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The problem is not the math itself, but the blind acceptance and even idolatry we have applied to the quantitative models. These predictive models leave citizens befuddled and unable to defend or criticize model-based decisions. We argue that we should accept the fact that we live in a qualitative world when it comes to natural processes. We must rely on qualitative models that predict only direction, trends, or magnitudes of natural phenomena, and accept the possibility of being imprecise or wrong to some degree. We should demand that when models are used, the assumptions and model simplifications are clearly stated. A better method in many cases will be adaptive management, where a flexible approach is used, where we admit there are uncertainties down the road and we watch and adapt as nature rolls on.

 

Orrin H. Pilkey

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My first heresy says that all the fuss about global warming is grossly exaggerated. Here I am opposing the holy brotherhood of climate model experts and the crowd of deluded citizens who believe the numbers predicted by the computer models. Of course, they say, I have no degree in meteorology and I am therefore not qualified to speak. But I have studied the climate models and I know what they can do. The models solve the equations of fluid dynamics, and they do a very good job of describing the fluid motions of the atmosphere and the oceans. They do a very poor job of describing the clouds, the dust, the chemistry and the biology of fields and farms and forests. They do not begin to describe the real world that we live in. The real world is muddy and messy and full of things that we do not yet understand. It is much easier for a scientist to sit in an air-conditioned building and run computer models, than to put on winter clothes and measure what is really happening outside in the swamps and the clouds. That is why the climate model experts end up believing their own models.

 

Freeman Dyson

 

For more than twenty-five years we have monitored beach nourishment projects around the United States. In order to secure federal funding and justify the enormous costs of these projects, anyone undertaking one must make a prediction of how long the sand will last on the replenished beach. The predictions are based on mathematical models that are said to be sophisticated and state of the art, and yet are consistently, dramatically wrong—always in an optimistic direction. In the rare instances when communities questioned the models after the predictions of a long healthy replenished beach clearly failed, the answer typically was that an unusual and unexpected storm caused the error. Well, the occurrence of storms at any beach is neither unusual nor unexpected. Eventually we became interested in how models were used in other fields. When you start looking into it, you find that a lot of global and local decisions are made based on modeling the environment. There are some fascinating (and discouraging) stories of model misuse and misplaced trust in models in the book.

 

Orrin H. Pilkey

 

Since all models are wrong the scientist cannot obtain a "correct" one by excessive elaboration. On the contrary following William of Occam he should seek an economical description of natural phenomena. Just as the ability to devise simple but evocative models is the signature of the great scientist so overelaboration and overparameterization is often the mark of mediocrity.

 

George E. P. Box

 

"Have the climate models been successful in predicting anything? They, of course, predict substantial global warming. This is not surprising given the expressed belief of some of the model builders in the global warming hypothesis and the many parameters in the model that need to be introduced. However, the models also predict unambiguously that the atmosphere is warming faster than the surface of the earth; but all the available observational data unambiguously shows the opposite! Truth in science is always determined from observational facts. One finds the truth by making a hypothesis and comparing observations with the hypothesis. It is absolutely essential that one should be neutral and not fall in love with the hypothesis. If the facts are contrary to any predictions, then the hypothesis is wrong no matter how appealing. "Truth by Assertion" is not science."

 

David Douglass

 

Going back into the history of a word, very often into Latin, we come back pretty commonly to pictures or models of how things happen or are done. These models may be fairly sophisticated and recent, as is perhaps the case with 'motive' or 'impulse', but one of the commonest and most primitive types of model is one which is apt to baffle us through its very naturalness and simplicity.

 

J. L. Austin