A quantitative evolutionary theory of adaptive behavior dynamics.

Abstract

The idea that behavior is selected by its consequences in a process analogous to organic evolution has been discussed for over 100 years. A recently proposed theory instantiates this idea by means of a genetic algorithm that operates on a population of potential behaviors. Behaviors in the population are represented by numbers in decimal integer (phenotypic) and binary bit string (genotypic) forms. One behavior from the population is emitted at random each time tick, after which a new population of potential behaviors is constructed by recombining parent behavior bit strings. If the emitted behavior produced a benefit to the organism, then parents are chosen on the basis of their phenotypic similarity to the emitted behavior; otherwise, they are chosen at random. After parent behavior recombination, the population is subjected to a small amount of mutation by flipping random bits in the population's bit strings. The behavior generated by this process of selection, reproduction, and mutation reaches equilibrium states that conform to every empirically valid equation of matching theory, exactly and without systematic error. These equations are known to describe the behavior of many vertebrate species, including humans, in a variety of experimental, naturalistic, natural, and social environments. The evolutionary theory also generates instantaneous dynamics and patterns of preference change in constantly changing environments that are consistent with the dynamics of live-organism behavior. These findings support the assertion that the world of behavior we observe and measure is generated by evolutionary dynamics.

Against matching theory: Predictions of an evolutionary theory of behavior dynamics

“…Plots in McDowell et al (2008) (also in McDowell, 2013b) showed in a striking way that even at very small time scales, the cumulative changes in and reversals of preference exhibited by the artificial organisms were indistinguishable from those exhibited by Davison and Baum's birds. These are just two examples of successful first-stage predictions of the evolutionary theory. In a recent review, McDowell (2013b) found that the theory generated results that were qualitatively and quantitatively consistent with a large body of existing data from experiments with live organisms.…”

“…Behaviors in these classes corresponded to responses on two alternatives in an experimental chamber. The Hamming distance (McDowell, 2013b;Popa and McDowell, 2010) that naturally exists at the boundary of these classes acted as a changeover delay.…”

“…The purpose of this article is to develop a second-stage prediction of an evolutionary theory of behavior dynamics (McDowell, 2004(McDowell, , 2013b. As explained in Section 3 below, this includes a critical experiment that compares contradictory predictions of matching theory and the evolutionary theory.…”

A multivariate assessment of the rapidly changing procedure with McDowell's Evolutionary Theory of Behavior Dynamics

“…Algorithm 1 (see the supplementary material of McDowell, ) samples parents based on their fitness values. In Algorithm 1, a father is selected by sampling a trial fitness value from the fitness function (line 5) and searching through the ordered population for a behavior with the sampled fitness (line 6).…”

An evolutionary theory of behavior dynamics applied to concurrent ratio schedules

“…These processes of variation and selection cause the population of potential behaviors to evolve under the selection pressure of consequences from the environment. A more detailed description of the theory's algorithmic operation is given in McDowell (). The theory was first applied to single variable‐interval (VI) schedules by McDowell (), and to concurrent VI VI schedules by McDowell, Caron, Kulubekova, and Berg ().…”