We introduce a new solution concept for games in extensive form with perfect information, valuation equilibrium, which is based on a partition of each player's moves into similarity classes. A valuation of a player is a real-valued function on the set of her similarity classes. In this equilibrium each player's strategy is optimal in the sense that at each of her nodes, a player chooses a move that belongs to a class with maximum valuation. The valuation of each player is consistent with the strategy profile in the sense that the valuation of a similarity class is the player's expected payoff, given that the path (induced by the strategy profile) intersects the similarity class. The solution concept is applied to decision problems and multi-player extensive form games. It is contrasted with existing solution concepts. The valuation approach is next applied to stopping games, in which non-terminal moves form a single similarity class, and we note that the behaviors obtained echo some biases observed experimentally. Finally, we tentatively suggest a way of endogenizing the similarity partitions in which moves are categorized according to how well they perform relative to the expected equilibrium value, interpreted as the aspiration level.
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- Jehiel, Philippe & Samet, Dov, 2005.
"Learning to play games in extensive form by valuation,"
Journal of Economic Theory,
Elsevier, vol. 124(2), pages 129-148, October.
- Philippe Jehiel & Dov Samet, 2010. "Learning to play games in extensive form by valuation," Levine's Working Paper Archive 391749000000000040, David K. Levine.
- Philippe Jehiel & Dov Samet, 2001. "Learning To Play Games In Extensive Form By Valuation," NajEcon Working Paper Reviews 391749000000000010, www.najecon.org.
- Philippe Jehiel & Dov Samet, 2001. "Learning to play games in extensive form by valuation," Game Theory and Information 0012001, EconWPA.
- Philippe Jehiel & Dov Samet, 2010. "Learning To Play Games In Extensive Form By Valuation," Levine's Working Paper Archive 391749000000000034, David K. Levine.
- Philippe Jehiel & Dov Samet, 2001. "Learning To Play Games In Extensive Form By Valuation," Levine's Working Paper Archive 391749000000000010, David K. Levine.
- David M Kreps & Robert Wilson, 2003.
Levine's Working Paper Archive
618897000000000813, David K. Levine.
- Fudenberg, Drew & Levine, David, 1998.
"Learning in games,"
European Economic Review,
Elsevier, vol. 42(3-5), pages 631-639, May.
- Ariel Rubinstein, 2010.
"Perfect Equilibrium in a Bargaining Model,"
Levine's Working Paper Archive
252, David K. Levine.
- Philippe Jehiel, 2005.
"Analogy-Based Expectation Equilibrium,"
784828000000000106, UCLA Department of Economics.
- Rosenthal, Robert W., 1981. "Games of perfect information, predatory pricing and the chain-store paradox," Journal of Economic Theory, Elsevier, vol. 25(1), pages 92-100, August.
- Rubinstein, Ariel, 1995.
"On the Interpretation of Decision Problems with Imperfect Recall,"
Mathematical Social Sciences,
Elsevier, vol. 30(3), pages 324-324, December.
- Piccione, Michele & Rubinstein, Ariel, 1997. "On the Interpretation of Decision Problems with Imperfect Recall," Games and Economic Behavior, Elsevier, vol. 20(1), pages 3-24, July.
- Jakub Steiner & Colin Stewart, 2007. "Learning by Similarity in Coordination Problems," CERGE-EI Working Papers wp324, The Center for Economic Research and Graduate Education - Economic Institute, Prague.
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