Abstract
Virtual organisms animated by a selectionist theory of behavior dynamics worked on concurrent random interval schedules where both the rate and magnitude of reinforcement were varied. The selectionist theory consists of a set of simple rules of selection, recombination, and mutation that act on a population of potential behaviors by means of a genetic algorithm. An extension of the power function matching equation, which expresses behavior allocation as a joint function of exponentiated reinforcement rate and reinforcer magnitude ratios, was fitted to the virtual organisms' data, and over a range of moderate mutation rates was found to provide an excellent description of their behavior without residual trends. The mean exponents in this range of mutation rates were 0.83 for the reinforcement rate ratio and 0.68 for the reinforcer magnitude ratio, which are values that are comparable to those obtained in experiments with live organisms. These findings add to the evidence supporting the selectionist theory, which asserts that the world of behavior we observe and measure is created by evolutionary dynamics.