Abstract

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 is 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.

Notes

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Graduation Date

2023

Semester

Spring

Advisor

Caulkins, Bruce

Degree

Master of Science (M.S.)

College

College of Engineering and Computer Science

Department

School of Modeling, Simulation, and Training

Degree Program

Modeling & Simulation

Format

application/pdf

Identifier

CFE0009546; DP0027553

URL

https://purls.library.ucf.edu/go/DP0027553

Language

English

Release Date

May 2023

Length of Campus-only Access

None

Access Status

Masters Thesis (Open Access)

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