Escape from model land

“Letting go of the phantastic mathematical objects and achievables of model- land can lead to more relevant information on the real world and thus better-informed decision- making.” – Erica Thompson and Lenny Smith

The title and motivation for this post comes from a new paper by Erica Thompson and Lenny Smith, Escape from Model-Land. Excerpts from the paper:

“Model-land is a hypothetical world (Figure 1) in which mathematical simulations are evaluated against other mathematical simulations, mathematical models against other (or the same) mathematical model, everything is well-posed and models (and their imperfections) are known perfectly.

“It also promotes a seductive, fairy-tale state of mind in which optimising a simulation invariably reflects desirable pathways in the real world. Decision-support in model-land implies taking the output of model simulations at face value (perhaps using some form of statistical processing to account for blatant inconsistencies), and then interpreting frequencies in model-land to represent probabilities in the real-world.”

“It is comfortable for researchers to remain in model-land as far as possible, since within model-land everything is well-defined, our statistical methods are all valid, and we can prove and utilise theorems. Exploring the furthest reaches of model-land in fact is a very productive career strategy, since it is limited only by the available computational resource.”

“For what we term “climate-like” tasks, the realms of sophisticated statistical processing which variously “identify the best model”, “calibrate the parameters of the model”, “form a probability distribution from the ensemble”, “calculate the size of the discrepancy” etc., are castles in the air built on a single assumption which is known to be incorrect: that the model is perfect. These mathematical “phantastic objects”, are great works of logic but their outcomes are relevant only in model-land until a direct assertion is made that their underlying assumptions hold “well enough”; that they are shown to be adequate for purpose, not merely today’s best available model. Until the outcome is known, the ultimate arbiter must be expert judgment, as a model is always blind to things it does not contain and thus may experience Big Surprises.”

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