaced by a complex, commercial judgement. The Basel regime became, if not self-regulating, then self-calibrating.

The ink was barely dry on Basel II when the financial crisis struck. This exposed gaping holes in the agreement. In the period since, the response has been to fill the largest of these gaps, with large upwards revisions to the calibration of the Basel framework. Agreement on this revised framework, Basel III, was reached in 2010. In line with historical trends the documents making up Basel III added up to 616 pages, almost double Basel II. ... The length of the Basel rulebook, if anything, understates its complexity. The move to internal models, and from broad asset classes to individual loan exposures, has resulted in a ballooning in the number of estimated risk weights. For a large, complex bank, this has meant a rise in the number of calculations required from single figures a generation ago to several million today (Haldane (2011)).

Taking all of this together, the parameter space of a large bank’s banking and trading books could easily run to several millions. These parameters are typically estimated from limited past samples. For example, a typical credit risk model might comprise 20-30 years of sample data – barely a crisis cycle. A market risk model might comprise less than five years of data – far less than a crisis cycle.

Viewed over an historical sweep, this pattern is even more striking. Contrast the legislative responses in the US to the two largest financial crises of the past century – the Great Depression and the Great Recession. The single most important legislative response to the Great Depression was the Glass-Steagall Act of 1933. Indeed, this may have been the single most influential piece of financial legislation of the 20th century. Yet it ran to a mere 37 pages. The legislative response to this time’s crisis, culminating in the Dodd-Frank Act of 2010, could not have been more different. On its own, the Act runs to 848 pages – more than 20 Glass-Steagalls. That is just the starting point. For implementation, Dodd-Frank requires an additional almost 400 pieces of detailed rule-making by a variety of US regulatory agencies.

Haldane ends the essay with some exploration of how we might reverse this trend, and manage to simplify regulations. I won't go into detail other than to second his suggestion that reducing the complexity of the financial system itself ought to be a principle target of such simplified regulations. But even before that, changing the mindset of financial economics is the first task. That mindset still remains fixated on the endless pursuit of optimal strategies by long calculations over risk weighted alternatives, when in reality we rarely know the risks or even the alternatives with much accuracy.

An important consequence of thinking in this world of known risks and optimal solutions is that we end up accepting some dubious arguments that we can achieve the best of all possible worlds with the right pricing mechanism. With bad assumptions, in other words, we fall into the trap of believing the economists' standard models, when they really have little to do with the real world. If we break free of this illusion, and face up to living in a world with real uncertainty, we may return to an era in which old-style regulations and prohibitions against certain activities make perfect sense, and dreams of perfect pricing mechanisms become evident as the fantasies they are:

Over the past 30 years or so, the regulatory direction of travel has been towards pricing risk in the financial system, rather than prohibiting or restricting it. In the language of Weitzman, regulators have pursued price over quantity-based regulation (Weitzman (1974)). That makes sense when optimising in a risky world.

It may make less sense when optimising in an uncertain world. Quantity-based restrictions may be more robust to mis-calibration. Simple, quantity-based restrictions are the equivalent of a regulatory commandment: “Thou shalt not”. These are likely to be less fallible than: “Thou shalt provided the internal model is correct”. That is one reason why Glass-Steagall lasted for 60 years longer than Basel II. Quantity-based regulatory solutions have gained currency during the course of the crisis. In the US, the Volcker rule is a quantity-based regulatory commandment: “Thou shalt not engage in proprietary trading”. In the UK, the Independent (“Vickers”) Commission on Banking has also proposed structural, quantity-based reforms: “Thou shalt not co-mingle retail deposit-taking and investment banking”.

Yet even these notionally simple, structural proposals run some risk of backdoor complexity. For example, the consultation document accompanying Volcker already runs to 298 pages. Were these proposals to become mired in detail, they risk sinking, like the Tower of Basel, into the swamp. This is not because these proposals go too far but because they may not go far enough. These reform efforts have too many commas, semi-colons and sub-clauses. They would benefit from a few more full stops.

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wisdom of crowds

My next Bloomberg column comes out this weekend is now out here. I wanted to give readers some further detail on the experiments I wrote about in the column, experiments designed to test how social influence affects the Wisdom of Crowds phenomenon. I actually wrote about the experiments in this post last year, and that post gives quite a lot of detail.

I think it is the most illuminating set of experiments I have seen on this phenomenon. Most important in the current environment, it's pretty clear I think that one can't look to the wisdom of crowds as a mechanism to enforce any kind of "wisdom" on the part of the financial markets. (This doesn't mean they're always wrong either, of course.) 

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Seeking "ivansml"

I'm finishing off my forthcoming book Forecast: What Extreme Weather Can Teach Us About Economics and have today been going over page proofs (extreme tedium...fixing typos etc). An important matter: I've referred in the book to some work on learning in macroeconomics (Evans and someone else) that was suggested to me by "ivansml", a graduate student at some European institution. Ivan: can you email me (buchanan.mark@gmail.com)? I would like to point out in a footnote that you directed me to this work. I can do this either using "ivansml" and referring to this site, or by using your real name (which I would rather do). THANKS!

