In search of beauty

In his review of my trio of books Nature’s Patterns in the TLS, Martin Kemp makes a start on a question that I leave more or less untouched: the issue of our subjective experience of pattern and form. Why do we respond aesthetically to pattern and order? Or to put it simplistically but, in the end, in the form that perhaps really counts: why do we find snowflakes and flowers beautiful?

That’s the issue psychologist Nicholas Humphrey tackles in an ambitious article in the current Prospect. More specifically, he asks why we apprehend beauty both in art and in nature. He seeks an answer in evolutionary psychology. And guess what: he finds it. He believes that ultimately an appreciation of beauty has its origin in sexual selection, much as Darwin anticipated. Beautiful works, he says, bear the hallmarks of human skill that offer signals of reproductive fitness: dexterity, intellectual ability, sensory acuity, perhaps even morality.

But why do we then find nature beautiful too, when there is no maker? Partly, says Humphrey, this is a question of convergence: we find traits in animals beautiful (such as butterfly wings) because the courtship displays of other animals are akin to those of humans. And partly, it is the result of the way we habitually personify inanimate nature, believing that it does indeed have a maker, and that this maker should therefore be the object of our love.

Now, let’s be fair: experience of beauty, and judgements of beauty in art, are so diverse that just about any attempt to explain them in biological and evolutionary terms is likely to be vulnerable. But just about every statement in Humphrey’s piece is open to challenge. It is hard to know where to start.

Probably the most serious problem for his thesis is that it is so culturally biased. Humphrey seems to assume that all cultures find beauty in the same places: that making visual art, music and literature is always primarily about making beauty, and that we all agree on it when we see it. But it is hotly debated whether, say, the primary function of music in some cultures is an aesthetic one at all. (Without culturally agreed points of common reference, it can be very hard to say.) Humphrey’s notion of what art is and what it strives to do seems solidly located within the Western Renaissance tradition.

The sexual selection idea seems kind of plausible, but no more than others. Certainly that’s the case for music, as I discussed in The Music Instinct. And like so much evolutionary psychology, not only is this idea not put to the test, but its proponents seem wholly unconcerned even to think about how that might be done. Is musical prowess an honest signal of survival skills, for instance? There seems to be no evidence for that (and sometimes evidence to the contrary). And what are the relevant skills? How does art demonstrate ‘loyalty’, for example, as Humphrey suggests? Are the ‘rich resources’ really those of the artist, or his/her patron?

And does a human sense of beauty really converge with animal mating displays? Sure, we admire the peacock’s tail, but some of these displays just strike us as bizarre. And many ‘beautiful’ animal traits, such as some butterfly markings, aren’t used for sexual display in any case. Humphrey argues that biological ‘good form’ is necessarily adaptive – among which characteristics he lists ‘rhyme’ (huh?), grace and symmetry. But symmetry is not necessarily adaptive in itself, and in any case, do we really look for the highest symmetry in art? (Answer: no. The highest symmetry is uniformity.)

But the weakest part of the argument surely comes when Humphrey tries to explain why we deceive ourselves in imagining that there is a creator in nature. He says that ‘at least in modern culture, many of our encounters with nature come first through art.’ One could question this statement even as it stands, but even if you accept it, it won’t wash. Why would we transform the ‘maker’ of a painting of a waterfall into a ‘maker’ of the waterfall itself? More to the point, if this is only the case in ‘modern culture’ (which it is), then it can play no evolutionary role.

Humphrey asks an important question: why do we possess a sense of beauty? It’s hard to answer, for sure. But inventing Just So stories, devoid of any means of empirical validation, won’t help. Nor are we likely to get very far by introversion: by assuming, as Hobbes and Descartes did, that we can so easily step outside our culture that it is meaningful to commence by asking ‘Well, how do I feel about it?’ We need at least to begin by asking not only what other cultures find beautiful, but what they mean by beauty.

Christmas is coming

I’m excited. Really. I have just discovered that my friend Mark Miodownik is going to deliver the Royal Institution Christmas Lectures this year. Mark is a materials scientist at King’s College (where, outrageously, the Materials Science Department is no more). He runs the wonderful Materials Library, where one can see and touch lots of very weird materials. I can think of no one better to fill Michael Faraday’s shoes for the Christmas Lectures, and I plan to be there.

