Fast and Frugal Heuristics
When making decisions in the real world, there is often a tradeoff between speed and accuracy. There is a whole spectrum of approaches that can be brought to bear on a problem, ranging from a simple gut feel decision to sophisticated statistics like running a nonlinear regression. If we have the necessary resources, is the latter always better? The Boston Consulting Group’s Strategy Institute describes what they call “fast and frugal heuristics”, and explains the situations in which such simple decision making strategies can be more effective than even sophisticated analytical techniques.
Since the Enlightenment, the main model of rational judgment in an uncertain world has been probability theory. The laws of probability, however, do not deal with the constraints in time, information, memory, and other resources that are characteristic of the decision making of actual humans (and machines). As a consequence, the underlying vision of rationality has been termed “unbounded rationality”—an omniscient and omnipotent fiction with little or no regard for the limitations in time, knowledge, and computational capacities that humans face. To make rationality more human than God-like, the concept of “bounded rationality” has been proposed. The key difference between unbounded and bounded rationality is the concept of limited search, to be defined by a stopping rule. The vision of bounded rationality, however, is not of one kind.
Rationality comes in many forms. The first split in Figure 2 separates models that assume the human mind has essentially unlimited demonic or supernatural reasoning power from those that assume we operate with only bounded rationality. There are two species of demons: those that exhibit unbounded rationality, and those that optimize under constraints. Optimization under constraints means optimization given various constraints, that is, limited resources such as attention, time, money, or information. The vision of constrained optimization is that minds would calculate the optimal trade-off between the benefits and costs of further search at regular time intervals, and stop search when the costs would outweigh the benefits. The rule “stop search when costs > benefits” sounds plausible at first glance, but a closer look reveals that this… can demand even more knowledge and computation than unbounded rationality.
There are also two main forms of bounded rationality: satisficing heuristics for searching through a sequence of available alternatives, and fast and frugal heuristics that use little information and computation to make a variety of kinds of decisions.
An example of an ignorance heuristic in action:
Let me illustrate the way this heuristic works with one example: Which US City Has More Inhabitants: San Diego or San Antonio? We posed this question to students at the University of Munich and the University of Chicago. The latter, who have a reputation for being among the most knowledgeable in the US, were correct 62% of the time. Yet 100% of the Germans got the correct answer 100% of the time. How did the Germans infer that San Diego was larger? All of the Germans had heard of San Diego, but many of them did not recognize San Antonio. They were thus able to apply the recognition heuristic and make a correct inference. The American students were not ignorant enough to be able to apply the recognition heuristic.

Treating Investors Like Customers