On Models and
Today model building is the preferred method of research in fields as otherwise diverse as investment banking and climatology. Experts in the various disciplines are zealously convinced of the efficacy of their models. Such reliance on models has proven disastrous in the past.
From 1930s to 1970s macro-economists built enormously complex models of economic systems designed to serve as basis for long term economic planning. Psychologically the models were impressive in that they gave people the illusion of knowledge and control. Unfortunately, those models didn’t work except perhaps to provide employment and professional advancement for cadres of economists. A macro-economic model consists of a series of equations, a set of variables, and a list of relations between those variables expressed by coefficients of the model. Although the original in-put out-put equations may have been created by means of observations of a functioning market-based economy, once fixed into the model, empirical and theoretical in-puts became conceptually redundant. The model itself became a black-box mechanism for formulating and implementing policy. And, according to developmental economist Michael Todaro such plans provided important psychological benefits in "mobilizing popular sentiment and cutting across tribal factions with the plea to all citizens to 'work together,'” so that an “enlightened central government, through its economic plan, [could] provide the needed incentive to overcome the inhibiting forces of traditionalism in the quest for widespread material progress." Reliance on such models by central governments was often disastrous for developing economies.
In fact, despite the solemn assurances of the experts, these models proved to have very little explanatory power or predictive power. They relied equations which were themselves time and place dependent. At best they represented snapshots of a particular economy at particular times although they were used to predict the behavior of other economies at other times. Not only were macro-economists unable to predict the state of an economy ten years out, they were rarely able to predict the state of an economy ten weeks out.
With the enormous computing capability of computers and advances in statistical analysis models have become more and more sophisticated although they may in fact be based on very little theory and relatively few well chosen empirical observations. Linked to computer graphics, today’s models are as beautiful, as impressive, and as entertaining as Star Wars battle scenes. We seem to quite literally see the world developing before our eyes. Nevertheless, the fundamental logic of model building has not changed. In many respects Google Earth is the finest of models. Its empirical content is many times more solid than the empirical content of global warming models. Nevertheless, while Google Earth can tell us a great deal about
Population growth models based on data from 1950 -1970 is inapplicable to population growth patterns in 2009. Investment banking models were based on data from 1945 - 2005 during which time single family housing prices in aggregate increased. The models based on these data predicted that single home mortgages in aggregate had less than a 1% chance risk of default. As a result neither the government nor the investment houses required margins for trading in these aggregated mortgages and their derivatives and the derivatives of their derivatives. The unintended consequences of these models led to an enormous multiplication of liquidity and astronomic growth in leverage and thus M3 – which has not been measured in three years. Financial collapse was certain but not predictable from within these particular models. According to Freeman Dyson of
The results of this reliance in climatology on models rather than on observation, experiment, and theory is as likely to be as disappointing as similar reliance on econometric models was in the mid twentieth century and investment banking models which led to our current financial collapse. Believers in these climate change models, in particular, are becoming increasingly dogmatic. No countervailing opinions even from within a particular disciplines are allowed to undermine the faith in the model itself. Critics are castigated as heretics. Unlike genuinely scientific theories like Newtonian mechanics, these models cannot be tested. The models create the appearance of precision by the magic of long division, but there is little real precision in their predictive ability more than two or three weeks out. When the model builders encounter facts which seem to contradict the predictions of they invariably tinker with the model and announce that the model simply needed a little adjusting. The proponents of these models will not admit even the possibility of being fundamentally in error. Too much seems to be at stake.
A model is not a theory. A genuine scientific theory is a net which addresses, at best, only certain aspects of reality. In its essence a theory is a simplification and so while its powers to explain are high, its ability to predict is limited except in very controlled conditions. What the theory cannot interpret in its terms, it must ignore. A sophisticated theorist will recognize the limitations of any particular theory. Today’s model builders, in contrast, believe that they have somehow reproduced reality and in their zeal they are often able to use “enlightened central governments” “to mobilize public sentiment” in their various causes.
