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This is from an article in Scientific American - Why Economic Models Are Always Wrong:

Quote:When it comes to assigning blame for the current economic doldrums, the quants who build the complicated mathematic financial risk models, and the traders who rely on them, deserve their share of the blame. But what if there were a way to come up with simpler models that perfectly reflected reality? And what if we had perfect financial data to plug into them?

Incredibly, even under those utterly unrealizable conditions, we'd still get bad predictions from models.

The reason is that current methods used to “calibrate” models often render them inaccurate.

I'm not familiar with economics, but as I understand it, it is an issue of predictability, and economic systems being chaotic aren't very predictable. Now the questions that arise are:

1. Are economic models really that bad?
2. If so, do governments have policies to mitigate the effects of unpredictability?
   

I've come across some works that do answer the questions (like Taleb's The Black Swan), but would like to hear more opinions on it.

(27-10-2011 06:41 PM)Lije Wrote:  [*]Are economic models really that bad?

A professor of economics whom I recently heard speak, had this to say about why so many models fail us during recessions. For a model of any phenomenon to yield useful predictions, its parameters must first be estimated from real-world measurements of the same phenomenon (or supplied from reasonable assumptions). The professor noted that since we have not yet had enough recessions to 'calibrate our models with' or to estimate parameters from, our models have not yet been 'tuned' and hence their predictions are not reliable. He quipped, in jest of course, that the way to get better models of recession is...to have more recessions!

More broadly speaking, economic models are wrong because all models are wrong. A common saying among modelers is that "All models are wrong, but some models are useful". Perhaps what they mean is that every model involves simplifying assumptions and a model that is built to predict some behaviors of a system may fail miserably with others. The mathematician Norbert Wiener, also the popularizer of the term 'cybernetics', is known to have said, again half in jest, "The best model of a cat is a cat!" A live cat is of course not as useful as a model of a cat where unnecessary details have been abstracted out (for instance, an armature of its motor apparatus maybe sufficient to study the dynamics of cat locomotion, if that is the problem we are working on). Likewise, even if the best model of a recession is a recession, that is all the more reason to work on the caricatured models we do have, to recognize their limitations and improve them. Of course this is a hard sell especially in such forecasting, where the stakes are high and mistakes are literally costly.

The question of how good a model is eventually amounts to the following epistemological issues in the philosophy of science, first, the problem of sufficiency (What is the set of variables/measurements that can be said to constitute a sufficient description of a 'recession' or a 'cat'?) and second and more generally, the problem of induction (How can we generalize what we have learnt from seen cats and recessions to those we haven't yet seen?).

(27-10-2011 06:41 PM)Lije Wrote:  [*] If so, do governments have policies to mitigate the effects of unpredictability?

A case can be made that it is an ethical obligation on part of governments to ensure that the risks accruing from investing and budgeting on the basis of unreliable models and forecasts, should be borne by those involved in the decision-making, while the weakest sections of society remain shielded from its adverse effects. On what constitutes the responsibility of governments and whether the responsibility is different or heightened for certain types of citizens are ethical questions which we would have to confront even in an imaginary future time when all our economic models become reliable. In other words, there might come a hypothetical time when our modeling tools undergo incremental improvement and are able to forecast with arbitrary accuracy, but even in such a society, the ends to which such forecasts must be employed and who the beneficiaries will be will still be questions to be perennially renegotiated.