Reinforcement learning in professional basketball players
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.
|Date of creation:||Dec 2011|
|Date of revision:|
|Publication status:||Published in Nature Communications 2:569.|
|Contact details of provider:|| Postal: |
Web page: http://www.ratio.huji.ac.il/
More information through EDIRC
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Drew Fudenberg & Jean Tirole, 1991. "Game Theory," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262061414, June.
- 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.
- Ignacio Palacios-Huerta & Oscar Volij, .
"Experientia Docet: Professionals Play Minimax In Laboratory Experiments,"
Economic theory and game theory
019, Oscar Volij.
- Ignacio Palacios-Huerta & Oscar Volij, 2008. "Experientia Docet: Professionals Play Minimax in Laboratory Experiments," Econometrica, Econometric Society, vol. 76(1), pages 71-115, 01.
- Ignacio Palacios-Huerta & Oscar Volij, 2006. "Experientia Docet: Professionals Play Minimax in Laboratory Experiments," NajEcon Working Paper Reviews 122247000000001050, www.najecon.org.
- Mark Walker & John Wooders, 2001. "Minimax Play at Wimbledon," American Economic Review, American Economic Association, vol. 91(5), pages 1521-1538, December.
- 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.
- 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.
When requesting a correction, please mention this item's handle: RePEc:huj:dispap:dp593. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Ilan Nehama)
If references are entirely missing, you can add them using this form.