This paper focuses on challenges to improving the behavioral realism of computer generated agents and attempts to reflect the state of the art in human behavior modeling with particular attention to value ontologies, emotion, and stress in game-theoretic settings. The goal is to help those interested in constructing more realistic software agents for use in simulations, in virtual reality environments, and in training and performance aiding settings such as on the web or in embedded applications. This paper pursues this goal by providing a framework for better integrating the theories and models contained in the diverse human behavior modeling literatures, such as those that straddle physiological, cognitive and emotive processes; individual differences; emergent group and crowd behavior; and (punctuated) equilibria in social settings. The framework is based on widely available ontologies of world values and how these and physiological factors might be construed emotively into subjective expected utilities to guide the reactions and deliberations of agents. For example what makes one set of opponent groups differ from another? This framework serves as an extension of Markov decision processes appropriate for iterative play in game-theoretic settings, with particular emphasis on agent capabilities for redefining drama and for finding meta-games to counter the human player. This article presents the derivation of the framework and some initial results and lessons learned about integrating behavioral models into interactive dramas and meta-games that stimulate (systemic) thought and training doctrine.