Learning strategic environments: an experimental study of strategy formation and transfer
I present an experiment on learning about a game in an initially unknown environment. Subjects play repeatedly simple 2 × 2 normal-form coordination games. I compare behavioral learning algorithms for different feedback information. Minimal feedback only informs about own payoffs, while additional f...
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| Published in | Theory and decision Vol. 71; no. 4; pp. 539 - 558 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Boston
Springer US
01.10.2011
Springer Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0040-5833 1573-7187 |
| DOI | 10.1007/s11238-011-9266-3 |
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| Abstract | I present an experiment on learning about a game in an initially unknown environment. Subjects play repeatedly simple 2 × 2 normal-form coordination games. I compare behavioral learning algorithms for different feedback information. Minimal feedback only informs about own payoffs, while additional feedback informs about own payoffs and the opponent’s choice. Results show that minimal feedback information leads to a myopic learning algorithm, while additional feedback induces non-myopic learning and increases the impulse with which players respond to payoff differences. Finally, there is evidence for a strategy transfer across games which differ only according to the relabel of actions, but not according to permutation in the payoff matrix. |
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| AbstractList | I present an experiment on learning about a game in an initially unknown environment. Subjects play repeatedly simple 2 × 2 normal-form coordination games. I compare behavioral learning algorithms for different feedback information. Minimal feedback only informs about own payoffs, while additional feedback informs about own payoffs and the opponent’s choice. Results show that minimal feedback information leads to a myopic learning algorithm, while additional feedback induces non-myopic learning and increases the impulse with which players respond to payoff differences. Finally, there is evidence for a strategy transfer across games which differ only according to the relabel of actions, but not according to permutation in the payoff matrix. I present an experiment on learning about a game in an initially unknown environment. Subjects play repeatedly simple 2 × 2 normal-form coordination games. I compare behavioral learning algorithms for different feedback information. Minimal feedback only informs about own payoffs, while additional feedback informs about own payoffs and the opponent's choice. Results show that minimal feedback information leads to a myopic learning algorithm, while additional feedback induces non-myopic learning and increases the impulse with which players respond to payoff differences. Finally, there is evidence for a strategy transfer across games which differ only according to the relabel of actions, but not according to permutation in the payoff matrix.[PUBLICATION ABSTRACT] I present an experiment on learning about a game in an initially unknown environment. Subjects play repeatedly simple 2_× 2 normal-form coordination games. I compare behavioral learning algorithms for different feedback information. Minimal feedback only informs about own payoffs, while additional feedback informs about own payoffs and the opponent's choice. Results show that minimal feedback information leads to a myopic learning algorithm, while additional feedback induces non-myopic learning and increases the impulse with which players respond to payoff differences. Finally, there is evidence for a strategy transfer across games which differ only according to the relabel of actions, but not according to permutation in the payoff matrix. Reprinted by permission of Springer |
| Author | Nicklisch, Andreas |
| Author_xml | – sequence: 1 givenname: Andreas surname: Nicklisch fullname: Nicklisch, Andreas email: andreas.nicklisch@wiso.uni-hamburg.de organization: Max Planck Institute for Research on Collective Goods, University of Hamburg |
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| Cites_doi | 10.1016/S0899-8256(03)00183-0 10.1002/bs.3830260306 10.1016/0022-1031(66)90078-3 10.1016/0167-2681(93)90072-W 10.1016/S0167-4870(01)00050-2 10.1016/j.jet.2005.12.008 10.1006/game.1994.1037 10.1016/0899-8256(91)90005-Y 10.1007/s11238-007-9053-3 10.1006/game.1995.1002 10.2307/2171898 10.1111/j.1468-2354.2004.00122.x 10.1007/s00199-007-0209-8 10.1111/1468-0262.00316 10.1016/j.jebo.2004.08.002 10.1006/jeth.2002.2927 10.1016/S0899-8256(02)00549-3 10.2307/2951699 10.1111/1468-0297.00418 10.1006/game.1999.0786 10.1038/364056a0 10.1016/0167-2681(86)90021-1 10.1080/00207720701792131 10.1016/j.geb.2007.05.007 10.1037/0022-3514.32.2.311 10.1037/h0045737 10.1007/s00191-003-0150-3 10.1162/rest.90.3.414 10.1007/s10683-006-9159-4 10.1111/1468-0262.00054 10.2139/ssrn.682325 10.3998/mpub.9739 10.1037/h0093819 |
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| Keywords | Feedback information D84 Experimental economics Decision algorithm D83 Learning about the game Coordination Normal form Empirical method Feedback regulation Game theory Learning Permutation matrix Economy Behavioral analysis Feedback Player strategy Learning algorithm Impulse |
| Language | English |
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| Title | Learning strategic environments: an experimental study of strategy formation and transfer |
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