I haven’t posted much over the last week or so – things have been super busy trying to complete my PhD thesis. I hope to be submitting the thesis in the next few weeks so there’s not likely to be much blogging going on until that’s done (and I’ve had a little rest). So until I get back something resembling a ‘normal’ routine I’ll leave you with this…
One of my advisors point out this book review in the New York Times to me. From the article it seems that in Why Environmental Scientists Can’t Predict the Future, Orrin Pilkey and Linda Pilkey-Jarvis suggest environmental models aren’t up to the job that the modellers building and using them say they are:
Dr. Pilkey and his daughter Linda Pilkey-Jarvis, a geologist in the Washington State Department of Geology, have expanded this view into an overall attack on the use of computer programs to model nature. Nature is too complex, they say, and depends on too many processes that are poorly understood or little monitored — whether the process is the feedback effects of cloud cover on global warming or the movement of grains of sand on a beach.
Their book, “Useless Arithmetic: Why Environmental Scientists Can’t Predict the Future,” originated in a seminar Dr. Pilkey organized at Duke to look into the performance of mathematical models used in coastal geology. Among other things, participants concluded that beach modelers applied too many fixed values to phenomena that actually change quite a lot. For example, “assumed average wave height,” a variable crucial for many models, assumes that all waves hit the beach in the same way, that they are all the same height and that their patterns will not change over time. But, the authors say, that’s not the way things work.
Also, modelers’ formulas may include coefficients (the authors call them “fudge factors”) to ensure that they come out right. And the modelers may not check to see whether projects performed as predicted.
Along the way, Dr. Pilkey and Ms. Pilkey-Jarvis describe and explain a host of modeling terms, including quantitative and qualitative models (models that seek to answer precise questions with more or less precise numbers, as against models that seek to discern environmental trends).
They also discuss concepts like model sensitivity — the analysis of parameters included in a model to see which ones, if changed, are most likely to change model results.
But, the authors say it is important to remember that model sensitivity assesses the parameter’s importance in the model, not necessarily in nature. If a model itself is “a poor representation of reality,” they write, “determining the sensitivity of an individual parameter in the model is a meaningless pursuit.”
Given the problems with models, should we abandon them altogether? Perhaps, the authors say. Their favored alternative seems to be adaptive management, in which policymakers may start with a model of how a given ecosystem works, but make constant observations in the field, altering their policies as conditions change. But that approach has drawbacks, among them requirements for assiduous monitoring, flexible planning and a willingness to change courses in midstream. For practical and political reasons, all are hard to achieve.
Besides, they acknowledge, people seem to have such a powerful desire to defend policies with formulas (or “fig leaves,” as the authors call them), that managers keep applying them, long after their utility has been called into question.
So the authors offer some suggestions for using models better. We could, for example, pay more attention to nature, monitoring our streams, beaches, forests or fields to accumulate information on how living things and their environments interact. That kind of data is crucial for models. Modeling should be transparent. That is, any interested person should be able to see and understand how the model works — what factors it weighs heaviest, what coefficients it includes, what phenomena it leaves out, and so on. Also, modelers should say explicitly what assumptions they make.
Some of these suggestions sounds sensible and similar to what I’ve been thinking about in my thesis. However, to suggest abandoning environmental modelling altogether – claiming that is it of no value whatsoever – seems a little excessive and I’m going reserve my judgment for now.
I’m being sent a review copy so when I get my life back I’ll take a look at it and post some more informed criticism.