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Useless Arithmetic: Why Environmental Scientists Can't Predict the Future Hardcover – Jan 9 2007
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This book is a welcome antidote to the blind use of supposedly quantitative models.(Carl Wunsch American Scientist)
This is an easy and persuasive read.(Fred Pearce New Scientist)
Useless Arithmetic dispels many myths and is a 'must read' packing in case studies and insights on faulty thinking.(The Midwest Book Review)
[This] readily accessible book should be read by any activist who's ever had to face off against the opposition's engineers.(Earth Island Journal)
A concise, powerful, and readable book.(Steven R. Carpenter Issues in Science and Technology)
This book should be in every library... Essential.(Choice)
Useless Arithmetic will surely excite any reader.(David Simberloff BioScience)
Using concrete examples, the authors of Useless Arithmetic cut through the scientific jargon to show how and why many aspects of the environment are under threat because of the slavish adherence to misleading mathematical models by their technical and political advocates.(Victor R. Baker, University of Arizona)
In a complex, imperfect world quantitative models feed the delusion that society can predict its way out of its environmental dilemmas. The corrosive result is that politics and science have become inextricably interwoven to the considerable detriment of both. This engaging, wise, and far-reaching book diagnoses the causes and costs of our quantitative hubris, and in so doing points the difficult way toward a more productive relationship among science, democracy, and the vexing challenges of environmental stewardship.(Daniel Sarewitz, director, Consortium for Science, Policy, and Outcomes, Arizona State University)
Useless Arithmetic is an important book for those of us who believe that environmental science and policy should be self-correcting on the basis of experience. Written for lay persons, it draws attention to a broad range of sobering experiences typically ignored in the over-promotion of quantitative models for predictive purposes.(Ron Brunner, Center for Public Policy Research, University of Colorado, Boulder)
Orrin H. Pilkey and Linda Pilkey-Jarvis argue that many models are worse than useless, providing a false sense of security and an unwarranted confidence in our scientific expertise. Regardless of how one responds to their views, they can't be ignored. A must-read for anyone seriously interested in the role of models in contemporary science and policy.(Naomi Oreskes, professor, Department of History, University of California, San Diego) See all Product Description
Most Helpful Customer Reviews on Amazon.com (beta)
Quantitative models are decried throughout the book, and the suggestion is made that what is reasonable is "qualitative" modelling. The distinction isn't really developed until the last chapter where some good examples are to be found. Still, the distinction isn't as crisp as I'd like - perhaps it is a qualitative difference and not a quantitative one! Another positive suggestion is that incrementalism is a generally better approach to interacting with the complexities of nature than the brittle approaches that arise from an overly numerate engineering mentality. In other words, instead of using quantitative models to plan enormous, long-term projects, try something on a small scale, observe the results, and go from there.
I came away with considerably more knowledge of the topics discussed. I was already a convert to the basic themes - that we tend to overestimate what we know, to trust numbers more than we should, that political processes often interact with science in ways that are inimical to both good decisions and greater knowledge. Several times I thought of Eisenhower's dictum that plans are generally useless but planning is essential. Perhaps that captures best the distinction Pilkey is trying to make about qualitative models.
Unlike some of the other reviewers, I was not offended by the political implications of anything Pilkey asserts. I didn't see it as either pro or anti global warming in any political sense. No hidden agendas here, it's really about modelling. Recommended.
Not one of these groups has had any success, but it is arguable that the environmental scientists have done the most damage to other people by being wrong.
Orrin Pilkey, a well-known if not always well-liked specialist on coastal processes at Duke, began a seminar to examine why the predictions of coastal engineers seemed so often to lead to projects that didn't work. (I have known the North Carolina beaches where Pilkey does much of his work for more than 50 years. The Outer Bankers hate what Pilkey says about their beaches, but he's right.)
