Representing Moral Self-Regulatory Restoration in the Formal Architecture of Control Theory: A Computational Simulation

Moral self-regulatory restoration is the process by which an agent, after a transgression, acts to bring the perceived state of their conduct back in line with their moral standard. The present work represents this process within the formal architecture of control theory, whose components (input function, reference, comparator, and output function) are distinct, and implements it as a simulation in R. The architecture makes it possible to locate where constructs from social cognitive theory act in the loop. The restorative gain (K) summarizes capability, value, and confidence; measures of reflective moral self-efficacy are candidate proxies of K. Restoration can be limited in three ways: moral disengagement (M), which, as described by Bandura, reconstrues the appraisal of the transgression while leaving the standard in place, and is therefore located in the input function; standard lowering (L), an accommodation of the reference; and reduced restorative effort, represented by a lower gain. Simulations show that M and L generate identical trajectories after a single transgression but diverge across transgressions of different severity and under interventions on the appraisal of the act or on the standard. The gain governs the time course of restoration and moral disengagement its endpoint: a high gain restores the standard only when moral disengagement is low. When the observation window ends before restoration has stopped, the two parameters also interact statistically, although they act on separate components. The architecture provides a template for representing other self-regulatory processes and deriving the data patterns that alternative descriptions imply.