Prevention not Recovery

Thomas Kuhn’s seminal work on scientific revolutions made the point through paradigms. Carlotta Perez’s work on industrial revolutions documented the point and W. Brian Arthur fully developed the point in his excellent book, The Nature of Technology: What It Is and How It Evolves. Technology emerges to solve the problems of the times. As Kuhn explained, first a technology is developed to solve a specific problem in one industry and then it is used to solve other problems and in other industries to become a paradigm.

There are two questions that you should be asking yourself as we sit here in the early 20th century, hopefully approaching the post-COVID era:

1. What is the problem we need to focus on?

2. What is the technology we need to solve that problem?

If you were thoughtful, you might ask what changes in thinking and learning may be required to address the problem with the new technology. This article briefly discusses the problem, the technology and the shift in thinking required.

The Problem

I have been surprised by how little discussion there has been about the lessons to be learned from the COVID-19 pandemic, especially the lessons beyond healthcare. I think the World Economic Forum shares my concerns. They have an excellent website, Strategic Intelligence, that exhaustively breaks down the major social issues at a level I have not seen elsewhere (see image above). Their article this week, What has COVID-19 taught us about flattening the climate curve?, prompted this writing. The WEF makes the point that we are consistently underspending for prevention and therefore defaulting to spending more for recovery. For example, the economic cost of Hurricane Katrina was $161 billion compared to the annual U.S. federal agency FEMA budget of $17 billion in 2019. Plentiful statistics easily make the U.S. the example, but this pattern is true for natural disasters around the world. We under spend even though we know these events and their catastrophe will reoccur. The WEF states the point well.

“To avoid another Hurricane Katrina or be taken by surprise when the next pandemic hits, we have to prioritize resources for prevention, not recovery. If we fail to learn this lesson from coronavirus, humanity will find itself flattened by the next curve it brings upon itself.”

We might conclude that the problem that we all need to face up to is learning, preferably on a coordinated worldwide basis, to prevent by reducing the consequences of the black swans. Black Swan is a term coined by Nassim Taleb to describe unforeseen events with extreme consequences. Do not be confused by my use of the terms “prevention” and “black swan” in the same sentence. Hurricanes are black swans when they are Katrina’s but we have fairly accurate models to predict the number of annual hurricanes in the Gulf of Mexico, Bangladesh or Japan and the most likely path of each storm that forms. Prevention, for example, in the case of hurricanes includes better building codes, early warning evacuation plans, prepositioning after storm supplies, annual maintenance on levees, etc.

Technology

If we think about prevention, an implicit underlying concept is the ability to predict the event(s). What technology do we have to predict complex events. What technology has emerged to do analytics, prediction and prescriptive analysis of future events. Answer — artificial intelligence (AI). AI is now approaching Kuhn’s paradigm status — solves multiple problems correctly, repeatedly, in multiple industries or applications. It has taken more than sixty years since the founding conference at Dartmouth in 1957 for AI to achieve its status as a paradigm. I believe AI has emerged and been perfected in part in order to provide the necessary tool kit to support improved prediction and prevention capabilities. The problems are clearly becoming more grave and more frequent — California forest fires, Fukushima, COVID-19 — and the social and economic costs make a recovery strategy untenable. The frequency of black swans is increasing as the world becomes more interconnected, which prompts the need to transition strategies. AI is a critical tool for mankind in this transition to prevention.

However, as the Rotman School of Management professors Agarwal, Gans, and Goldfarb make clear in their excellent book — Prediction Machines: The Simple Economics of Artificial Intelligence, we have not been trained to be predictors and we do not evaluate information routinely for its appropriateness as a basis for prediction. Fortunately, evolution programmed us to collect information instinctively to reduce uncertainty … about future events. What we need to do is make prediction and the necessary information collection explicit skills taught in schools beginning in K-12.[1]

Conclusion

As the Rotman professors wrote:

“Prediction facilitates decisions by reducing uncertainty, while judgment assigns value. In economists’ parlance, judgment is the skill used to determine a payoff, utility, reward, or profit. The most significant implication of prediction machines is that they increase the value of judgment.”

We need judgment to adopt prediction as a key tool in the 21st century in part to better understand and use AI, but more importantly to properly execute well thought out prevention strategies. We need desperately to adopt a new approach of prevention rather than recovery before we run out of the financial resources and the debt capacity to at least stabilize the most pressing problems of climate and pandemics.

[1] https://medium.com/@rhhfla/the-21st-century-renaissance-the-education-century-4518383521ed