The Frontal Cortex

Dangerous Models

You know what I think about when I hear about the epic failure of all these fancy financial models that were designed to calculate risk? I think about the Atlantic Cod. These fish used to be everywhere. (Once upon a time, they were considered the cash crop of the ocean. Spanish fishing vessels would trek across the Atlantic just to fish the abundant cod off the coast of Canada.) Now the Newfoundland cod fishery is gone, yet another victim of overfishing.

The story of cod is usually told as the tragedy of trawlers. A trawler is boat designed to drag a massive net behind it. These nets are weighted, so that they cling to the bottom of the ocean floor. They sweep up everything for miles and miles. Most of the haul is trash – trawlers leave a trail of dead, unwanted fish – but they can also capture thousands of cod in a single haul. The use of radar made these trawlers even more efficient; now they knew exactly where to drop their nets. The result was a boom in caught cod: by the late 1960’s, fishermen were hauling in more than 800,000 tons of cod every year.

But trawlers aren’t entirely to blame. Their catch was still within the legal limits. (Cheating, of course, remained a big problem. Many fishing boats caught way too many fish, just as fraudulent lending helped implode the subprime market.) In fact, the Canadian government had been concerned about the cod population for decades. In the 1970’s, the government instituted strict regulations that limited the total catch to just 16 percent of the total cod population. The tricky part, of course, was coming up with the population estimates in the first place. It’s hard to know how many fish to catch if you don’t know how many fish there are. But fishery scientists were confident that their sophisticated models were accurate. They had randomly selected areas of the ocean to sample and then, through the use of a complicated algorithm, arrived at their total estimate of the cod population. They predicted that the new regulations would allow the cod stock to steadily increase. Fish and the fishing industry would both thrive.

The models were all wrong. The cod population never grew. By the late 1980’s, even the trawlers couldn’t find cod. It was now clear that the scientists had made some grievous errors. The fishermen hadn’t been catching 16 percent of the cod population; they had been catching 60 percent of the cod population. The models were off by a factor of four. “For the cod fishery,” write Orrin Pilkey and Linda Pilkey-Jarvis, in their excellent book Useless Arithmetic: Why Environmental Scientists Can’t Predict the Future, “as for most of earth’s surface systems, whether biological or geological, the complex interaction of huge numbers of parameters make mathematical modeling on a scale of predictive accuracy that would be useful to fishers a virtual impossibility.”

People love models, especially when they’re big, complex and quantitative. Models make us feel safe. They take the uncertainty of the future and break it down into neat, bite-sized equations. But here’s the problem with models, which is really a problem with the human mind. We become so focused on the predictions of the model – be it the cod population, or the risk of mortgage derivatives – that we stop questioning the basic assumptions of the model. (Instead, the confirmation bias seeps in and we devote way too much mental energy to proving the model true.) It’s not just about black swans or random outliers. After all, there was no black swan event that triggered this most recent financial mess. There was simply an exquisite model, churning out extremely profitable predictions, that happened to be based on a false premise. Hopefully, the markets will recover quicker than the Atlantic cod.

Comments

  1. #1 Rachael
    October 28, 2008

    I often dislike models, but for different reasons than what you listed above. Much of my thesis work was devoted to modeling release of drug from polymer coatings — different application, same idea.

    >>People love models, especially when they’re big, complex and quantitative. Models make us feel safe.

    >>(Instead, the confirmation bias seeps in and we devote way too much mental energy to proving the model true.)

    As you are probably aware, it’s not possible to prove a model to be true or false, although evidence can be accumulated that either supports or refutes aspects of the model. Models, like any scientific theory, need testing. The real problem comes in when researchers don’t ask the right scientific question when they test the model. Is the researcher looking for a model that accurately reflects an underlying mechanism (the cod model probably did) or is the researcher interested in an accurate prediction of the output (the cod model didn’t)? Often we have the opposite problem: a model can be constructed such that it produces incredibly accurate predictions, but the underlying mechanism has never been demonstrated, and so parameter extraction from those sorts of models is a shaky science.

