Dr. Jack McDowell: Complex Systems Theory in Behavior Analysis

Abstract

Traditional scientific theories typically abstract simplified variables from phenomena and enter them into mathematical expressions to test empirically. In behavior analysis, for example, behavioral and environmental phenomena are often reduced to simple rates of target responding and reinforcement, which are then entered into mathematical expressions such as the matching law. A modern version of theory development instead treats observable phenomena as the result of the operation of a complex system. The operation of the system is stated in the form of low level rules, which constitute the theory. A system that follows the rules produces higher level emergent outcomes that can be compared to data. One advantage of complex systems theory over traditional theory in science is that it naturally produces a wider range of phenomena, both steady-state and dynamic, that can be compared with experimental findings. An example of a complex systems theory in behavior analysis is the evolutionary theory of behavior dynamics (ETBD), which is stated in the form of low-level Darwinian rules that can be used to animate artificial organisms (AOs). The behavior of the AOs is a form of artificial intelligence that can be studied empirically and compared to the behavior of live organisms. The ETBD has been shown to accurately describe the behavior of live organisms, both qualitatively and quantitatively, in a wide variety of environments. The theory has also been successfully applied to the study and treatment of clinically significant behavior problems.