The investigation led to a wider examination of numerical models of all sorts of natural processes. Among those examined -- all failures -- were managing the Grand Banks fishery, predicting the lifetime of nourished beaches, predicting toxicity of lakes in abandoned pit mines and predicting how fast sea level will rise. Predictably, more attention is paid to coastal processes than anything else, but other topics get fair treatment, even one as far from the coast as the Yucca Mountain atomic waste dump.
What the Pilkeys found was no surprise to me as a newspaper reporter, and will be even less a surprise to scientists. People forget, but reporters keep clipping files. Mine contain many reminders about predictions made but unfulfilled. The Pilkeys conclude that it is impossible to write quantitative numerical models of any complex process on the surface of the earth. This is unlike numerical models of tractable systems, like the orbits of comets or the fracture strength of a steel-frame building.
For most people, the most familiar such models are the global circulation models used by the Intergovernmental Panel on Climate Change to predict catastrophic global warming. The Pilkeys barely mention GCMs in passing, and this is probably wise. If their goal is to get non-technical people (or even technical people with open minds about modeling) to rethink the enthusiasm for them, then injecting global warming into the mix would no doubt distract from their greater goal.
However, the deduction is clear and inescapable -- forget the GCMs. They offer no guide to anything.
The reasons all these models fail are many. One the Pilkeys emphasize more than any other is the "ordering problem." Even if you knew all the parameters that count (seldom or never the case), and even if you knew how they interact with each other), you can never, ever know in which order the variable ones will present themselves. If current changes affect beaches and hurricanes affect beaches, and both are variable over time, you cannot know in advance whether the storm will come first, or something will alter the current first.
This is not an original thought. Although he did not call it an ordering problem, the immunologist and philosopher of science Peter Medawar identified it as the reason that it will always be impossible to conduct meaningful experiments to disentangle the effects of nature and nurture on human behavior. (See his essay, "Further Comments on Psychoanalysis" in "The Strange Case of the Spotted Mice" and my review of that book.)
Other critics, such as Vaclav Smil and Naomi Oreskes, have also demolished the concept of numerical modeling of complex systems, and the Pilkeys give them credit for their work. Even outside the natural sciences, the empirical failure of attempts to predict (or even to quantify) complex systems have been demonstrated conclusively, for instance by lawyer Michael Wheeler in "Lies, Damn Lies and Statistics: the Manipulation of Public Opinion in America." That book was published more than 30 years ago; there really can be no excuse at this late date for people to question the Pilkeys' conclusion.
People do. Orrin Pilkey has been called a neo-Luddite by defenders of quantitative modeling.
The beaches, however, are not there, just as he said they would not be.
As an alternative, the Pilkeys advocate qualitative modeling. It gives, at best, trends, it outlines areas that might require remediation as things develop. Its asset is flexibility. That's something you never get with a seawall. Those are inflexible.
Another approach, about which the Pilkeys take no firm stand, is the Dutch method of dealing with retreating beaches: dump and run. In other words, build up the beach without expectations of how soon it will have to be done again. The Dutch, with their heritage of centuries of battling the sea, probably think in terms of processes rather than "solutions."
This short but powerful volume is explicitly aimed at non-technical readers. Although quantitative models are mathematical, it is not necessary to know math to understand how they work, and the Pilkeys relegate what little math is in the book to an appendix. Like Orrin Pilkey's lectures to public audiences on coastal erosion, he can make complex processes understandable, if not predictable.
I only wish the Pilkeys had included a chapter on wellhead protection zones, which are going to become an increasingly hot topic in many areas. These attempt to predict how far you have to keep pollutant sources away from the places you find your drinking water. The approach used so far has been numerical modeling. "Useless Arithmetic" does not encourage confidence in the protection policies being adopted.
The Good: The authors advance the idea that mathematical (computer) modeling of complex systems is often misused. A combination of not understanding all of the important physical processes, making inappropriate assumptions about initial conditions and improper handling of chaotic events make the predictions inaccurate. The process is further corrupted by politics, money, bias and hubris. This is all too true.