    I think one of the biggest problems in current-day modeling is a failure to recognize that models must be tested. “Testing” does not mean “check whether my model predicts my output” or “verify the exact value of my parameters”. “Testing” means that experimental conditions must be varied and the output must be measured (which output you measure depends on whether you’re looking for an accurate mechanism or an accurate prediction). If the cod-modelers wanted to convince me of anything, they would have had to try fishing at a certain percentage for 5 years, check the population growth/decline, change the fishing percentage and check again.

    Of course nobody would ever do that. But that’s my point. Even though I’m in the business of modeling, and I think modeling is an extremely important scientific tool, we could all be a little more critical of the methods by which people generate models.

    Let me digress with a story. I recently attended an epidemiology talk where the speaker demonstrated that her data could be fit well by four models. Three models where the same model with slightly different assumptions about the start and end points of the data. The fourth model was empirical (using a SPLINE for goodness’ sake). All four models were used to estimate one parameter. The speaker — who is a leader in the field, with many awards and publications to their name — went so far as to conclude in writing, with a bolded, fun-colored powerpoint note, that “The use of four models confirms that this phenomenon is real.” My jaw almost dropped.

    I could fit any data you give me with an unlimited number of fancy models. For many of the models that I generate, I could give logical explanations of how they were constructed. I could ascribe their operators to real phenomenon. I could probably rationalize the parameter estimates. All of these activities can be performed for models that do not reflect the physics of the system or produce accurate predictions. It is to the detriment of good science that the purpose and testability of models is so widely misunderstood.

    (By the way, it’s ironic — though only to me — that you posted this today. I’m working on a manuscript that details the problems inherent in particular sorts of modeling. I haven’t worked on it in more than a month, and this morning, I opened it up for what will be its final edit.)

  2. #2 Colin
    October 28, 2008

    Essentially, all models are wrong, but some are useful.
    –George E.P. Box

    I am glad there are people out there like Rachael who live to discern which are useful and why; no easy task.

  3. #3 Rachael
    October 28, 2008

    Colin, what a great quote

    Here’s another (not as relevant, but amusing):

    “The best material model of a cat is another, or preferably the same, cat”
    A. Rosenblueth, Philosophy of Science, 1945

  4. #4 Raj
    October 28, 2008

    (My apologies in advance)

    Models coddle with rose-colored goggles, making people feeble to the risks of greed-evils.

    Sorry…couldn’t resist…

  5. #5 sniper609
    October 28, 2008

    Models are useful to help describe physical phenomena, especially based on the current laws of physics and your understanding of the phenomena. When testing suggests your model is wrong, you try to explain it physically.
    Some predictive models are based entirely on past correlations with zero or little understanding of the basis for that model, in effect an extrapolated curve fit of some behavior, something where the “science” is weak at best. The financial market, never factoring in greed and fraud, is a prime example today. Also, garbage in, garbage out is probably the most reasonable cause of the fishery problem not meeting expectations. And no one talks of the confidence bounds of their predictions, especially in the pseudo sciences.

  6. #6 Richard Simons
    October 28, 2008

    As I remember the events, fishery scientists were consistently recommending lower catch limits for the cod, but the Fisheries Minister (John Crosby for at least part of that time) was over-ruling them and allowing higher limits, on the grounds that the proposed limits would cause hardship to the people of the area. I do not recall any of the fishers or their representatives agreeing with the scientists’ estimates of the stocks so perhaps he could do little else and still keep his seat.

  7. #7 David
    October 29, 2008

    Excellent post. We seem to invest energy in models the way we do with ‘experts’ – in an effort to be able to rely on someone, something outside ourselve to make everything Ok.

  8. #8 Capa Dost
    October 29, 2008

    Criticisms of finanical models apply also to our models of global warming. We have no reason to assume that they are accurate. The problem may be worse or better than we believe. While the data we’re feeding the models is probably correct, we understand only parts of the underlying mechanism.

