Eric Beinhocker .. has undertaken his own 500-page haj, entitled â€œThe Origin of Wealth: Evolution, Complexity, and the Radical Remaking of Economicsâ€ .. it is good enough, and scholarly enough, to warrant [the attention of the professors] rather than their scorn.
.. Mr Beinhocker is himself critical of â€œloose analogisingâ€ between biology and economics. The economy, Mr Beinhocker says, is not â€œlikeâ€ a rainforest. Rather, economies and ecosystems are both evolutionary systems in their own right. The evolutionary formulaâ€”variation, selection and replicationâ€”is a formal, â€œall-purposeâ€ principle, which can perform its magic equally well in either domain.
How, then, does the principle go to work on the stuff of economics? The business plan is the economic equivalent of DNA, and the enterprise its host in the world. Mr Beinhocker envisages a Borgesian library of every conceivable plan, from farming wheat in Lebanon in 8500BC to manufacturing â€œsupernano neural-blastule tubesâ€ that have yet to be invented. Evolution provides a remarkably effective way for the economy as a whole to scour this vast library for viable strategies, finding â€œneedles of good design in haystacks of possibility,â€ as Daniel Dennett, a philosopher, has put it.
Countless firms, busily tinkering with their business models, provide a source of variation. The market itselfâ€”what gets bought and what gets left on the shelfâ€”imposes a powerful form of selection. And although business plans cannot reproduce, successful ones do command a growing share of the economy’s resources, as companies expand on the back of them and rivals copy them.
Such a process succeeds at the level of the economy as a whole, while remaining coldly indifferent to the fate of individual firms within it. Indeed, Mr Beinhocker cites research showing that progress owes more to new firms replacing old than to incumbent firms renewing themselves. This is not a comfortable conclusion for businessmen who might buy this book. But as a McKinsey man, Mr Beinhocker cannot resist offering a few tips to executives who are not content to be â€œexperimental grist for the evolutionary millâ€.
Such ideas are less threatening to economists: Milton Friedman evoked the notion of market selection over 50 years ago. But more unsettling than the ideas are the techniques and tools Mr Beinhocker advocates. He argues that economists should abandon blackboard deduction in favour of computer simulation. The economists he likes do not â€œsolveâ€ models of the economy â€” deducing the prices and quantities that will prevail in equilibrium â€” rather they grow them â€œin silicoâ€, as he puts it.
An early example is the sugarscape simulation done in 1995 by Joshua Epstein and Robert Axtell, of the Brookings Institution. On a computer-generated landscape, studded with â€œsugarâ€ mountains, they scattered a variety of simple, sugar-eating creatures, which compete for this precious commodity. Some creatures move faster than others, some see farther, and some burn sugar at a higher metabolic rate than their rivals.
Surprisingly, the results of their myopic lives can be gripping. Even simple rules of behaviour result in collective patterns that are impossible to foresee yet easy to recognise. The sugarscape, for example, is quickly beset by a division between haves and have-nots, which bears a strong statistical resemblance to the distribution of income in real economies. These macro-results cannot be deduced from the micro-rules simulators write. Rather, they emerge from the interactions of the creatures in the model, just as â€œwetnessâ€ emerges from the interaction of water molecules, rather than being a property of the molecule itself.
Such simulations may be unpredictable, but they are nonetheless understandable, Mr Beinhocker insists. By toying with different parameters, such as metabolic rates or the height of the sugar mountains, analysts can learn how to â€œtuneâ€ their model to generate different results. This understanding may be more valuable than a forecast, he argues. But whatever such enlightenment is worth, it is not easy to communicate to others. The revelations contained in a deductive proof or theorem are easy to pass on: they leave a set of footprints for other people to follow, making it easy for a theory to persuade and convert. For simulations, by contrast, â€œthe only way to see what happens is to run the model and evolve it â€” there is no shortcut.â€