A reluctant algorithm is an algorithm that attempts to solve a problem slowly as opposed to the usual goal of solving problems quickly. They aren't necessarily the type of thing that you would try to build but are more of a theoretical construct. In some cases, you may want to know the slowest possible way to solve a problem. In order to answer this question, you can look at various reluctant algorithms to see which is the slowest. The only rules are that reluctant algorithms must continue working on the problem, make progress, not go backwards and eventually solve the problem. Reluctant algorithms have applications in areas such as artificial stupidity where you want to slow things down to human speed. For example, a game bot that doesn't win every time.
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An algorithm that is inefficient without taking a break or going backwards that makes progress and eventually solves a problem.
Modeling worst-case scenariosCrafting algorithms that interact at human-speed in contexts such as conversation or games.
Calculating whether an algorithm is really thinking or if it has failed when you don't have access to its code.Negative uses such as malicious compliance and resistance to change.
This is the complete list of articles we have written about algorithms.
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