An Extended Reinforcement Algorithm for Estimation of Human Behaviour in Congestion Games
The paper reports simulations applied on two similar congestion games: the first is the classical minority game. The second one is a asymmetric variation of the minority game with linear payoff functions. For each game simulation results based on an extended reinforcement algorithm are compared with real experimental statistics. It is shown that the extension of the reinforcement model is essential for fitting the experimental data and estimating the players behaviour.
|Date of creation:||Dec 2004|
|Date of revision:|
|Contact details of provider:|| Postal: |
Fax: +49 228 73 6884
Web page: http://www.bgse.uni-bonn.de
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.:
- A. Roth & I. Er’ev, 2010. "Learning in Extensive Form Games: Experimental Data and Simple Dynamic Models in the Intermediate Run," Levine's Working Paper Archive 387, David K. Levine.
- 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:bon:bonedp:bgse24_2004. 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: (BGSE Office)
If references are entirely missing, you can add them using this form.