Bayesian love

On Valentine's Day prior probability

  • posted: 2019-02-14
  • updated: 2019-02-15
  • status: finished
  • confidence: low

Today is February 14th and I have some thoughts about the social contribution of the Valentine's Day concept.

One of the advantages of Valentine's Day, which remains fundamentally underestimated by many people, is that it's an agile way to produce strategic coordination aimed at detecting those who cheat at the game of love. First, I'll address the case of men, then women.

A "player" is a charming, intelligent man, capable of engaging in interesting conversation, implicitly presenting himself as a gentleman but who, in reality, leads women to fall into his seduction trap only to deliberately deceive them. He adopts this behavior solely to obtain sexual relations in exchange.

First, a significant portion of men on dating apps are players. They're only interested in non-exclusive relationships and casual sex. However, a vast majority of women want at least a minimum level of commitment in their relationships. The player disappoints by creating a sentimental illusion of being much more invested than he truly is.

The problem is that it's fairly difficult to detect a player, precisely because he excels in the art of deception. One behavioral signature of the player consists of courting multiple potential partners, implicitly or explicitly assuring each one of his manifest and unfailing affection. Without Orwellian surveillance, it's almost impossible to detect or prevent disillusionment.

The best countermeasure available in this type of situation is to designate a Schelling's point1, an evening universally elected and allocated to one's monogamous partner. Given that a person cannot be in two different places at the same time, players face a complication. For example, if Myriam, Julie, and Charlotte all think they are Pierre's girlfriend, our friend Pierre can only select one with whom to spend Valentine's Day. By the end of the day, the fraud will be brutally exposed. The principle of presence on Valentine's Day is a credible signal of exclusive romantic involvement. So even if it's a childish and silly holiday—even if your partner thinks the same—it's crucial to give it the importance it deserves. In case your partner creates an escape route, I invite you to update your Bayesian priors2. Does this mean there's cause for alarm and your partner is a confirmed player? Absolutely not. Nevertheless, if there have been suspicious precedents, it would be appropriate to raise a flag.

In case your partner creates an escape route, I invite you to update your Bayesian priors2. Does this mean there's cause for alarm and your partner is a confirmed player? Absolutely not. Nevertheless, if there have been suspicious precedents, it would be appropriate to raise the red flag.

There are two main excuses:

  • "I don't celebrate."
  • A last-minute impediment, by ultimate coincidence.

For my part, if I were a player, I would book a flight for February 14th, months in advance, which constitutes an honorable reason for absence.

  1. In game theory, a branch of mathematics, the Schelling point is a response given by two participants in a game who cannot communicate with each other, because it's the expected and natural answer. This concept was introduced by the economist Thomas Schelling and is also known as a "focal point." It refers to the solution that people tend to choose when they need to coordinate without communication. People gravitate toward these solutions because they seem natural, special, or relevant given the context of the problem. For example, if two people need to meet in New York City but can't communicate about the location or time, many would choose a landmark like Grand Central Station at noon. This becomes a Schelling point because it's prominent and naturally draws attention as a meeting place. ↩

  2. In Bayesian statistics, priors are probability distributions that represent your beliefs about an unknown parameter before seeing any data. They express what you think is likely or unlikely beforehand, based on previous knowledge or assumptions. When new evidence arrives, you update these prior beliefs to form posterior beliefs using Bayes' theorem. ↩↩