Stubborn learning

Theory and Decision 79 (1):51-93 (2015)
  Copy   BIBTEX

Abstract

The paper studies a specific adaptive learning rule when each player faces a unidimensional strategy set. The rule states that a player keeps on incrementing her strategy in the same direction if her utility increased and reverses direction if it decreased. The paper concentrates on games on the square [0,1]×[0,1]\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$[0,1]\times [0,1]$$\end{document} as mixed extensions of 2×2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2\times 2$$\end{document} games. We study in general the behavior of the system in the interior as well as on the borders of the strategy space. We then describe the system asymptotic behavior for symmetric, zero-sum, and twin games. Original patterns emerge. For instance, for the “prisoner’s dilemma” with symmetric initial conditions, the system goes directly to the symmetric Pareto optimum. For “matching pennies,” the system follows slowly expanding cycles around the mixed strategy equilibrium.

Links

PhilArchive



    Upload a copy of this work     Papers currently archived: 92,197

External links

Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library

Similar books and articles

Self-Deception and Stubborn Belief.Kevin Lynch - 2013 - Erkenntnis 78 (6):1337-1345.
The Stubborn Drive.Teresa de Lauretis - 1998 - Critical Inquiry 24 (4):851-877.

Analytics

Added to PP
2014-07-03

Downloads
34 (#472,008)

6 months
8 (#367,748)

Historical graph of downloads
How can I increase my downloads?

Citations of this work

No citations found.

Add more citations

References found in this work

The possible and the impossible in multi-agent learning.H. Peyton Young - 2007 - Artificial Intelligence 171 (7):429-433.

Add more references