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Reinforcement learning in professional basketball players

  • Tal Neiman
  • Yonatan Loewenstein
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    Reinforcement learning in complex natural environments is a challenging task because the agent should generalize from the outcomes of actions taken in one state of the world to future actions in different states of the world. The extent to which human experts find the proper level of generalization is unclear. Here we show, using the sequences of field goal attempts made by professional basketball players, that the outcome of even a single field goal attempt has a considerable effect on the rate of subsequent 3 point shot attempts, in line with standard models of reinforcement learning. However, this change in behaviour is associated with negative correlations between the outcomes of successive field goal attempts. These results indicate that despite years of experience and high motivation, professional players overgeneralize from the outcomes of their most recent actions, which leads to decreased performance.

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    File URL: http://ratio.huji.ac.il/sites/default/files/publications/dp593.pdf
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    Paper provided by The Federmann Center for the Study of Rationality, the Hebrew University, Jerusalem in its series Discussion Paper Series with number dp593.

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    Length: 16 pages
    Date of creation: Dec 2011
    Date of revision:
    Publication status: Published in Nature Communications 2:569.
    Handle: RePEc:huj:dispap:dp593
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    1. Ignacio Palacios-Huerta & Oscar Volij, . "Experientia Docet: Professionals Play Minimax In Laboratory Experiments," Economic theory and game theory 019, Oscar Volij.
    2. Drew Fudenberg & Jean Tirole, 1991. "Game Theory," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262061414, June.
    3. Mark Walker & John Wooders, 2001. "Minimax Play at Wimbledon," American Economic Review, American Economic Association, vol. 91(5), pages 1521-1538, December.
    4. Pavlo Blavatsky, 2003. "Note on "Small Feedback-based Decisions and Their Limited Correspondence to Description-based Decisions"," CERGE-EI Working Papers wp218, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    5. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-81, September.
    6. P.-A. Chiappori, 2002. "Testing Mixed-Strategy Equilibria When Players Are Heterogeneous: The Case of Penalty Kicks in Soccer," American Economic Review, American Economic Association, vol. 92(4), pages 1138-1151, September.
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