The 21^st century battle space consists of vastly complex and dynamic environments to which human decision makers must adapt in order to achieve their objective. Moreover, the recent trend of manpower reductions necessitates the implementation of highly automated systems. Therefore, a decision maker must not only function under conditions of target ambiguity, time pressure, and information overload, he/she must also be able to effectively operate somewhat autonomous pieces of machinery. This paper presents the work in progress of a human decision making model for dynamic, time stressed tasks. We build upon a theoretical model of behavioral decision making generally used in judgment analyses in static contexts. For this paper, we focus on the mathematical formulation of the problem and some solution techniques.

Our model of the human decision maker seeks to account for noncompensatory (i.e., rule-based) behavior. In general, we present a model that uses inductive inference principles to generate rules in disjunctive normal form that are consistent with human decisions. More specifically, we formulate the problem as a multi-objective linear programming (MOLP) problem. We provide some sensitivity analyses of exhaustive solutions to simple decision problems and propose the use of heuristic algorithms for more complex problems.