Perils of Modeling Complex Systems

I thought this article in the NY Times about the failure of models to accurately predict the progression of swine flu cases was moderately instructive.

In the waning days of April, as federal officials were declaring a public health emergency and the world seemed gripped by swine flu panic, two rival supercomputer teams made projections about the epidemic that were surprisingly similar — and surprisingly reassuring. By the end of May, they said, there would be only 2,000 to 2,500 cases in the United States.

May’s over. They were a bit off.

On May 15, the Centers for Disease Control and Prevention estimated that there were “upwards of 100,000” cases in the country, even though only 7,415 had been confirmed at that point.

The agency declines to update that estimate just yet. But Tim Germann, a computational scientist who worked on a 2006 flu forecast model at Los Alamos National Laboratory, said he imagined there were now “a few hundred thousand” cases.

We can take at least two lessons from this:

  • Accurately modeling complex systems is really, really hard.  We may have hundreds of key variables, and changes in starting values or assumed correlation coefficients between these variables can make enormous differences in model results.
  • Very small changes in assumptions about processes that compound or have exponential growth make enormous differences in end results.  I think most people grossly underestimate this effect.  Take a process that starts at an arbitrary value of “100” and grows at some growth rate each period for 50 periods.    A growth rate of 1% per period yields an end value of  164.  A growth rate just 1 percentage point higher of 2% per period yields a final value of  269.    A growth rate of 3% yield a final value of 438.  In this case, if we miss the growth rate by just a couple of percentage points, we miss the end value by a factor of three!

Bringing this back to climate, we must understand that the problem of forecasting disease growth rates is grossly, incredibly more simple than forecasting future temperatures.  These guys missed the forecast my miles of a process that is orders of magnitude more amenable to forecasting than is climate.  But I am encouraged by this:

Both professors said they would use the experience to refine their models for the future.

If only climate scientists took this approach to new observations.

  • Bill Drissel

    Guessing 2% when the right answer is 3% is not off by “just a coupla percentage points”, it’s off by 50%.

    Regards,
    Bill Drissel
    Grand Prairie, TX

  • Both professors said they would use the experience to refine their models for the future.

    If only climate scientists took this approach to new observations.

    Actually, some of them have. The problem is, their models have been demonstrated to be waaaay off the mark, enough so that they should be scrapped. But the climate scientists are so stuck in their reality that they think they only have to “tweak” their models, rather than rewrite them entirely.

    Ryan O. and Jeff Id were basically patted on the head and told to leave the adults alone after Ryan basically demolished the Steig Antarctic models a couple of weeks ago. Rather than take the criticisms seriously, and looking at how flawed his models are, Steig’s attitude was pretty much “go get published, and then come back and we’ll talk about how to ‘refine’ my models.”

    The fundamental flaw in all of the interactions with the AGW proponents is assuming good faith on their part. That is wrong, and they need to be called on it. You’ve gotten closer with your posts of yesterday. But it needs to be shouted from the rooftops at this point. They are charlatans, and are only interested in the power-grab that they are enabling. Period. Anyone challenging the orthodoxy gets shouted down and painted as an extremist.

    That has to stop.

  • Stevo

    “If only climate scientists took this approach to new observations.”

    What kind of dopy cunt thinks they would do anything else?

  • Another lesson that can be taken from this:

    “On May 15, the Centers for Disease Control and Prevention estimated that there were “upwards of 100,000” cases in the country, even though only 7,415 had been confirmed at that point.”

    Estimates are not empirical data. Government estimates are double-plus ungood empirical data. And an estimate confirmed by the NY Times’ citing the “imagining” of some guy who did a model once is just too silly for words.

    The model output is far, far closer to the (incomplete so far) empirical observations than the estimates are. I’m just sayin’.

  • Cover Me, Porkins

    When has science ever successfully predicted a phenomenon whose occurrence has never been observable, but only extrapolative? Never? At least only rarely. Not with something as complex and currently inscrutable as climate.

    I didn’t think I would grow up to see so-called experts in sacrificial worship to a concept that hid somewhere waiting to devour the world. Global warming, Cthulhu, same difference.