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Bubbles

I just stumbled on this post from a few months back by Noah Smith. Like all his stuff it is a fun and informative read. Essentially, he looks back to the famous experimental paper of Vernon Smith and colleagues which found clear evidence for strong and sustained bubbles in an artificial market with students trading a fictitious asset with real value. The novelty of the experiment was that this asset had a clear and perfectly well-known fundamental value (unlike real financial instruments), and so it was easy to see that the market value at first soared way above the fundamental value, and then crashed down again.

Noah's post looks at why this result, for financial economists, didn't nail the proof that asset bubbles can exist and ought to be expected in real markets. Most of the arguments seem to be centered on the idea that the people acting in real markets are far more sophisticated than those students, and so would never pay more than the true fundamental value for anything. Suffice it to say this argument doesn't hold together at all well in the face of empirical evidence on real trading behavior, some of which Noah reviews.

One thing caught my eye, however, and is worth a short mention. As Noah writes...

If bubbles represent the best available estimate of fundamental values, then they aren't something we should try to stop. But many other people think that bubbles are something more sinister - large-scale departures of prices from the best available estimate of fundamentals. If bubbles really represent market inefficiencies on a vast scale, then there's a chance we could prevent or halt them, either through better design of financial markets, or by direct government intervention.

 I am certainly someone of the latter camp -- convinced that markets often depart from fundamentals (even such values even exist) for long periods of time. But I think the third sentence on what we might do about bubbles needs to be refined a little from a logical point of view.

Bubbles aren't necessarily totally bad things. Perhaps we may find that they are a useful and necessary part of the collective learning process. The foraging of a flock of birds is highly irregular; it moves this way and that, following the lead of different birds at different times, sometimes moving on large excursions in a single direction. A market might be somewhat similar as a collective social process for searching and exploring. We shouldn't expect that what it has found at any one moment is optimal; it may often make huge mistakes. But the process of exploration may be useful anyway.

In that case, we may find that we don't want to stamp out bubbles, unless they get really big. Or if the bubble is one driven by a systematic increase of leverage by investors which sets the stage for a certain explosive episode of de-leveraging, with subsequent long term consequences.

What we do want to stamp out, however, is the dangerous idea (still supported by many economists) that bubbles don't exist. That's the one idea that can make our markets really prone to disasters.

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Optimism vs Pessimism

As I mentioned at the end of my post yesterday, many of the comments on my recent Bloomberg column chided me for being overly optimistic about the future of humanity, and especially about our capacity to create a sustainable future, especially through the intelligent use of technology to help us control and manage a complex world. The criticism was elegantly put by David Johnson:

I just saw your piece on Bloomberg on augmenting our decision making skills artificially, and I am sorry to say, based on quite a bit of painful experience, that this doesn't actually work as one might hope.

I'm retired now, but I spent nearly thirty years as a computer software designer, and I can't tell you how many times I have seen people flatly refuse to believe counter-intuitive results coming from some sophisticated program. 

Indeed, even simple instruments, such as pressure gauges can present results that cause system operators to dismiss the data as the output of a defective sensor. 

For example, the accident at Three Mile Island was the direct result of operators misjudging the meaning of two gauges that were apparently giving contradictory readings.  One gauge implied that the level of cooling water was getting too high, while the other implied that it was dangerously low.  The operators could not envision any scenario in which both could be correct, so they decided (arbitrarily and without ANY cross-checking!) that the low reading was invalid, and they shut down the emergency cooling water, which was precisely the wrong thing to do.

In that case, it turned out that there was a vapor lock in the plumbing connecting the two parts of the system that the two gauges were monitoring, so, indeed, the pressure in the cooling water supply was rising, even as the water level in the reactor vessel itself was dropping dangerously.  However, as simple as this problem really was, it was totally outside the experience of the operators, so they never considered the possibility.  Moreover, the system designers had not recognized the possibility either, or they would have designed the plumbing differently in the first place.

My point here is that a problem of this sort is stupidly simple compared to the complexities of systems like the global climate, yet even trained professionals cannot handle the level of weirdness that can result from one unanticipated discrepancy.

In other words, we are generally stupid enough that we cannot understand, much less accept, how stupid we really are, so there is no way that the average person will casually defer to the judgment of an artificial system like a computer program.

I received many comments making similar points, and I'd like to say that I agree completely and absolutely.

The way I look at the argument of Sander van der Leeuw is that he has identified a weak point in the nature of our relationship with the world. Our brains individually and collectively simply cannot match up to the complexity of the world in which we live (especially as our own technology has made it much more complex in recent decades). It's this mismatch that lies behind the pervasive tendency for our actions and innovations to have unanticipated consequences, many of which have led us to very big problems. Hence, he's suggesting that IF WE HAVE ANY HOPE of finding some solutions to our problems through further innovation it will be by finding ways to help our brains cope more effectively. He suggests information technology as the one kind of technology that might be useful in this regard, and which might help -- again, if used properly -- to heal the divide between the real complexity of the world and our pictures and models of it.

