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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Greenberg, M., Domitrovich, C., Bumbarger, B.: Preventing mental disorders in school-age children: A review of the effectiveness of prevention programs. Prevention Research Center for the Promotion of Human Development, Pennsylvania State University. Submitted to Center for Mental Health Services, Substance Abuse and Mental Health Services Administration, US Department of Health & Human Services (1999)
Miller, T., Hendrie, D.: Substance abuse prevention dollars and cents: A cost-benefit analysis. In: Services, US Department of Health & Human Services. (ed.), p. 59 (2009)
Greenberg, M.: Current and future challenges in school-based prevention: The researcher perspective. Prevention Science 5, 5–13 (2004)
Bumbarger, B., Perkins, D.: After randomised trials: issues related to dissemination of evidence-based interventions. Journal of Children’s Services 3, 55–64 (2008)
Bumbarger, B.K., Perkins, D.F., Greenberg, M.T.: Taking effective prevention to scale. In: Doll, B., Pfohl, W., Yoon, J. (eds.) Handbook of Prevention Science. Routledge, New York (2009)
Collins, L., Murphy, S., Bierman, K.: A conceptual framework for adaptive preventive interventions. Prevention Science 5, 185–196 (2004)
Rivera, D., Pew, M., Collins, L.: Using engineering control principles to inform the design of adaptive interventions: A conceptual introduction. Drug and Alcohol Dependence 88, S31–S40 (2007)
Bellman, R., Lee, E.: History and development of dynamic programming. Control Systems Magazine 4, 24–28 (2002)
Puterman, M.: Markov decision processes: Discrete stochastic dynamic programming. John Wiley & Sons, Inc., New York (1994)
Schaefer, A., Bailey, M., Shechter, S., Roberts, M.: Modeling medical treatment using Markov decision processes. Operations Research and Health Care, 593–612 (2005)
Greenberg, M., Kusche, C., Cook, E., Quamma, J.: Promoting emotional competence in school-aged children: The effects of the PATHS curriculum. Development and Psychopathology 7, 117–136 (1995)
Bierman, K., Coie, J., Dodge, K., Greenberg, M., Lochman, J., McMahon, R., Pinderhughes, E.: The Effects of a Multiyear Universal Social-Emotional Learning Program: The Role of Student and School Characteristics. Journal of Consulting and Clinical Psychology 78, 156–168 (2010)
Smallwood, R., Sondik, E.: The optimal control of partially observable Markov processes over a finite horizon. Operations Research 21, 1071–1088 (1973)
Hauskrecht, M., Fraser, H.: Planning treatment of ischemic heart disease with partially observable Markov decision processes. Artificial Intelligence in Medicine 18, 221–244 (2000)
Kirkizlar, E., Faissol, D., Griffin, P., Swann, J.: Timing of testing and treatment for asymptomatic diseases. Mathematical Biosciences, 28–37 (2010)
Oakley, A., Strange, V., Bonell, C., Allen, E., Stephenson, J.: Process evaluation in randomised controlled trials of complex interventions. Bmj 332, 413 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kang, Y., Prabhu, V. (2011). An Approach for Dynamic Optimization of Prevention Program Implementation in Stochastic Environments. In: Salerno, J., Yang, S.J., Nau, D., Chai, SK. (eds) Social Computing, Behavioral-Cultural Modeling and Prediction. SBP 2011. Lecture Notes in Computer Science, vol 6589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19656-0_37
Download citation
DOI: https://doi.org/10.1007/978-3-642-19656-0_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19655-3
Online ISBN: 978-3-642-19656-0
eBook Packages: Computer ScienceComputer Science (R0)