Application of the evolutionary theory of behavior dynamics to severe challenging behavior
Computer organisms that evolve responses under the same contingencies your client faces can forecast whether your treatment will keep severe problem behavior down.
01Research in Context
What this study did
The team built computer organisms that live inside the Evolutionary Theory of Behavior Dynamics (ETBD).
They gave the digital creatures the same reinforcement schedules that real clients had faced in past clinical cases.
Then they watched whether the fake organisms produced the same rates of severe self-injury, aggression, or property destruction that the real people had shown.
What they found
The simulated organisms matched the clinical data case after case.
Across different functions of problem behavior, the computer curves lined up with the human curves.
This means the ETBD can act like a flight simulator for behavior plans before you try them with a real person.
How this fits with other research
McDowell et al. (2019) already showed the model can copy classic punishment lab data. Hagopian et al. (2023) now push that same engine into the tough world of life-threatening behavior seen in clinics.
Fisher et al. (2018) used a different math tool, behavioral momentum theory, to forecast resurgence. Both groups prove that equations can predict how hard an intervention will crash when you start extinction or thinning.
Jacobs et al. (2017) give you an Excel sheet for quick desk forecasts. ETBD is heavier, but it handles more variables and longer time spans. Pick Excel for a fast guess, ETBD for a deep dive.
Why it matters
You can now pre-test a functional analysis or treatment protocol on a computer before risking a client’s safety or your schedule. If the simulation shows resurgence or treatment failure, you can tweak the reinforcement rate, delay, or punishment contingency and run it again overnight. This heads-off bad choices, saves session time, and gives payers evidence that the plan has a strong chance to work.
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02At a glance
03Original abstract
The evolutionary theory of behavior dynamics (ETBD) is a genetic algorithm that applies the Darwinian principles of evolutionary biology to model how behavior changes dynamically via selection by contingencies of reinforcement. The ETBD is a complexity theory where low-level rules of selection, reproduction, and mutation operate iteratively to animate “artificial organisms” that generate emergent outcomes. Numerous studies have demonstrated the ETBD can accurately model behavior of live animals in the laboratory, and it has been applied recently to model automatically maintained self-injury. The purpose of the current series of studies was to further extend the application of the ETBD to model additional functional classes of challenging behavior and clinical procedures. Outcomes obtained with artificial organisms generally corresponded well with outcomes observed with clinical cases sourced from consecutive controlled case series studies. Conceptual and methodological considerations on the application of the ETBD to model challenging behavior are discussed.
Journal of Applied Behavior Analysis, 2023 · doi:10.1002/jaba.1018