More on the problem with economics

OK, my article on agent-based modelling of the economy is now out in the Economist – you might be able to get it here, but if firewalls prevent that then here, naturally, is the original thing. And I’m interested that the reader comments don’t seem by any means as adverse to this sort of thing as I’d imagined regular economists would be. Encouraging. Some feel that the economy, or people, are too complex to be captured by any kind of modelling. I don’t believe there is any reason to think that (and some good evidence to the contrary), although it is surely right that we must keep all models in perspective. And we have to remember that social science is the hardest science of all.

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For economists, the most serious deficit of the credit crunch may be in credibility. Vocal critics such as Nassim Nicholas Taleb are demanding to know why, when they failed utterly to foresee the crisis – indeed, apparently endorsed the conditions that created it – we should have the slightest faith in their capacity to mend it. And the diametrically opposed views of professional economists on what the remedy should be scarcely commands trust.

Yet there is little sign of discomfort or self-reflection in the citadel of orthodox economic theory. Much the same people, using much the same tools, are guiding economic policy after the crash as before it. Forecasting at the Federal Reserve, for example, is still being done using the so-called dynamic stochastic general equilibrium (DSGE) models that led one of its governors, Frederic Mishkin, to deliver an assessment of the downturn in the US housing market in summer 2007 that now looks grotesquely optimistic. The message seems to be ‘if you don’t fix it, it ain’t broke.’

Mainstream economics has always had its dissidents. But the seeds of change have never before found such fertile soil. Heavyweights such as Joseph Stiglitz and Paul Krugman are calling for radical rethinking. The Institute for New Economic Thinking (INET) in New York, which had its inaugural conference in April, boasts Stiglitz and Amartya Sen on its advisory board, and is bankrolled by George Soros. A hearing of the US House of Representatives Committee of Science and Technology in July called on distinguished witnesses such as Robert Solow to ‘build a science of economics for the real world’.

Critics tend to concur about what is wrong with the tools currently used for macroeconomic forecasting and policy – DSGE models were targeted in the House hearing, for example, while the INET has attacked many of the assumptions, including the efficient-market hypothesis and rational expectations, on which these models are predicated. But there is less agreement about what should replace the old techniques.

The hearing aimed to ‘question the wisdom of relying for national economic policy on a single, specific model when alternatives are available.’ One of the most promising and popular of these alternatives was on display at a workshop in Warrenton, Virginia at the end of June, funded by the US National Science Foundation and attended by a diverse bunch that included economists from the Fed and the Bank of England, social scientists, policy advisors and computer scientists. They explored the potential of so-called agent-based models (ABMs) of the economy to help us learn the lessons of the current financial crisis and perhaps to develop an early-warning system for anticipating the next one.  Better still, this non-traditional approach might offer prevention rather than cure: not the false promise of a crisis-free economy, but a way of identifying systemic vulnerabilities and mitigating their effects.

Agent-based modeling [1] does not assume that the economy can achieve a settled equilibrium. The modeler imposes no order or design on the economy from the top down, and unlike many traditional models, ABMs are not populated with ‘representative agents’: identical traders, firms or households whose individual behaviour mirrors the economy as a whole. Rather, an ABM uses a bottom-up approach which assigns particular behavioural rules to each agent. For example, some may believe that prices reflect fundamentals while others may rely on empirical observations of past price trends.

Crucially, agent behaviour may be determined (and altered) by direct interactions between them, whereas in conventional models interaction happens only indirectly through pricing. This provision of ABMs enables, for example, the copycat behaviour that leads to “herding” among investors. The agents may learn from experience or switch their strategies according to majority opinion. They can aggregate into institutional structures such as banks and firms. These things are very hard, sometimes impossible, to build into conventional models. But in an agent-based model one simply runs a computer simulation to see what emerges, free from any top-down assumptions. As economist Alan Kirman has put it, ABMs ‘provide an account of macro phenomena which are caused by interaction at the micro level but are no longer a blown-up version of that activity.’

Agent-based models are not exactly an alternative to conventional approaches, but a generalization of them: just about any economic theory could be expressed as an ABM, including the DSGE models now used for forecasting by most central banks. While those models are also based on microeconomic foundations, they accept the traditional view that there exists some ideal equilibrium towards which all prices are drawn. That this is often approximately true is why DSGE models perform well enough in a business-as-usual economy.

