“What chiefly distinguishes the empirical research on decision making and problem solving from the prescriptive approaches derived from SEU [subjected expected utility] theory is the attention that the former gives to the limits on human rationality. These limits are imposed by the complexity of the world in which we live, the incompleteness and inadequacy of human knowledge, the inconsistencies of individual preference and belief, the conflicts of value among people and groups of people, and the inadequacy of the computations we can carry out, even with the aid of the most powerful computers. The real world of human decisions is not a world of ideal gases, frictionless planes, or vacuums. To bring it within the scope of human thinking powers, we must simplify our problem formulations drastically, even leaving out much or most of what is potentially relevant.”
Herbert Simon expressed these words in a 1986 briefing panel concerning decision-making and problem-solving. His statement admonishes us of the non-linearity of life in such process. We never possess complete information or perfect foresight. Yet, this does not hinder us from making decisions and expressing our beliefs. However, it is imperative we recognize our limitations.
One reason why, relatively-speaking, only a handful of people ever saw the financial crisis occurring relates to the mischaracterization of reality. In my view, I don’t believe there was ever a full understanding of what actually constitute risk and how that’s different from uncertainty. Risk is uncertainty that can be quantified. One measure of it might be “standard deviation”. Uncertainty, on the other hand, cannot be quantified. I think of this like a “black swan” or “fat tail” events. In many instances of business activity, it is the “unquantifiable” that poses the major risk. How can one quantify gov’t policy or geopolitical factors that are inherently non-numeric? Very difficult. Even if one could estimate it, chances are the prognosis may not be entirely accurate.
Business and financial models use parameters, which in many instances, “leave out much or most of what is potentially relevant.” This continues to be true today. This applies to me too. That is why I add that my predictions could be worse than I propose. The alternative—a better than expected scenario—is highly unlikely because I have already accounted what I believe is potentially relevant.