The subject material is presented virtually without mathematics and can be understood by just about anyone. It will also arm the lay person to ask questions that are likely to be embarrassing to most models. Those questions range from "what were the assumptions?" to "has the model successfully predicted events or does it need to be constantly fudged to match the real world?". Multiple examples are given of assumptions and processes that violated basic common sense.
I was particularly interested in the chapter on modeling the nuclear waste repository at Yucca Mountain. I made some of the permeability measurements of basalt and granite that were used in the models. I shared office and lab space with people that generated a lot of the measurements on salt that went into the modeling effort. I can't offer independent confirmation of every statement the author's made. However, their comments about the models making the ludicrous assumption that cracks and fissures weren't important was spot on. My colleagues and I also published work that attempted to measure the characteristics of cracks and fissures and evaluated fluid flow through these cracks and fissures. The modelers made a conscious choice to ignore the latter data.
The bad: The authors are apparently unable to apply their lessons outside of their own field of expertise. After successfully demolishing a number of models that had been accepted without question, they turned to global warming. They spent the chapter praising the climate modelers for admitting that the models didn't accurately predict sea levels.
However, they completely accepted the idea that man made carbon dioxide was an important contribution to global warming. In doing so, they violated every statement they made about blindly accepting models:
1) They were apparently unaware the greenhouse effect is a theory and that the primary proof of the theory is an unverified mathematical model.
2) They make the case that we should believe this model because every credible scientist endorses it. They had previously made the point that other widely accepted models have been wrong. Further, this statement is a lie unless the test for credibility is agreement with the model.
3) The climate models predict that the greenhouse effect will warm the lower atmosphere (energy that would otherwise be radiated to space is captured by the lower atmosphere) and then be transferred to the Earth. Measurements have not shown the predicted temperature increases in the lower atmosphere. Further, the models predict that the effect would be stronger at the poles than at the equator. Measurements have shown the temperate zones getting warmer and the poles getting colder. This has not caused the authors or the proponents of man made global warming to reconsider the fundamental assumption.
4) Political bias has resulted in the IPCC report being modified to remove statements about the limitations of the model that the scientific community had included. The authors do point out that global warming research is a $4B business that will seek to perpetuate itself and then praise the global warming work while attributing an economic bias to those that question it.
5) The models have not successfully predicted anything. Further, the modelers have not been able to tweak the models to generate some important physical phenomenon such as the South Pacific heat vent (NASA says that the vent is triggered by a small rise in the ocean temperature and radiates about the same amount of heat as is purportedly generated by man made greenhouse gasses).
For a lucid and comprehensive discussion of the opposing views, I recommend "Unstoppable Global Warming Every 1,500 Years" by Singer and Avery.
The ugly. Not being content to ignore their own advice re. questioning the climate models, they stoop to characterizing those who do question the model with words such as "profoundly unenlightened", "rabid", "clumsy and disingenuous", "not objective", "high and mighty", "motivated entirely by economic consideration", "lack of scientific integrity". This displays a dismaying lack of intellectual integrity on the part of the authors.
Unfortunately, the authors exhibit a level of bias against any model they don't approve that is so over the top that I was constantly wondering what cheese would be served with the "whine". And then they cap it off by blindly accepting an entire range of dire global warming predictions, which are entirely derived from - you guessed it - models of complex natural processes. I guess if you like the model's answers then it is magically a good model.
I have a hard time accepting what appears to be intellectual dishonesty, so although the book makes some good points, I really can't recommend it. The authors also appear to be particularly upset with certain individuals and organizations in the coastal engineering community, because the animus comes through loud and clear.
If you really want a good book on the limitations of mathematical modeling as applied to the real world, there is a two-volume set called "Reality Rules" that is much better. However, the Reality Rules books are not aimed at the layperson, so be prepared for some real math in these books.
Unfortunately too many folks are seeing models as oracles. This book examines seven natural system models and points out their uselessness as accurate quantitative predictors. They do not throw the work of climate science into this bin but recognize that there are loons out there who are attributing too much certainty to those models.
Well written, with touches of humor, this book is core for living with these darn models.
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