What do you call a financier in search of the iron laws of human behaviour? Answer: someone with a bad case of “physics envy”.

That is the peculiar psychological disorder diagnosed by Andrew Lo, a professor of financial engineering, as afflicting bankers and economists. Symptoms include a desperate search for the predictive certainty that comes from the hard sciences.

At least since the 18th century, economists have been borrowing from physics, redeploying everything from thermodynamics and the “conservation of energy” principle to the understanding of macroeconomics and the generation of fancy derivatives. The global financial crisis has, however, seen financiers cast their scientific net further as they try to understand what went wrong and how to make the banking system more stable in future. As a result, they are developing “biology envy”.

Bankers and financial economists are working with mathematical biologists to learn lessons about resilience from natural ecosystems – from fisheries to forests – and from the spread of disease. The exercise is certainly of more than academic interest. Andrew Haldane, executive director for financial stability at the Bank of England, says the regulatory structure for banking may be shaped by studies now in progress that treat global finance as a “complex adaptive system” like a living ecosystem.

The outcome could determine whether the system is robust enough to survive another financial storm without casualties on the scale of Lehman Brothers and without the need for governments to spend thousands of billions of taxpayer dollars to prevent a collapse.

Some policy conclusions are already clear. One is that the banking system has become at the same time too complex and too homogeneous. The problem is that over the past 20 years or so almost all the big globally active banks diversified their holdings and risk, moving into increasingly complex (and opaque) financial instruments. Unfortunately for the stability of the whole system, banks all diversified their business lines in a similar way and, in the process, became inextricably interdependent.

“From an individual firm’s perspective, these strategies looked like sensible attempts to purge risk through diversification: more eggs are being placed in the basket,” says Mr Haldane. “Viewed across the system as a whole, however, it is clear now that these strategies generated the opposite result: the greater the number of eggs, the greater the fragility of the basket – and the greater the probability of bad eggs.”

That is what a mathematical ecologist would have predicted if he or she had known what was going on in the world of finance. The tropical rainforest, for example, has so many interdependent species that it is more vulnerable to an external shock than the simpler ecological diversity of savannahs and grasslands.

The chart shows the global financial ecosystem in 2005. It has become much more interconnected over the past two decades. Total external financial stocks held by the world’s banking centres (nodes in the network) increased 14-fold since 1985 and the links between them were by then six times greater.

Financial products themselves meanwhile became fiendishly complex. An investor would in theory need to read as many as 1.125bn pages to understand the ingredients in the type of security known as a collateralised debt obligation squared, or CDO²,
which could contain portions of up to 93.75m mortgages.

Mathematical biology also helps to explain in retrospect why hedge funds, the institutions once thought to be at greatest risk of financial collapse, have survived the crisis in a healthy state. Compared with banking, the hedge fund sector is populated with relatively small, specialised players – the robust structure of a diverse ecosystem.

One distinguished mathematical biologist who is delving deep into the financial ecosystem is Lord Robert May, zoology professor at Oxford university and former president of Britain’s Royal Society. The financial theorists have a lot of ground to make up, he says: “The more I hear about financial economics, the more I am struck by its similarity to ecology in the 1960s.”

Economists talking about “efficient” or “perfect” markets remind Lord May of ecologists talking about “the balance of nature” 40 years ago, when ecosystems with a rich web of interactions were thought to be the most stable. Subsequent analysis has shown the opposite to be the case: the most robust systems can be decoupled into discrete components without collapsing.

Some were becoming concerned about systemic risk before the financial crisis erupted. The Bank of England started experimenting about five years ago with computer models of the banking system as an ecological network. The US National Academy of Sciences and the Federal Reserve Bank of New York launched a joint study in 2006 that brought together 100 experts to explore parallels between systemic risk in the financial sector and various fields of science and technology, from ecology to engineering. But the financial storm had set in by the time its conclusions were published.

Fisheries management has interesting parallels with financial regulation, says Lord May. For the past 50 years fish stocks have been managed on a species-by-species basis that aims to maximise the “sustainable yield” of individual fish such as cod or herring – an approach analogous to regulatory risk analysis that focuses on individual banks. But with the collapse of some important fishing grounds, marine scientists are coming to recognise that what really matters is the wider ecosystem and environmental context. You cannot protect cod, for example, without considering the sand eels, whiting, haddock, squid and other species on which cod feed.

Medical epidemiology is another fruitful borrowing ground for financial analysis. Just as epidemiologists trying to stem an outbreak of disease want to focus on identifying and vaccinating the most dangerous “super-spreaders” of infection, regulators need to control the damaging consequences for the whole banking network of the failure of large, interconnected institutions.

International banking rules such as Basel II have had the perverse effect of imposing the greatest capital restrictions on the smaller and less diversified banks that posed the least risk to the system, while the large “super-spreader” institutions were given more leeway. Borrowing an analogy from sexually transmitted disease, Mr Haldane says: “Basel vaccinated the naturally immune at the expense of the contagious; the celibate were inoculated, the promiscuous intoxicated.”

Further insights are emerging from a collaboration between David Rand at Harvard university’s programme for evolutionary dynamics and Nicholas Beale, who runs Sciteb, a London consultancy. “The fundamental requirement for the regulator is to ensure that the banks do not all diversify in the same way but rather we have ‘diverse diversification’,” Mr Beale says.

