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Language, Categorization, And Convention

Author

Listed:
  • LOUIS NARENS

    (Department of Cognitive Sciences, University of California, Irvine, Irvine, CA 92697-5100, USA)

  • KIMBERLY A. JAMESON

    (Institute for Mathematical Behavioral Sciences, University of California, Irvine, Irvine, CA 92697-5100, USA)

  • NATALIA L. KOMAROVA

    (Department of Mathematics, University of California, Irvine, Irvine, CA 92697, USA)

  • SEAN TAUBER

    (Cognitive Sciences, University of California, Irvine, Irvine, CA 92697-5100, USA)

Abstract

Linguistic meaning is a convention. This article investigates how such conventions can arise for color categories in populations of simulated "agents". The method uses concepts from evolutionary game theory: A language game where agents assign names to color patches and is played repeatedly by members of a population. The evolutionary dynamics employed make minimal assumptions about agents' perceptions and learning processes. Through various simulations it is shown that under different kinds of reasonable conditions involving outcomes of individual games, the evolutionary dynamics push populations to stationary equilibria, which can be interpreted as achieving shared population meaning systems. Optimal population agreement for meaning is characterized through a mathematical formula, and the simulations presented reveal that for a wide variety of situations, optimality is achieved.

Suggested Citation

  • Louis Narens & Kimberly A. Jameson & Natalia L. Komarova & Sean Tauber, 2012. "Language, Categorization, And Convention," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 15(03n04), pages 1-21.
    • Handle: RePEc:wsi:acsxxx:v:15:y:2012:i:03n04:n:s0219525911500226
    DOI: 10.1142/S0219525911500226
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    Cited by:

    1. Joe, Kirbi & Gooyabadi, Maryam, 2021. "A Bayesian nonparametric mixture model for studying universal patterns in color naming," Applied Mathematics and Computation, Elsevier, vol. 395(C).

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