The role of forgetting in the evolution and learning of language

Abstract

Lewis signaling games illustrate how language might evolve from random behavior. The probability of evolving an optimal signaling language is, in part, a function of what learning strategy the agents use. Here we investigate three learning strategies, each of which allows agents to forget old experience. In each case, we find that forgetting increases the probability of evolving an optimal language. It does this by making it less likely that past partial success will continue to reinforce suboptimal practice. The learning strategies considered here show how forgetting past experience can promote learning in the context of games with suboptimal equilibria.

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2009-04-08

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Author Profiles

Kevin Zollman
Carnegie Mellon University
Jeffrey Barrett
University of California, Irvine

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Citations of this work

Self-Assembling Networks.Jeffrey A. Barrett, Brian Skyrms & Aydin Mohseni - 2019 - British Journal for the Philosophy of Science 70 (1):1-25.
Self-Assembling Games.Jeffrey A. Barrett & Brian Skyrms - 2017 - British Journal for the Philosophy of Science 68 (2):329-353.
Signalling Under Uncertainty: Interpretative Alignment Without a Common Prior.Thomas Brochhagen - 2020 - British Journal for the Philosophy of Science 71 (2):471-496.
Deterministic Chaos and the Evolution of Meaning.Elliott O. Wagner - 2012 - British Journal for the Philosophy of Science 63 (3):547-575.
Propositional Content in Signals.Brian Skyrms & Jeffrey A. Barrett - 2019 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 74:34-39.

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