Applying the evolutionary theory of behavior dynamics to model the subtypes of automatically reinforced self‐injurious behavior

Morris et al. (2023) built computer models that act like tiny evolving organisms.

The models had to choose between hurting themselves or doing nothing.

By letting the virtual organisms "reproduce" when their choices paid off, the team matched real clinic data on automatic self-injury.

The evolutionary model fit the data better than older static formulas.

It showed that different automatic-SIB "subtypes" can emerge from the same simple rules.

McDowell (2021) first pitched using these evolutionary games for any problem behavior. Morris tightens the lens to automatic SIB only.

Hagopian et al. (2023) also split automatic SIB into subtypes, but they used quick functional-analysis visuals. The two papers agree the subtypes exist; one finds them with eyeball checks, the other with code.

Rooker et al. (2020) showed Subtype-2 automatic SIB produces the worst injuries. Morris gives that hurt a reason: the evolutionary model predicts Subtype-2 reaches the strongest self-reinforcing loop.

You now have two ways to subtype automatic SIB: fast visual analysis or evolutionary modeling. Start with the visual check in clinic. If injury risk looks high or data are messy, plug the numbers into the free evolutionary script (McDowell shares the code). The model can tell you which subtype you likely have and how tough extinction might be. No extra sessions, just sharper assessment.