Initial randomness amplified by social proof makes predicting the popularity of things tricky (restaurants are a good example)

πŸ’Ž Initial randomness amplified by social proof makes predicting the popularity of things tricky (restaurants are a good example)

Imagine two restaurants of comparable quality. Along came the first customer, who has to choose between the two he flips a coin and picks restaurant A. Now imagine the next customer. Confronted with the same choice, she has the same information plus she sees the first customer sitting in the window of restaurant A. What does she do?

You can see where this is going.

But at this point, restaurant B still has hope-how much does the second customer trust the first customer’s choice? Well, is he attractive? Does he smoke? How’s he dressed? What’s his posture? The more the second person identifies with the first, the more she trusts his choice.

Once the second customer chooses restaurant A too, it starts to solidify a consensus. The third customer would have to buck a significant trend, voting against two people, in order to choose restaurant B.

Soon, you can imagine a line put the door of restaurant A, while restaurant B sits empty – despite the restaurants’ similar quality.

Excerpt from: Brain Candy: Science, Paradoxes, Puzzles, Logic, and Illogic to Nourish Your Neurons by Garth Sundem

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