Computer models and cognitive failure
Filed Under: Environmental News, Green Politics on October 11, 2011
Even before computers, there were problems with models. A physicist’s testimony to the US House of Representatives on climate change (pdf) provides a lovely historical example:
Modelers have been wrong before. One of the most famous modeling disputes involved the physicist William Thompson, later Lord Kelvin, and the naturalist Charles Darwin. Lord Kelvin was a great believer in models and differential equations. Charles Darwin was not particularly facile with mathematics, but he took observations very seriously. For evolution to produce the variety of living and fossil species that Darwin had observed, the earth needed to have spent hundreds of millions of years with conditions not very different from now. With his mathematical models, Kelvin rather pompously demonstrated that the earth must have been a hellish ball of molten rock only a few tens of millions of years ago, and that the sun could not have been shining for more than about 30 million years. Kelvin was actually modeling what he thought was global and solar cooling. I am sorry to say that a majority of his fellow physicists supported Kelvin. Poor Darwin removed any reference to the age of the earth in later editions of the “Origin of the Species.” But Darwin was right the first time, and Kelvin was wrong. Kelvin thought he knew everything but he did not know about the atomic nucleus, radioactivity and nuclear reactions, all of which invalidated his elegant modeling calculations.
One of the more mordantly amusing aspects of the current credit crisis is the massive failure of relying on computer models for assessing risk. A failure that was quite comprehensive:
In fact, most Wall Street computer models radically underestimated the risk of the complex mortgage securities … The people who ran the financial firms chose to program their risk-management systems with overly optimistic assumptions and to feed them oversimplified data. This kept them from sounding the alarm early enough.
Paul Volker has been publicly scathing about such financial engineering. A make-believe universe where spreadsheets were oracles and bigger and better computer models bigger and better oracles.
Even worse, under Basel II computer models became part of the regulatory framework:
Instead of applying a uniform standard (such as a specific debt to equity ratio) to all financial institutions, Basel II contemplated that each regulated financial institution would develop its own individualized computer model that would generate risk estimates for the specific assets held by that institution and that these estimates would determine the level of capital necessary to protect that institution from insolvency. But in generating this model and crunching historical data to evaluate how risky its portfolio assets were, each investment bank gave itself a discretionary opportunity to justify higher leverage. Because each model was ad hoc, specifically fitted to a unique financial institution, no team of three SEC staffers was in a position to contest these individualized models or the historical data used by them. Thus, the real impact of the Basel II methodology was to shift the balance of power in favor of the management of the investment bank and to diminish the negotiating position of the SEC’s staff. Basel II may offer a sophisticated tool, but it was one beyond the capacity of the SEC’s largely legal staff to administer effectively.
Financial institutions used highly sophisticated computer models, put together by highly-paid people using masses of data based on what was taken to the most up-to-date understanding of how things work. All of which gave the output of the models huge credibility.
The problem was precisely that they had such credibility. In particular, their output was treated as empirical evidence: as telling people about the state of their risk exposure.
They did nothing of the kind. All they did—all computer models can ever do—is tell you the consequences of your premises, both empirical and analytical/causal. They do not tell you about how the world is. They tell you about how you think the world is. One can then test your thinking about the world by comparing what your model(s) churn out to how the world turns out to be.
But this is a distinction that is very, very easy to lose sight of. Particularly given the effort and analytical power put into creating the things and their “black box”—facts in one end, results out the other end—nature.
