The Win-Stay, Lose-Shift algorithm, developed by mathematician Herbert Robins in 1952, is a decision-making strategy that uses the metaphor of slot machines. The method involves choosing a machine at random and playing it until you lose, then switching to another machine. This strategy has been proven to be more reliable than chance. It differs from other strategies such as the 37% rule, which involves allotting 37% of your time to research before making a decision, and the explore/exploit trade-off, which requires balancing the tried and tested with the new and risky.

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The concept of over exploration can be mitigated in decision making by implementing strategies such as the 37% rule, the explore/exploit trade-off, and the Win-Stay, Lose-Shift algorithm. The 37% rule suggests that one should spend 37% of their time researching before making a decision, then commit to the next best choice they find. The explore/exploit trade-off emphasizes the need to balance the tried and tested with the new and risky. The Win-Stay, Lose-Shift algorithm, developed by mathematician Herbert Robins, suggests that one should stick with a decision until it proves unsuccessful, at which point they should switch to a different approach.

The effectiveness of the 37% rule can be influenced by several factors. Firstly, the amount of time you have to make decisions can greatly impact the effectiveness of this rule. If you have a limited amount of time, you may not be able to fully utilize the 37% of the time for research. Secondly, the nature of the decision can also affect the rule's effectiveness. If the decision involves high risks, you may need to spend more than 37% of your time on research. Lastly, personal factors such as your ability to make quick decisions and your risk tolerance can also influence the effectiveness of the 37% rule.

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Algorithms to Live By: The Computer Science of Human Decisions by Brian Christian and Tom Griffiths

Can computer science teach us the secrets of life? Perhaps not, but they can shed light on how certa...

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