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When the little buzzer went off after three minutes, I was (typically) still in the process of trying to explain to my bedazzled dating partner why my last name has three syllables (it’s Dutch).

As you can imagine, I did not find the love of my life.

If you’re still not entirely convinced, consider the fact that you are able to judge the appeal of a face in less than 13 milliseconds.

That’s right, research strongly suggests that your mind has decided on the attractiveness of a face before you are even consciously aware of the fact that you have seen one.

As a psychologist, I have always found the concept of speed dating fascinating.

In fact, some years ago, I decided to try it myself.

Other research has shown that more choices can cause people to avoid decisions and generally lead them to be less satisfied.

In essence, heuristics are decision-making tools that save effort by ignoring some information; and thus, their essential function is to reduce and simplify the processing of cues and information from our environment. In particular, prior research by Lenton and Francesconi suggests that when the number of potential speed-dating partners goes up, people tend to increasingly rely on heuristics in their decision making strategies.

Similarly, research on online dating performed by Alison Lenton and Barbara Fasolo indicated that participants presented with more potential partners did not experience any greater emotional satisfaction than participants presented with fewer options.

(They were, if anything, more confused about their choices.) These findings do not only pertain to the world of dating.

To illustrate, consider a popular heuristic that people often employ, the so-called “recognition heuristic.” The recognition heuristic states that “if one of two objects is recognized and the other is not then we should infer that the recognized object has the higher value.” Such a decision rule may sound overly simplistic but various studies have supported its use and effectiveness.

For example, in three studies predicting stock market performance, portfolios of stocks based on recognition (a constructed set of the most recognized stocks) outperformed (on average) managed funds, chance portfolios and stock expert predictions.

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