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A COMPUTATIONAL THEORY OF SELECTION BY CONSEQUENCES APPLIED TO CONCURRENT SCHEDULES
Author(s) -
McDowell J. J,
Caron Marcia L.,
Kulubekova Saule,
Berg John P.
Publication year - 2008
Publication title -
journal of the experimental analysis of behavior
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.75
H-Index - 61
eISSN - 1938-3711
pISSN - 0022-5002
DOI - 10.1901/jeab.2008.90-387
Subject(s) - selection (genetic algorithm) , computer science , matching law , matching (statistics) , reinforcement , population , function (biology) , reinforcement learning , darwinism , artificial intelligence , psychology , biology , mathematics , evolutionary biology , social psychology , statistics , demography , sociology
Virtual organisms animated by a computational theory of selection by consequences responded on symmetrical and asymmetrical concurrent schedules of reinforcement. The theory instantiated Darwinian principles of selection, reproduction, and mutation such that a population of potential behaviors evolved under the selection pressure exerted by reinforcement from the environment. The virtual organisms' steady‐state behavior was well described by the power function matching equation, and the parameters of the equation behaved in ways that were consistent with findings from experiments with live organisms. Together with previous research on single‐alternative schedules (McDowell, 2004; McDowell & Caron, 2007) these results indicate that the equations of matching theory are emergent properties of the evolutionary dynamics of selection by consequences.