Arbeitspapier
Learning in Networks Contexts: Experimental Results from Simulations
This paper describes the results of simulation experiments performed on a suite of learning algorithms. We focus on games in {\em network contexts}. These are contexts in which (1) agents have very limited information about the game; users do not know their own (or any other agent's) payoff function, they merely observe the outcome of their play. (2) Play can be extremely asynchronous; players update their strategies at very different rates. There are many proposed learning algorithms in the literature. We choose a small sampling of such algorithms and use numerical simulation to explore the nature of asymptotic play. In particular, we explore the extent to which the asymptotic play depends on three factors, namely: limited information, asynchronous play, and the degree of responsiveness of the learning algorithm.
- Language
-
Englisch
- Bibliographic citation
-
Series: Working Paper ; No. 1998-25
- Classification
-
Wirtschaft
Noncooperative Games
- Subject
-
learning
- Event
-
Geistige Schöpfung
- (who)
-
Friedman, Eric
Shenker, Scott
Greenwald, Amy
- Event
-
Veröffentlichung
- (who)
-
Rutgers University, Department of Economics
- (where)
-
New Brunswick, NJ
- (when)
-
1998
- Handle
- Last update
-
10.03.2025, 11:44 AM CET
Data provider
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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Friedman, Eric
- Shenker, Scott
- Greenwald, Amy
- Rutgers University, Department of Economics
Time of origin
- 1998
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