One fine day Richard Feynman left a restaurant. 50 years later we already know why better known bad than good unknown

In the late 1970s, the brilliant physicist Richard Feynman He went with his friend Ralph Leighton to eat at a Thai place named Indra in Glendale, California. Looking at the restaurant’s menu, Leighton couldn’t decide: should he order his usual favorite, ginger chicken, or try something new and perhaps better?

Any other person would have responded in one way or another (“if you like it so much, you better insure” or something like “he who does not risk does not gain”). Richard Feynman, brilliant as he is, did something else: He started scribbling equations on a napkin. and he turned that into a mathematical problem that he not only detected, but solved.

For some reason, the prodigious physicist never published that analysis, and his notes were left to Leighton. For years that story was forgotten, but 50 years later researchers from the universities of Oxford, New York and Princeton managed to rescue those notes and Feynman’s solution.

And what that revealed was surprising.

Rescuing Feynman’s restaurant problem

The researchers explained in their study, published in PNAS (Proceedings of the National Academy of Sciences) that although Feynman had focused on what happened to the different dishes in the same restaurant, they They preferred to expand the problem: what happens when we are in city X, for example, on vacation, and we want to choose a restaurant.

Richard Feynman’s handwritten notes on a restaurant napkin turned out to be a fascinating problem. Source: PNAS.

Feynman’s restaurant problem is actually a variant of what is known as the optimal stopping problemto which also belongs the famous variant of secretary problemwhich gave rise to the 37% rule: When choosing from 100 options, one should try the first 37 to maximize the chances of choosing the best one. Then you can “settle” for that one, because it is difficult for there to be a better one among the rest.

But we are digressing. Feynman’s original mathematical formula established an optimal policy based on a uniform distribution of quality. According to the physicist’s formulation, our quality bar is not static nor falls by chance, but decreases exponentially as the days available in our vacation calendar are exhausted.

Thus, it usually happens that when we are at the beginning of our vacation, We usually demand absolute perfection in the chosen restaurant because the remaining time allows the risk to be amortized. In the end, however, that threshold of demand collapses and we settle for a decent restaurant. We move from the exploration phase – taking risks with new places (or dishes) – to exploitation – repeating places (or dishes) that we liked.

The researchers wanted to test this mathematical model with a sample of 2,520 participants, and in doing so they detected a striking anomaly. During the first nights in a new city, participants explored massively, much more than mathematical logic itself advised.

The researchers discovered that this phenomenon responded to the so-called “early exploration bonus” that fell rapidly as the days went by: if we have an opportunity to “get it right,” our brain shows tremendous psychological resistance to tying itself to a restaurant at the first opportunity. We prefer to continue trying other restaurants because we trust that we will find a better one.

The four “gastronomic worlds” of the study: the behavior of the participants varied according to each distribution. Source: PNAS.

But as the experiment went on, something else was discovered. Humans are not blind robots, but we calibrate the bar according to the city we visit. The experiment placed participants in four different “food worlds” in which the ratio of excellent restaurants to mediocre (or decent) ones varied. The data showed that the human brain is capable of diagnosing the type of “food world” it finds itself in just by trying three or four restaurants. From there, set the bar.

Feynman mathematically intuited that the bar would lower exponentially as the return date approached, but the experiment revealed something different. Human beings reduce our level of demand linearly with respect to the proportion of days we have left on vacation. We are becoming less and less demanding and more “nostalgic”. This guarantees something important: that at least on the last nights we enjoy the “better bad known than good not known”, because that “bad known” will not be so bad after all: we have already experienced it.

Fascinating.

Image | SAP (edited with Magnific)

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