Modeling Human Decisions in Performance and Dependability Models

Abstract

Many systems are driven partially by human operators who decide about basic operations that influence system behavior. Therefore the performance and dependability depend on the technical system and the human operator. Performance and dependability models usually include a detailed model of the technical infrastructure but the human decision maker is only roughly modeled by simple probabilities or delays. However, in psychology much more sophisticated models of human decision making exist. For tasks with two choices usually diffusion models are applied. These models include information about the process of human decision making based on perception or memory retrieval and take into account the time pressure under which decisions have to be made. In this paper we combine these diffusion models with Markov models for performance and dependability analysis. By using a discretization approach for the diffusion model the combined model is a Markov chain which can be analyzed with standard means. The approach allows one to integrate detailed models of human two-way decisions in performance and dependability models.