    BTW, I think reducing greenhouse gases would be an excellent idea, along with a general reduction in pollution. I’m just pointing out that we left-wing tree huggers need to be realistic about the models that seem to support our views. Although if Jonah is correct about the human inclination to discount information that clashes with our views, we probably won’t. :)

  9. #9 Miles
    October 29, 2008

    As a mathematician, I think that what you’re seeing is an example of “the false certainty of numbers.”

    If you tell people that you’re pretty sure about something, then they’ll doubt it. But if you attach a number to your hypothesis — preferably with a lot of extra and unjustified precision — they’ll believe you as unhesitatingly as seven-year-olds at their first communion.

  10. #10 milkshake
    October 29, 2008

    Johnny von Neumann: “With four parameters I can fit an elephant and with five I can make him wiggle his trunk.”

  11. #11 Alan
    October 29, 2008

    The failure at the heart of many models is the ludic fallacy, that is, assuming that components of real life will correlate in accordance with a given model under controlled assumptions. Originators or proponents of a model may then develop an ownership bias which favors a belief in its predictive power. Enter Karl Popper …

  12. #12 Shannon Murphy
    October 29, 2008

    Ohhhhh this is why I hate math. KIDDING.

  13. #13 John
    October 29, 2008

    So then, are we all ready to disavow the models which support the theory that burning fossil fuels causes global warming? Probability alone should tell us that they couldn’t possibly be correct, and likely not useful. The arguments above are just one more argument against.

  14. #14 Ben
    October 30, 2008

    ‘Probability alone should tell us that they couldn’t possibly be correct’.

    LOL. Good one. Game, set and match. Fire up those coal plants now!

  15. #15 Alan
    October 30, 2008

    Are we all ready to abandon the models which support the theory that burning fossil fuels does not cause global warming? Probability alone should tell us that these models couldn’t possibly be correct, and probably not useful.

  16. #16 Ben
    October 30, 2008

    I’m not sure I understand, Alan. You rewrote John’s post a little…but the message is still the same. Are you a bot?

    I’m reminded of David Ruelle’s book, Chance and Chaos. In the chapter on probabilities he points out the need for a physical theory of probabilities to give a prediction some operational meaning. Referring to ‘purist’ (frequentists?) who think a statement like ‘the probability it will rain this afternoon is .9′ is meaningless, he says:

    One might, however, be able to give it meaning, for instance by making a large number of numerical simulations on a computer (compatible with out present knowledge of the meteorological situation) and finding the proportion of cases in which the simulation gives rain. If one finds a probability of 90 percent for rain, even the purists will take their umbrellas.

    Beyond the issues of testing and trusting models, the example of cod fishing would point to the problem being less about the validity of particular predictions but in what actions we use those predictions to justify. There’s a very interesting book called ‘Making Better Environmental Decisions’ by Mary O’Brian where she makes the case that the ability (claim?) of risk assessors to accurately determine the risk of various alternatives unnecessarily narrows our decision making process to those alternatives.

  17. #17 Ben
    October 30, 2008

    In the article it says:
    “After all, there was no black swan event that triggered this most recent financial mess.”

    I haven’t read the Black Swan, but I was under the impression that for the financial models used to calculate risk the fall in the housing market in the US was a ‘black swan event’. I think Taleb’s point is not that meteorites may hit the earth or that extraordinary events may happen, but that the supposedly ‘smart’ models most risk managers use are based on very limited data and don’t consider many events that Jane-the-sensible-banker, with her ‘common sense’ and millions of years of mammalian paranoia, would probably lose sleep over.

  18. #18 Jordan Dawe
    October 30, 2008

    The cod story is totally wrong. Fisheries scientists have been trying to get the quotas lowered for years, but the government wouldn’t do it because of the job losses it would entail. I don’t know of any fisheries models that have been predicting stock increases for the last 50 years. They all predict decreases.

  19. #19 Alan
    October 30, 2008

    “I’m not sure I understand, Alan. You rewrote John’s post a little…but the message is still the same. Are you a bot?”

