An approach for dynamic optimization of prevention program implementation in stochastic environments

  • Authors:

  • Yuncheol Kang;Vittal Prabhu

  • Affiliations:

  • Department of Industrial and Manufacturing Engineering, Pennsylvania State University, University Park, PA;Department of Industrial and Manufacturing Engineering, Pennsylvania State University, University Park, PA

  • Venue:
  • SBP'11 Proceedings of the 4th international conference on Social computing, behavioral-cultural modeling and prediction
  • Year:
  • 2011

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Abstract

The science of preventing youth problems has significantly advanced in developing evidence-based prevention program (EBP) by using randomized clinical trials. Effective EBP can reduce delinquency, aggression, violence, bullying and substance abuse among youth. Unfortunately the outcomes of EBP implemented in natural settings usually tend to be lower than in clinical trials, which has motivated the need to study EBP implementations. In this paper we propose to model EBP implementations in natural settings as stochastic dynamic processes. Specifically, we propose Markov Decision Process (MDP) for modeling and dynamic optimization of such EBP implementations. We illustrate these concepts using simple numerical examples and discuss potential challenges in using such approaches in practice.