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

Yuncheol Kang; Vittal Prabhu

doi:10.1007/978-3-642-19656-0_37

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

Yuncheol Kang
, Vittal Prabhu

Division of Business Administration

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Social Computing, Behavioral-Cultural Modeling and Prediction - 4th International Conference, SBP 2011, Proceedings
Pages	260-267
Number of pages	8
DOIs	https://doi.org/10.1007/978-3-642-19656-0_37
State	Published - 2011
Event	4th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2011 - College Park, MD, United States Duration: 29 Mar 2011 → 31 Mar 2011

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6589 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	4th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2011
Country/Territory	United States
City	College Park, MD
Period	29/03/11 → 31/03/11

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 16 Peace, Justice and Strong Institutions

Keywords

Evidence-based Prevention Program
Markov Decision Process
Partially Observable Markov Decision Process

Access to Document

10.1007/978-3-642-19656-0_37

Cite this

Kang, Y., & Prabhu, V. (2011). An approach for dynamic optimization of prevention program implementation in stochastic environments. In Social Computing, Behavioral-Cultural Modeling and Prediction - 4th International Conference, SBP 2011, Proceedings (pp. 260-267). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6589 LNCS). https://doi.org/10.1007/978-3-642-19656-0_37

Kang, Yuncheol ; Prabhu, Vittal. / An approach for dynamic optimization of prevention program implementation in stochastic environments. Social Computing, Behavioral-Cultural Modeling and Prediction - 4th International Conference, SBP 2011, Proceedings. 2011. pp. 260-267 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "An approach for dynamic optimization of prevention program implementation in stochastic environments",

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.",

keywords = "Evidence-based Prevention Program, Markov Decision Process, Partially Observable Markov Decision Process",

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Kang, Y & Prabhu, V 2011, An approach for dynamic optimization of prevention program implementation in stochastic environments. in Social Computing, Behavioral-Cultural Modeling and Prediction - 4th International Conference, SBP 2011, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6589 LNCS, pp. 260-267, 4th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2011, College Park, MD, United States, 29/03/11. https://doi.org/10.1007/978-3-642-19656-0_37

An approach for dynamic optimization of prevention program implementation in stochastic environments. / Kang, Yuncheol; Prabhu, Vittal.
Social Computing, Behavioral-Cultural Modeling and Prediction - 4th International Conference, SBP 2011, Proceedings. 2011. p. 260-267 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6589 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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KW - Partially Observable Markov Decision Process

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Kang Y, Prabhu V. An approach for dynamic optimization of prevention program implementation in stochastic environments. In Social Computing, Behavioral-Cultural Modeling and Prediction - 4th International Conference, SBP 2011, Proceedings. 2011. p. 260-267. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-19656-0_37

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

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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Cite this