    Mine were the comments on the ludic fallacy, not on global warming, which appear to be copied from “John.” Maybe there’s (1) a bot on the loose, (2)somebody spoofing, or (3) someone else is using the same nom de plume as me.

  20. #20 hector
    October 30, 2008

    Haven’t read the Pilkeys’ book, but I’m suspicious of their argument, since it’s widely known in Canada that political factors led the government to keep allowing overfishing of the Grand Banks.

    One would also have to question whether government fisheries scientists, if indeed they were overoptimistic, were unconsciously or otherwise slanting their modeling in the direction both their masters (the government) and their clients (the fishing industry) clearly wanted to hear.

    One of the basic assumptions of any model that involves human interaction with anything should be that politics are always involved.

  21. #21 Ned
    November 17, 2008

    There were certainly a couple of fisheries scientists in the Canadian DFO (Department of Fisheries and Oceans) who more or less predicted the collapse of the cod fishery and caused great displeasure to their government bosses. A relevant quotation:

    “In 1997 two former DFO scientists published an article entitled, “Is Scientific Inquiry Incompatible with Government Information Control?” Their report and others cited a pattern of suppression of scientific information at DFO.

    The authors cited numerous examples where DFO scientists had warned the Minister that ground fish stocks were in a dangerous decline, and these findings were either ignored or suppressed as high quotas continued to be allocated. In one of these instances, a DFO scientist named Ransom Myers was apparently threatened with termination of his job when he concluded that the true cause of the cod collapse was simply human over-fishing rather than predation by seals.”

    More information on this here: http://xrl.us/oxkuc

  22. #22 örgü
    March 5, 2009

    The authors cited numerous examples where DFO scientists had warned the Minister that ground fish stocks were in a dangerous decline, and these findings were either ignored or suppressed as high quotas continued to be allocated.

    Thanks for the job ..

  23. #23 sohbet
    March 21, 2009

    here were certainly a couple of fisheries scientists in the Canadian DFO (Department of Fisheries and Oceans) who more or less predicted the collapse of the cod fishery and caused great displeasure to their government bosses. A relevant quotation:

    “In 1997 two former DFO scientists published an article entitled, “Is Scientific Inquiry Incompatible with Government Information Control?” Their report and others cited a pattern of suppression of scientific information at DFO.

  24. #24 club penguin
    May 23, 2009

    Some predictive models are based entirely on past correlations with zero or little understanding of the basis for that model, in effect an extrapolated curve fit of some behavior, something where the “science” is weak at best. The financial market, never factoring in greed and fraud, is a prime example today.

  25. #25 sohbet siteleri
    June 8, 2009

    Johnny von Neumann: “With four parameters I can fit an elephant and with five I can make him wiggle his trunk.”

    ok

  26. #26 dantel
    June 19, 2009

    “Models make us feel safe.” I think that’s the point.
    I read the article twice and found very useful. Thanks.

  27. #27 lida
    June 26, 2009

    Thank you so much for content, you would track;)

  28. #28 Kids Games
    September 1, 2009

    You draw a great comparison in layman’s terms, I am not mathematically inclined, so it was appreciated. Questioning drives Science forward, and I think that would be a good approach to these models. Question and test everything before you call it absolute.

  29. #29 sohbet
    September 2, 2009

    You draw a great comparison in layman’s terms, I am not mathematically inclined, so it was appreciated

  30. #30 Ball Bearings
    September 2, 2009

    The financial market, never factoring in greed and fraud, is a prime example today.

  31. #31 chat
    December 10, 2009

    Questioning drives Science forward, and I think that would be a good approach to these models. Question and test everything before you call it absolute.

  32. #32 sohbet
    December 10, 2009

    ome predictive models are based entirely on past correlations with zero or little understanding of the basis for that model, in effect an extrapolated curve fit of some behavior, something where the “science” is weak at best.

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  34. #34 Oyun Oyna
    February 24, 2010

    Johnny von Neumann: “With four parameters I can fit an elephant and with five I can make him wiggle his trunk.

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