An Adaptive Agent-Based Model for Observing Smoking Cessation Patterns

Abstract

Abstract Background The complex effect of multiple influencing factors in the idiosyncrasies of how people frame a purpose, and the decisions and actions they carry out to reach a goal have a strong impact on repeated-occurrence behaviors, such as smoking cessation, where the perceived benefit of behavioral change may wane in response to difficulties or setbacks. Purpose We applied a modeling cycle methodology to implement a knowledge-based ABM to assess the impact of individual health preferences, and societal factors on modifiable behaviors to identify time-related opportunities for non-pharmaceutical interventions to sustain the desired behavioral changes. Methods We gathered and encoded information about patient beliefs, preferences, and societal factors as simple rules to roughly represent patient behaviors. Through ABM simulations we looked at idiosyncratic patterns stemming from the complex effect of multiple influencing factors. Results Marked smoking/non-smoking fluctuations of women vs. slower, steadier decline in smoking men highlight the complex effect of multiple influencing factors in the idiosyncrasies of how people frame a purpose, and the decisions and actions they carry out to reach a goal. Unintentional patterns of segregation underline the impact surrounding neighbors’ have on an agent’s behavior, blurring the line between individual motivation and collective influence, leading agents to align with the surrounding majority. Conclusions ABMs provide insights on the impact multi-factorial, dynamic individual behaviors, and societal factors have on repeated-occurrence modifiable behaviors, e.g., smoking cessation, and pinpoint opportune educational and motivational adjuvant interventions to counteract negative, more ingrained behaviors, and external factors to improve compliance and success.