An algorithm, according to the website Math Vault, is “a finite series of well-defined, computer-implementable instructions to solve a specific set of computable problems.” Indeed, for most of us, the solution of everyday problems is based on the use of pragmatic linear algorithms: given A (a burned-out lightbulb, a soccer team that frequently loses, a marital crisis), the required course of action is B (replace the lightbulb, fire the coach, couples therapy, etc.). However, in contrast to replacing a lightbulb, firing a coach rarely solves the problem.
Life, in short, is not simple. Yet, most of us rely on astonishingly simplistic algorithmics for problem solving. The coronavirus offers us all an excellent opportunity to snap out of the simplistic linear illusion, certainly where medicine is concerned. The model to which we’ve been habituated – that a virus is treated with antibiotics, and that the treatment for diarrhea is a rice diet – doesn’t work when we encounter complex biological systems. In these situations, simplicity is supplanted by complexity. The division of viruses into good and bad has now been replaced by the insight that the disposition of microbes in our body does not determine only whether (linearly) we will develop a fever, but also (complexly) how much weight we’ll put on.
Stacey’s Matrix, formulated by the South African economist Ralph D. Stacey, provides a number of important insights into this complexity. To fight the coronavirus, we have all been requested, in the name of linear cause-and-effect relations, to lock ourselves in, so as “to flatten the curve.” This abruptly raised – through a controversial decision-making process – a far more complex curve: people forced into unemployment. Overnight, and without a 14-day incubation period, the economy, a no less complex field than medicine, was afflicted by a new ailment: the leave-without-pay sickness. Only time will tell who will cure it, when and how.
According to Stacey’s model, which originates in the world of management, there are two axes of reference that determine the latitude for decision-making processes. The X axis in the graph refers to the level of the resultant certainty of a particular event. It does not deal with future prediction or prophecy, but with evaluating a probabilistic situation. The more familiar we are with a past event, the easier it is to evaluate what is going to happen, and the less familiar we are, the more difficult it will be. If it’s a “black swan,” something we’ve never before encountered, it will be more difficult to evaluate what the new situation will look like.
Both those who believe that the coronavirus is at most another type of flu, and those who maintain that it’s the most serious epidemic since the Spanish flu, will be on the left side of the X axis; while those who maintain that the numbers and the data that are being bandied about are not reliable, and therefore it’s impossible to know what is really happening or will happen, will be far on the right side of the X axis.
The Y axis, in contrast, does not deal with future probability, but with the organizational agreement around the values of the present – and where the coronavirus is concerned, with each community’s social pact. The broader the consensus (for example, in Israel, “Do not cast me off in old age,” or “We expect our soldiers to defend us” are sentiments most people can identify with), as indicated on the lower part of the Y axis, the easier it is for society to make decisions, even if they entail a steep price.
And vice versa: the more that substantive and fundamental differences of values exist concerning the motives behind the decisions, concerning the correct point of balance between individual freedom and the common good, and concerning the personal-social price that we are liable to pay, the more difficult it is to arrive at universally agreed decisions (the upper part of the Y axis).
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In line with political need
Where would you say the government’s decision-making process lies these days? In the blue area or the purple area in the graph? Recall the warnings that are broadcast to us every night on television. Do we deserve an illusion of un-unequivocal, linear decision making (“Quarantine is the solution!”)? Or is it the latitude of uncertainty, in which decision making is naturally far more complex, that characterizes “the greatest crisis since…”? If so, wouldn’t it make sense for us to be hearing a far more modest message (“The truth is, we don’t know”)?
But governmental systems in our time always “know.” Cause-and-effect relations that are completely unconnected, and others that are closely interconnected because of the complexity of the system but which it’s convenient to ignore, are replaced, in accordance with political need, with the advice of experts from the system (the green area in the illustration). But with extreme events like epidemics, the know-how is acquired only after the fact. At such times, we need social and political psychologists who understand theories of crisis management, including impacts on mental health, no less than we need medical personnel who deal on a daily basis with entirely different aspects of public health.
Decision making that is motivated by risk aversion is fundamentally different from decision making that is motivated by hope. The asymmetry between them is clear and has been proved in innumerable studies in psychology and economics. As a society, it is incumbent on us to find the right balances between fear and hope. And not through experts who live in linear illusion, but through humble recognition of the complexity of systems. As a physician who has been occupied for many years with managing patients with complex cases, I have to say that not a day goes by without my recalling the immortal words of Sir William Osler: “Medicine is a science of uncertainty and an art of probability.” The eruption of the coronavirus also obeys that rule.
The obsessive occupation with the daily count of the infected and the dead, while giving insufficient weight to the personal economic tragedy, lasting many years on, endured by families and individuals who have lost their livelihoods, recalls the “McNamara fallacy.” The groundless paradigm – named for Robert McNamara, U.S. secretary of defense during the Vietnam War – which equated comparative body counts with military success, is an important lesson for all of us: Let us not take an interest in the national body count (a quantitative variable), while ignoring the psychological and economic price paid by individuals (qualitative variables). It took the United States many years to recover from the quantitative illusion of the Vietnam body count. We must not fall into the same trap.
Opher Caspi, M.D., is a physician specializing in internal and integrative medicine; he also holds a Ph.D. in psychology