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Learning in Networks Contexts: Experimental Results from Simulations

Author

Listed:
  • Eric Friedman

    (Rutgers University)

  • Scott Shenker

    (ICSI, Berkeley)

  • Amy Greenwald

    (NYU)

Abstract

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.

Suggested Citation

  • Eric Friedman & Scott Shenker & Amy Greenwald, 1998. "Learning in Networks Contexts: Experimental Results from Simulations," Departmental Working Papers 199825, Rutgers University, Department of Economics.
  • Handle: RePEc:rut:rutres:199825
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    File URL: http://www.sas.rutgers.edu/virtual/snde/wp/1998-25.pdf
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    References listed on IDEAS

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    Cited by:

    1. Huck Steffen & Sarin Rajiv, 2004. "Players With Limited Memory," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 4(1), pages 1-27, September.

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    More about this item

    Keywords

    learning;

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games

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