While we are inherently used to dealing with risk in our economic activities, few are prepared for total unpredictability. The world has been devising and finding new techniques to deal with risk, but what was totally ignored is uncertainty, a grim reminder of which is the unimaginable conditions — all harking back to this fundamental theme of uncertainty that defines the human condition induced by COVID-19.

Frank Knight, an economist, formalised a distinction between risk and uncertainty in his 1921 book, Risk, Uncertainty, and Profit. In his classic book, Knight introduced a distinction between measurable uncertainty, which he called “risk,” and “true uncertainty,” which cannot “by any method be reduced to an objective, quantitatively determined probability.”

The risk applies to situations where we do not know the outcome of a given situation, but can accurately measure the odds. Uncertainty, on the other hand, applies to situations where we cannot know all the information we need in order to set accurate odds in the first place.

In the real world, all events are so complex that forecasting is always a matter of grappling with “true uncertainty,” not risk; past data used to forecast risk may not reflect current conditions, anyway. The distinction of risk vs uncertainty as made by Knight has important implications for policy selection. Assuming the former when the latter is relevant can lead to wrong decisions.

Knight’s distinction about risk and uncertainty may still help us analyse the recent behaviour of Coronavirus and COVID-19. World Health Organisation (WHO) and Governments regarded their own apparently precise risk assessments as trustworthy may have thought they were operating in conditions of Knightian risk, where they could judge the odds of future outcomes.

Decision making under risk relies on known probability distributions of outcomes. Policy design becomes then a question of identifying the most likely occurrence given the underlying models, and applying measures that optimise the outcome. Risks around those most likely occurrences are described probabilistically, and confidence in one’s actions in turn is best captured with statistical intervals. However, probabilities are measures of frequencies of events that have happened in the past, and therefore, in real-time we are not necessarily confident that they represent accurate descriptions of the future.

The lesson that the 2007 financial crisis has taught us is that even though models do serve us satisfactorily most of the time, there will be times that they fail us, and they may even fail us spectacularly. It is on these occasions that probabilities do not provide a reliable assessment of, or confidence about, the outcomes. Relying on them provides a false sense of security that can lead to wrong policy decisions.

Recall that the phrase “black swan” gained currency a decade ago during the Great Recession and aftermath. A black swan is an unpredictable event that is beyond what is normally expected of a situation and has potentially severe consequences. Black swan events are characterised by their extreme rarity, their severe impact, and the widespread insistence they were obvious in hindsight. The coronavirus is carrying an extreme impact, in terms of human lives, dislocation, and economic losses. It’s the new Black Swan. 

Under risk, we are confident — at least probabilistically — of the underlying model or combination of models that describe the scenario. This is not possible under Knightian uncertainty because one lacks knowledge of the underlying distributions. The planner starts with a number of models that may be relevant, but cannot identify the likelihood with which they describe the situation. A family of possible models are considered, without assigning probabilities to their occurrence. Then that model is identified which, if true, would result in a worse outcome than any other model in the family. Policy is designed to minimise this maximally bad outcome. The appeal of this technique is that it provides insurance against the worst anticipated outcome.

In the case of COVID-19, the models used are based on several dangerous global outbreaks from the past two decades: SARS in 2004, H1N1 in 2009, the Ebola outbreak in 2015, and even the Spanish flu in 1918.

Knight’s non-probabilistic “true uncertainty” requires very different management than is required for handling probabilistic risk and therefore, we are seeing a lot of confusions and different opinions everywhere regarding lockdown, the release of lockdowns, etc.

Many of us are criticising the government. Maybe there’s a trade off between efficiency and flexibility that is important, especially when the situation is volatile. It may be wise to achieve more flexibility in times like these, even if it may be at the cost of little more efficiency. We need to be patient and cooperate with the government decisions at this moment until a vaccine is found and the disease is controlled.