Their approach, rooted in mathematical models from evolutionary biology, “gives the real prospect of regulators being able to prevent dangerous ‘herding’, based on some simple, deep and new properties of financial networks”, he adds. A key element of the new system would be to provide banks with a “systemic risk rating” for each asset class, in a way that would induce them to diversify in different directions.

There is scope, too, for borrowing from epidemiology when it comes to gathering, analysing and communicating data. The World Health Organisation is constantly monitoring the globe for early signs of an epidemic of infectious disease – and if one breaks out, as Sars did in 2003 or swine flu this year, it provides vital information to governments, medical professionals and the general public. The banking world could do with an equivalent of the WHO, says Mr Haldane.

At the Massachusetts Institute of Technology, Prof Lo himself proposes that the US should set up a capital markets safety board to manage systemic risk, modelled on America’s National Transportation Safety Board.

While the analysis of ecosystems is the latest attempt to harness mathematical biology to finance, such systems analysis is not confined to biology. Experts have also seen useful lessons for banking stability in the way engineers protect electric power grids from collapse. Some others Some fancy a move back to physics, on a more sophisticated level. Theories that have dominated finance are drawn from research that took place in academia many years earlier – and was often reworked at around the same time as the concepts were permeating finance.

The crude forms of the “efficient market hypothesis” developed in the 1970s began to refashion the banking world in the 1990s, by which time the academic branch of economics was moving towards more subtle forms of behavioural finance. Similarly, the forms of classical physics that have driven financial engineering have long been superseded by more complex theories, such as refinements of relativity and quantum theory.

If biology does not do the trick, some of the more subtle and advanced concepts in physics might yet be able to shed light on economics. Or so some of the disenchanted quantitative analysts hope.

Changing the hypothesis: why ‘adaptive’ trumps ‘efficient’

Economists have always been keen to borrow principles from the hard sciences. In the 19th century Léon Walras and William Stanley Jevons both started their work with a view to importing the insights of physics into the economic sphere. Irving Fisher, the great neoclassical economist whose 1930s work has been rediscovered during this crisis, even wrote his doctoral thesis at the turn of the 20th century under the supervision of a physicist.

This tendency was given renewed impetus in the mid-20th century by Paul Samuelson’s application to economics of mathematical principles derived from thermodynamics. The development of computers able rapidly to analyse data made the development of mathematically elegant economic models particularly desirable, driving the acceptance of concepts such as American economist Eugene Fama’s efficient market hypothesis.

Most of the “quants” – financial mathematicians – who used such concepts to build financial models always knew that this project had serious flaws.

Emanuel Derman, for example, a physicist turned financier who formerly worked at Goldman Sachs, is credited with playing a central role in the development of models for derivatives. Yet more than a decade ago, he was warning Goldman Sachs clients of the limitations of derivatives models – he compared their relationship to reality to that between a child’s toy car and an actual automobile.

Mr Derman remains, to say the least, wary of the idea that efficient markets hypothesis can provide a “complete” guide to finance. “Unfortunately, absolute value theories don’t work very well in economics,” he wrote recently. “It’s difficult or well-nigh impossible to systematically predict what’s going to happen. You may think you know you’re in a bubble, but you still can’t tell whether things are going up or down the next day.”

Such scepticism has not often been expressed quite so frankly. On the contrary, some quants have furtively revelled in the power that their apparently elite knowledge gave them. “The dirty secret of banking is that lots of bankers have always felt a bit insecure because they did not really understand how this stuff worked – so those who understood it were in a strong position,” observes one banker.

However now that the crisis has exposed their shortcomings, the EMH and the entire model-based approach to finance are facing a radical rethink. A growing chorus of financiers, quants and economists argues that it is wrong to apply simplistic assumptions that underpin the physics-like models to people, since – unlike atoms, say – they can learn from each other and change in response to events. Changes may not happen in a neat, linear fashion.

Donald MacKenzie of Edinburgh university says the real problem with models is that bankers tend to view them as “cameras” that capture how the world works, like the camera that might photograph a physics experiment. Instead, he argues, they should be viewed as “engines” – since the presence of a model tends to change and drive market behaviour in a way that makes it impossible to assume that the past can predict the future.

Nevertheless, no alternative intellectual model – or source of inspiration – has emerged to offer a truly coherent alternative. George Soros (pictured), the former hedge fund manager, for example, argues that market participants need to embrace the idea of “reflexivity”, to recognise that markets change in response to participants, and to accept that models are an “engine, not camera”. However, turning this reflexivity theory into any investment manual or strategy has proved difficult.

Hence the move to look at branches of science beyond physics – and at biology in particular. Professor Andrew Lo of MIT has developed the adaptive market hypothesis, attempting to introduce the principles of evolution – competition, adaptation and natural selection – to his financial models.

Prof Lo believes that some of the features of human behaviour – such as loss aversion, overconfidence, overreaction and other behavioural biases – that are underappreciated by simpler models are, in fact, rational. These aspects of human behaviour, while not conforming to the caricature of homo economicus, may be optimal strategies for human behaviour that have been honed by millennia of evolutionary pressure.

Indeed, he takes this evolutionary process seriously: he is fond of pointing out to his audiences that they have both “mammalian” and “reptilian” brains that can be employed at different moments. Prof Lo believes that prices reflect not just information in the market place, but also deep-seated and slowly evolved human biases.