When computer models create mayhem

As an international banker who finances highly structured multimillion loans in emerging markets for power projects, railroads, toll roads, port expansions, etc., we use sophisticated financial models to predict outcomes and determine areas of potential weakness (stress testing) in order to appropriately underwrite transactions to protect lenders and investors. Often hundreds of millions — or even billions — of dollars ride on these financial projections, and their use is considered critical to making the investment decision. However, in three decades of using computer models, seldom have I seen them accurately project ultimate outcomes. Their real value is determining possible changes in the inputs and how they can impact the project, along with other quantitative and qualitative analysis.

Some of the current hysteria for the coronavirus has undoubtedly been fed by similar sophisticated healthcare computer models, most notably the Imperial College’s doomsday predictions which indicated that as many as 500,000 deaths could occur in Britain and over 2 million in the U.S. After several public policy actions had already been put into place by  governments — at least in part because of computer models such as the credible scientific U.K. forecast — the projections were abruptly revised down to show fewer than 20,000 U.K. deaths were likely to occur from the virus and 200,000 in the U.S.

Those of us who use computer modeling on a daily basis to assist in our analysis know how dynamic projections can be. These types of dramatic changes are routine when there are significant changes to the inputs that go into the models. In fact, that is the reason for modeling: determining the parameters of the inputs and how they affect the underlying outcomes.

Unfortunately, a superficial, agenda-driven press, which universally reports on outcomes from these imperfect tools as “settled science,” inevitably does a disservice to the casual reader in an effort to generate news and affect government policy.

It was refreshing to have a credible scientist like Dr. Deborah Birx on the White House Coronavirus Task Force gently scold those who are promoting catastrophic outcomes from computer models when the evidence no longer supports it.

A good financial analyst knows that a computer model is dynamic and needs constant adjustments as new data becomes available. As the numbers, or data, come in differently than what was originally anticipated, models have to be changed — and sometimes even redone drastically — because the original assumptions weren’t correct or were based on limited or faulty data.

This becomes obvious when observing the predictive values of climate change models developed over the last several decades.

A few years ago a prominent science writer noted that “most temperature records show that since 1998 the [climate] models and observed average global temperatures have parted ways.  The temperature in the [climate] models continue to rise, while the real climate has refused to warm up much during the last 15 years.” One former NASA scientist goes as far as to say “global warming projections have a large element of faith programmed into them.” These are understatements when reviewing the reporting on global warming predictions over the last several decades. The “settled science” isn’t as exact as some scientists and most reporters would have us believe.

The same rush to a foregone conclusion is happening with the coronavirus. If the media had paid attention to what the scientific advisors have been saying from the beginning, they wouldn’t have been surprised by their skepticism with the computer models. In almost every press conference the doctors have acknowledged the lack of good data on this virus and that it would take time to make more accurate projections or models. Not unlike the financial models bankers work on, the coronavirus modeling needs better inputs to test the assumptions — and most probably significant adjustments to better project future outcomes.

To make dramatic policy decisions solely on the basis of very imperfect models can lead to bad life or death decisions.

As more data becomes available, the coronavirus models will be fine-tuned and will become better tools for policy makers. It will be important that — as this occurs — biases and faulty assumptions are teased out of the models to ensure their accuracy and to better inform policy makers. While this is all happening in a matter of weeks for the coronavirus, the same should apply to climate change models that have become almost sacrosanct over the last couple of decades.

Since science has now become the ultimate arbiter of how we determine most public policy debates adjudicated in the press, coronavirus forecasting is a cautionary tale to reporters who easily confuse what is science and what is conjecture.

Projecting the future — whether it be a financial model for a power project in Latin America, a global pandemic, or the future climate of the earth — is a tool that can be valuable but understandably flawed.

Alan Beard is Managing Director of Interlink Capital Strategies, a Washington, D.C. based financial advisory firm and fund manager focused on arranging structured and project financing in emerging markets. He has written several books and articles on international finance, been an adjunct professor at Georgetown University and advised various government agencies on international finance issues.