Seventy percent of organizational change initiatives fail. Among organizations where change and adaptation are necessary for survival, the U.S. military stands at the top. The disparity between desired health and fitness behaviors and actual behaviors are a glaring reminder that change is difficult to implement and that current change systems struggle. Merit-based systems offer a solution by rewarding and reinforcing good behavior to generate lasting change. This paper evaluates Kotter's Change Model and Nudge Theory and found them insufficient because they do not sufficiently address reinforcement learning or the temporal tie between behaviors and rewards for reinforcement.
This paper then examines behavior modification through a theoretical framework called Active Inference. Active Inference suggests agents or organisms will engage in behavioral tradeoffs based on their prior knowledge, present sensing, and future beliefs. This paper suggests that the modeling of behaviors using active inference allows supervisors to predict and target behaviors that will need to be reinforced by a merit-based system to produce long-term change. Finally, this paper examines and recommends the adoption of blockchain play-to-earn models to standardize and automate rewards to produce lasting habits that result in long-term change.
Major: Modeling and Simulation
Educational Career:
Bachelor's of Psychology, BA, 2007, Virginia Military Institute
Master's of Human Services Counseling, MA, 2016, Liberty University
Committee in Charge:
Bruce Caulkins, Chair, School of Modeling, Simulation, and Training
Roger Azevedo, School of Modeling, Simulation, and Training
Crystal Maraj, School of Modeling, Simulation, and Training
Approved for distribution by Bruce Caulkins, Committee Chair, on March 21, 2023.
The public is welcome to attend.