But DSGE models are useless in a crisis, as even advocates such as Robert Lucas admit. Last year, Lucas responded in this magazine to the criticism that these theories had failed to foresee the credit crunch by saying that such events are inherently unpredictable. All that can be reasonably expected of economic models, Lucas implied, is that they work well in ‘normal’ times. Crashes must forever be anomalies where theory breaks down.

That’s true of DSGE models because their ‘dynamic stochastic’ element amounts to minor fluctuations around an equilibrium state. Yet there is no equilibrium during big market fluctuations such as crashes – one can say that DSGE models thus insist that such events never occur.

ABMs, in contrast, make no assumptions about the existence of efficient markets or general equilibrium. The markets that they generate are generally not in equilibrium at all but are more like a turbulent river or the weather system, subject to constant storms and seizures of all sizes. Big fluctuations and even crashes are an inherent feature.

That’s because ABMs contain feedback mechanisms that can potentially amplify small effects, such as the herding and panic that generates bubbles and crashes. In mathematical terms the models are nonlinear, meaning that effects need not be proportional to their causes. These nonlinearities are absent from DSGE models, but they were evidently central to the credit crunch.

For example, in Virginia Andrew Lo of MIT’s Laboratory for Financial Engineering presented a model of the US housing market, inspired by ABM approaches, which showed how a fateful conjunction of rising house prices, falling interest rates and easy access to refinancing created high systemic risk, amplifying the housing downturn into an awesome burden of debt [2]. And John Geanakoplos of Yale University explained how the leverage cycle in remortgaging – high leverage during booms, low during recessions – can bloom into instability like an out-of-control pendulum, unless carefully managed [3]. The web of interdependencies forged from the buck-passing of risk using complex derivatives may create the potential for propagating nonlinear instabilities analogous to those that crashed the power grid of the North American eastern seaboard in 2003, and are precisely the kind of thing that ABMs are well suited to capturing. Sujit Kapadia of the Bank of England is attempting to uncover and model these network-based vulnerabilities in financial systems [4],

While all of these culprits have been fingered in the voluminous post-mortems of the current crisis, there has been barely any discussion of the way nonlinear feedbacks gave them such impact. As a result, the understanding on which any preventative regulation and ‘macroprudential’ strategies might be based is still thin.

Another of the key lessons of the crisis is the role of interactions between different sectors – housing and finance, say. While concentional macroeconomic models can incorporate these, ABMs might be better tailored to each specific sector – for example, including banks in financial markets, which DSGE models do not. In principle, ABMs can include as much of the economy as you like, with all the sector-specific structures and quirks. Indeed, the organizers of the Virginia workshop – physicist-turned-economist Doyne Farmer of the Santa Fe Institute in New Mexico and social scientist Robert Axtell of George Mason University in Virginia – wanted to explore the feasibility and utility of constructing an immense ABM of the entire global economy by ‘wiring’ many such modules together.

What might be required for such an enterprise in resources and expertise, and what might it hope to achieve? One vision is a real-time simulation, fed by masses of input data, that would operate rather like the traffic models now used for forecasting on the roads of Dallas and the Rhine-Westphalia region. But it might be more realistic and useful to employ a suite of such models, in the manner of global climate simulations, which project various possible futures and thus give an aggregated forecast – and show how our actions, laws and institutions might influence it.

In either case, the models would need much more data on the activities of individuals, banks and companies than is currently available. Gathering such information will be one of the key tasks of the US Office of Financial Research instituted by the 2010 Dodd-Frank Act to reform Wall Street. While this plan has raised privacy fears, such data-gathering is no less essential for understanding the economy than are meteorological observations for understanding climate, or geological monitoring to anticipate earthquakes.

And although seismologists may never be able to make precise forecasts, it would be deplorable if they were to shrug and resign themselves to modelling just the regular, gradual movements of tectonic plates and faults. Instead they have developed methods for mapping the evolution of stress patterns, identifying areas at risk, and refining rough heuristics for hazard assessment. Why should the same not be done for the financial system? It won’t be cheap or easy. But to deny the very possibility merely to absolve the conventional models of their severe limitations is starting to look unforgivable.

References

1. B. LeBaron & L. Tesfatasion, Am. Econ. Rev. 98(2), 246-250 (2008).

2. A. E. Khandani, A. W. Lo & R. C. Merton, Working Paper, September 2009.

3. A. Fostel & J. Geanakoplos, Am. Econ. Rev. 98(4), 1211-1244 (2008).

4. P. Gai & S. Kapadia, Bank of England Working Paper 383 (2010).