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Apart from the 37% rule, explore/exploit trade-off, and Win-Stay, Lose-Shift algorithm, other decision-making algorithms include the Multi-Armed Bandit algorithm, the Greedy algorithm, and the Monte Carlo Tree Search. These algorithms are used in various fields such as computer science, economics, and artificial intelligence to optimize decision-making processes.
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The "37% rule" refers to a series of steps, or algorithms, that someone must follow to make the best decision within a set amount of time. Someone allots 37% of their time to research before they make a decision, then commits to the very next "best choice" they find. The "explore/exploit" trade-off refers to the need to balance the tried and tested with the new and risky. The payoff of this algorithm depends entirely on how much time you have to make decisions. People are more likely to visit their favorite restaurant on their last night in town than risk something new. Developed in 1952 by mathematician Herbert Robins, the "Win-Stay, Lose-Shift" algorithm uses slot machines as a metaphor. Choose a machine at random and play it until you lose. Then switch to another machine; this method was proven to be more reliable than chance. A psychology study found that given choices, people often "over explore" rather than exploit a win. Given 15 opportunities to choose which slot machine would...
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Can computer science teach us the secrets of life? Perhaps not, but they can shed light on how certain everyday processes work and how to exploit them...
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