venerdì 3 dicembre 2010

I sistemi complessi e l'economia.

In questo articolo  il Wall Street Journal discute i fallimenti dei modelli macroeconomici nel prevedere la crisi finanziaria e i rimedi che vengono proposti da fisici, psicologi ed economisti per costruire modelli più aderenti alla realtà: 

Physicist Doyne Farmer thinks we should analyze the economy the way we do epidemics and traffic.
Psychoanalyst David Tuckett believes the key to markets' gyrations can be found in the works of Sigmund Freud.
Economist Roman Frydman thinks we can never forecast the economy with any accuracy.
Disparate as their ideas may seem, all three are grappling with a riddle that they hope will catalyze a revolution in economics: How can we understand a world that has proven far more complex than the most advanced economic models assumed?
The question is far from academic. For decades, most economists, including the world's most powerful central bankers, have supposed that people are rational enough, and the working of markets smooth enough, that the whole economy can be reduced to a handful of equations. They assemble the equations into mathematical models that attempt to mimic the behavior of the economy. From Washington to Frankfurt to Tokyo, the models inform crucial decisions about everything from the right level of interest rates to how to regulate banks.
In the wake of a financial crisis and punishing recession that the models failed to capture, a growing number of economists are beginning to question the intellectual foundations on which the models are built. Researchers, some of whom spent years on the academic margins, are offering up a barrage of ideas that they hope could form the building blocks of a new paradigm.

Ripensare l'economia alla luce dei progressi compiuti nell'analisi dei sistemi complessi è una buona idea. Damodaran sul suo blog richiama però l'attenzione sulla necessità di evitare un uso inutilmente complicato della modellizzazione. Il riferimento all'articolo di Lorenz sulla dipendenza sensibile dalle condizioni iniziali nella teoria della turbolenza non è particolarmente azzeccato, ma il senso generale non manca di una certa dose di pragmatismo, in particolare quando scrive come

When faced with more uncertainty, strip the model down to only the basic inputs, minimize the complexity and build the simplest model you can. Take out all but the key variables and reduce detail. I use this principle when valuing companies. The more uncertainty I face,  the less detail I have in my valuation, recognizing that my capacity to forecast diminishes with uncertainty and that errors I make on these inputs will magnify as they percolate through the valuation. More good news: if I am going to screw up, at least I will do so with a lot less work!! 

E' un buon consiglio, da tenere particolarmente in mente quando si cerca di costruire strategie di investimento quantitative. 

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