PRINCETON – To understand how we got ourselves into our current economic mess, complicated explanations about derivatives, regulatory failure, and so on are beside the point. The best answer is both ancient and simple: hubris.
PRINCETON – To understand how we got ourselves into our current economic mess, complicated explanations about derivatives, regulatory failure, and so on are beside the point. The best answer is both ancient and simple: hubris.
In modern mathematical economics, many people in the rich world decided that we had finally devised a set of scientific tools that could really predict human behavior. These tools were supposed to be as reliable as those used in engineering.
Having ushered scientific socialism into its grave at the Cold War’s end, we quickly found ourselves embracing another Science of Man.
Our new beliefs did not stem from some new experiment or unexpected observation, the way a real scientific paradigm shift does.
Economists do not typically conduct experiments with real money. When they do, as when the Nobel laureate Myron Scholes ran the hedge fund Long Term Capital Management (LTCM), the dangers often outweigh the benefits (a lesson we still don’t seem to have learned.)
And, since almost every observation that economists make turns out in a way that wasn’t predicted, no unexpected observation could ever actually change an economic paradigm.
What really produced the change in economics that led to disaster was the simple fact that you could now get away with saying certain kinds of things in public. Some of us honestly thought that history was over.
And after all, you can’t have a final, utopian society without having a final, scientific theory of human behavior, together with some mad scientists or philosophes to preside over the whole thing.
The problem is that, no matter how "scientifically” these new beliefs were formulated, they are still false. Capitalism is, among other things, a struggle between individual people over the control of scarce resources.
Like boxing and poker, it is a soft, restrained, private form of warfare.
Military strategists have known for centuries that there is, and can be, no final science of war. In a real struggle over things that actually matter, we must assume that we are up against thinking opponents, who may understand some things about us that we don’t know about ourselves.
For example, if profit can be made by understanding the model behind a policy, as is surely the case with the models used by the United States Federal Reserve, sooner or later so much capital will seek that profit that the tail will begin to wag the dog, as has been happening lately.
The truth is that such models are most useful when they are little known or not universally believed. They progressively lose their predictive value as we all accept and begin to bet on them.
But there can be no real predictive science for a system that may change its behavior if we publish a model of it.
Markets might once have been fairly efficient, before we had the theory of efficient markets. If investing is simply a matter of allocating money to an index, however, liquidity becomes the sole determinant of prices, and valuations go haywire.
When a substantial fraction of market participants are simply buying the index, the market’s role in ensuring good corporate governance also disappears.
The formation of large bubbles in recent decades was partly a consequence of the commonness and incorrigibility of the belief that no such thing could ever happen. Our collective belief that markets are efficient helped make them wildly inefficient.
Despite this, over the course of the last 20 years, economists began to act as if we thought we could genuinely predict the economic future. If the universe didn’t oblige, it wasn’t because our models were wrong; "market failure” was to blame.
It is not clear how we could know that markets were failing whenever they fell significantly, but believed that we had no business second-guessing them when they climbed.
Nor is it clear how we all knew that LTCM’s failure or the post-September 11 malaise posed grave risks to the system, but could seriously doubt that the dot-com craze was a bubble.
We repeatedly rescued bubbles, and never deliberately burst them. As a result, our financial markets became a pyramid scheme.
Moral hazard, we thought, could safely be ignored, because it is "moral,” which, as every true scientist knows, just means "imaginary.”
But a market is not a rocket, economists are not rocket scientists, and moral hazard is, in human affairs, the risk that matters most.
The false belief that we can collectively see the future using science has led us all to make various binding promises about things in that future that no human being can possibly guarantee.
A promise of something that we should know cannot be guaranteed is also known as a lie. That vast tissue of lies is now tearing itself apart.
Governments think we can stop this process by throwing money at it, but there are many reasons to believe that this won’t work.
The banking system is probably already past saving – many institutions simply aren’t banks anymore, but vast experiments that didn’t work out as predicted.
We could easily be "stimulating” and "rescuing” the economy for a rather long time, in ways that only delay the needed adjustment, before we are finally forced to allow the required creative destruction to occur.
But that is not the real problem. The real problem is the pseudoscientific ideology behind today’s crisis. A final science of man has no room for the unplanned and unpredictable recovery that is the only kind a capitalist economy can have after a crisis of this size.
If we cleave to the false security of a supposed science that isn’t working, and forget about the philosophy behind it, ideas like personal responsibility and the right to fail, our leaders will very scientifically give us no recovery at all.
Daniel Cloud teaches philosophy at Princeton University, and is a founding partner of two hedge funds, Firebird Fund Management and Quantrarian Capital Management.
Copyright: Project Syndicate, 2009.
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