I think this makes a lot of sense, and it ought to inform our future use of technology and the way we use it to innovate. But I certainly wouldn't want to go any further and predict that we will actually be able to act in this way, or learn from this insight. If asked to bet on it, I would actually bet that humanity will have to suffer dearly and catastrophically before we ever change our ways.

Even more than stupid, we are stubborn. On this point I also could not agree more with David Johnson:

Seriously, the Arctic ice cap has been more or less stable, within a few percent, for about three million years, but now, in just thirty years, about 75% of the mass of ice has disappeared.  Yet, millions of people simply ignore this massive and extremely dangerous change.  Instead, they chalk up the reports as evidence of a conspiracy by climate scientists to frighten taxpayers into supporting more fictitious make-work for those self-same scientists.  That is a lethal level of stupidity, but it still passes easily for "common-sense" among a very large fraction of the general population.

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Archeology of Innovation

My last Bloomberg column appeared a few days ago. Anyone interested can read it here. I wanted to bring some attention to what I think is a truly profound argument being made by anthropologist Sander van der Leeuw of Arizona State University on the nature of innovation (technological, social, or otherwise).

Obviously, humans excel at innovation and this is what probably accounts for our great (rampant) success as a species. This innovation has also brought us to the brink of catastrophe. A recent study published in Nature concluded that the next few generations should expect "a sharp reduction in biodiversity and severe impacts on much of what we depend on to sustain our quality of life, including fisheries, agriculture, forest products and clean water." This is also the outcome of our innovation, which is a double-edged sword. A deep question is WHY? Why is our innovation like this, always (it seems) leading to unintended consequences?

My Bloomberg column gives the basics of the argument, but I strongly recommend reading van der Leeuw's paper, "The Archeology of Innovation," for the full picture. It's a fun, mind-expanding paper in an informal style, and what really makes it unique is that it looks at human evolutionary history (over the past 50,000 years or so) through the lens of information and information processing. This is a novel idea, and especially novel given our current information revolution. The paper argues that most of the fundamental transitions in human history -- including the agricultural and industrial revolutions -- were essentially revolutions in which we learned to use information in a new way. As he notes, 

... the current emphasis in certain quarters on our present-day society as the ‘information society’ is misguided—every society since the beginning of human evolution has been an ‘information society.’

Learning from the past is of course a good way to see what might happen in the future.

But the most interesting part of his argument, for me, concerns what past patterns might imply for the future of humanity and our ability to overcome our current global challenges. He essentially suggests that we need to think very carefully about how we innovate, rather than do it recklessly with more or less blind hope (as we do today, encouraged by short-sighted economic return). I'll just give a few short segments:

Human cognition, powerful as it may have become in dealing with the environment, is only one side of the (asymmetric) interaction between people and their environment, the one in which the perception of the multidimensional external world is reduced to a very limited number of dimensions. The other side of that interaction is human action on the environment, and the relationship between cognition and action is exactly what makes the gap between our needs and our capabilities so dramatic.

The crucial concept here is that of ‘unforeseen’ or ‘unanticipated’ consequences. It refers to the well-known and oft-observed fact that, no matter how careful one is in designing human interventions in the environment, the outcome is never what it was intended to be. It seems to me that this phenomenon is due to the fact that every human action upon the environment modifies the latter in many more ways that its human actors perceive, simply because the dimensionality of the environment is much higher than can be captured by the human mind. In practice, this may be seen to play out in every instance where humans have interacted in a particular way with their environment for a long time—in each such instance, ultimately the environment becomes so degraded from the perspective of the people involved that they either move to another place or change the way they are interacting with the environment. 

Ultimately, this necessarily leads to ‘time-bombs’ or ‘crises’ in which so many unknowns emerge that the society risks being overwhelmed by the number of challenges it has to face simultaneously. It will initially deal with this by innovating faster and faster, as our society has done for the last two centuries or so, but as this only accelerates the risk spectrum shift, this ultimately is a battle that no society can win. There will inevitably come a time that the society drastically needs to change the way it interacts with the environment, or it will lose its coherence. In the latter case, after a time, the whole cycle begins anew—as one observes when looking at the rise and decline of firms, cities, nations, empires or civilizations.

Van der Leeuw's point is that it's rather simple minded -- and not really consistent with a real knowledge of history -- to have blind faith in the ability of humanity to innovate its way out of the various global crises we're now confronting. Our innovation in the past is what has caused them. We need, therefore, to innovate differently and more predictably. Can we?

 

Some readers of my Bloomberg column offered my some insightful criticisms by email, which I intend to share tomorrow.

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