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- Win–stay,_lose–switch abstract "In psychology, game theory, statistics, and machine learning, win–stay, lose–switch (also win–stay, lose–shift) is a heuristic 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 (losses). 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.A large-scale empirical study of players of the game rock, paper, scissors shows that a variation of this strategy is adopted by real-world players of the game, instead of the Nash equilibrium strategy of choosing entirely at random between the three options.".
- Win–stay,_lose–switch wikiPageID "14082746".
- Win–stay,_lose–switch wikiPageLength "2306".
- Win–stay,_lose–switch wikiPageOutDegree "15".
- Win–stay,_lose–switch wikiPageRevisionID "653982245".
- Win–stay,_lose–switch wikiPageWikiLink Altruism.
- Win–stay,_lose–switch wikiPageWikiLink Bandit_problem.
- Win–stay,_lose–switch wikiPageWikiLink Bounded_rationality.
- Win–stay,_lose–switch wikiPageWikiLink Category:Computational_learning_theory.
- Win–stay,_lose–switch wikiPageWikiLink Category:Game_theory.
- Win–stay,_lose–switch wikiPageWikiLink Category:Heuristics.
- Win–stay,_lose–switch wikiPageWikiLink Evolutionary_game_theory.
- Win–stay,_lose–switch wikiPageWikiLink Game_theory.
- Win–stay,_lose–switch wikiPageWikiLink Heuristic.
- Win–stay,_lose–switch wikiPageWikiLink Machine_learning.
- Win–stay,_lose–switch wikiPageWikiLink Multi-armed_bandit.
- Win–stay,_lose–switch wikiPageWikiLink Nash_equilibrium.
- Win–stay,_lose–switch wikiPageWikiLink Prisoners_dilemma.
- Win–stay,_lose–switch wikiPageWikiLink Psychology.
- Win–stay,_lose–switch wikiPageWikiLink Rock,_paper,_scissors.
- Win–stay,_lose–switch wikiPageWikiLink Rock-paper-scissors.
- Win–stay,_lose–switch wikiPageWikiLink Statistics.
- Win–stay,_lose–switch wikiPageWikiLinkText "Win–stay, lose–switch".
- Win–stay,_lose–switch wikiPageWikiLinkText "win–stay, lose–switch".
- Win–stay,_lose–switch hasPhotoCollection Win–stay,_lose–switch.
- Win–stay,_lose–switch wikiPageUsesTemplate Template:Gametheory-stub.
- Win–stay,_lose–switch wikiPageUsesTemplate Template:Reflist.
- Win–stay,_lose–switch subject Category:Computational_learning_theory.
- Win–stay,_lose–switch subject Category:Game_theory.
- Win–stay,_lose–switch subject Category:Heuristics.
- Win–stay,_lose–switch comment "In psychology, game theory, statistics, and machine learning, win–stay, lose–switch (also win–stay, lose–shift) is a heuristic 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.".
- Win–stay,_lose–switch label "Win–stay, lose–switch".
- Win–stay,_lose–switch sameAs m.03cszmz.
- Win–stay,_lose–switch sameAs Q8026660.
- Win–stay,_lose–switch sameAs Q8026660.
- Win–stay,_lose–switch wasDerivedFrom Win–stay,_lose–switch?oldid=653982245.
- Win–stay,_lose–switch isPrimaryTopicOf Win–stay,_lose–switch.