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Sunday, February 12, 2017

Dealing with uncertainty and predicting the future

The NYT this moring published a piece by John Lancaster titled "The major blind spots in macroeconomics". It featured a talk given by Andrew Haldane, the Chief Economist of the Bank of England (Haldane talk), "The Dappled World". The the title of this work in turn was borrowed from work of Nancy Cartwright:
One of the potential failings of the economics profession, I will argue, is that it may have borrowed too little from other disciplines - a methodological mono-culture. In keeping with this spirit, the title of my lecture is itself borrowed. In 1999 Professor Nancy Cartwright, a philosopher of science, published a book with the title The Dappled World: A Study of the Boundaries of Science (Cartwright (1999)). This quote captures its essence:
“Science as we know it is apportioned into disciplines, apparently arbitrarily grown up; governing different sets of properties at different levels of abstraction; pockets of great precision; large parcels of qualitative maxims resisting precise formulation; erratic overlaps; here and there, once in a while, corners line up but mostly ragged edges; and always the cover of law just loosely attracted to the jumbled world of material things.” 
The criticism of the macroeconomics and of science in general seems to have substantial relevance to medicine. Dr. Cartwright's description of the apportionment of disciplines and erratic overlaps describes my world.

Haldane's speech also highlights the abject failure of macroeconomics to  predict the near catastrophic economic events of the early 21st century. The models employed all failed to identify the 2008 downturn. This was especially disconcerting since the major purpose of the field is to anticipate these events. Macroeconomics has a problem with dealing with uncertainty. However, they are not alone in having to deal with uncertainty.

Medicine has a similar set of issues but we have elected to take a somewhat different but perhaps equally flawed approach. Whereby mainstream economics has taken a somewhat pollyannish approach and consistently fails to predict economic catastrophes (or near catastrophes), we in medicine make them all the time, even when they are not likely to happen. We do deal with different frequencies. There are lots of people in the word and for each of us, we will ultimately deal with our own personal catastrophes. Major depressions affecting large populations occur very infrequently when compared to major health events affecting single humans.

Economists are not called upon to predict the financial fates of single humans but those of us in the health care world are called upon to make predictions about the near term and longer term fates of individuals. However, whether looking at populations or single individuals, we are saddled with the same broad issues: uncertainty and inherent problems with predicting the future.

It seems that economist tend to err on the side of optimism and are not able to consistently predict low frequency catastrophic events. On the other hand, physicians tend to err on the side of over-predicting the likelihood of individual catastrophes. We do have the advantage (disadvantage) of being always right over the longer term for any given individual. However, timing is important. Devoting resources now for a potential catastrophe which may occur tomorrow or 50 years from now has real costs. There are likely different decisions we all would make if we knew the future better that we now know.

What are the prospects of knowing the future better? I think they are likely pretty high but this improved knowledge will require both more population based data and a broader understanding of what frequencies mean. This data will be meaningless to people unless they are able to view information through a different and quantitative lens. I also think this will require some degree of reorganization of how we view expertise. The "dappled nature" of science and health care delivery creates barriers to understanding and interpreting the data needed to make sense of a complex and uncertain world.  That may be the larger challenge we face.

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