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

Yuncheol Kang, Vittal Prabhu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish (US)
Title of host publicationSocial Computing, Behavioral-Cultural Modeling and Prediction - 4th International Conference, SBP 2011, Proceedings
Pages260-267
Number of pages8
DOIs
StatePublished - Mar 14 2011
Event4th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2011 - College Park, MD, United States
Duration: Mar 29 2011Mar 31 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6589 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2011
CountryUnited States
CityCollege Park, MD
Period3/29/113/31/11

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'An approach for dynamic optimization of prevention program implementation in stochastic environments'. Together they form a unique fingerprint.

Cite this