In psychology, game theory, statistics, and machine learning, Win-Stay, Lose-Switch (also Win-Stay, Lose-Shift) is a learning strategy used to model learning in decision situations. It was first invented as an improvement over randomization in bandit problems. It was later applied to the prisoner's dilemma in order to model the evolution of altruism.

The learning rule bases its decision only on the outcome of the previous play. Outcomes are divided into successes (wins) and failures (loses). If the play on the previous round resulted in a success, then the agent plays the same strategy on the next round. Alternatively, if the play resulted in a failure the agent switches to another action.

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