Exemplary practice of this invention implements a computer to model human decision-making within a comprehensive human-perception construct dichotomized as probabilistically perceptual and preferentially perceptual. Potential actions are identified. Each identified potential action is evaluated in consideration of (i) probabilities of success as perceived by the decision-maker, and (ii) preferences of the decision-maker relating to consistency, credibility, confidence, bias, and urgency. Decision-making is modeled on a continual basis whereby evaluation of at least one potential action is performed anew in each successive time-step. Evaluations of potential actions yield “goodness” values, which are compared to determine best potential actions. Threshold “goodness” parameters are established to filter out some potential actions and leave other potential actions for goodness comparison. The best potential action of a particular time-step is compared to the best potential action of the preceding time-step in order to decide whether to maintain or change the existing course of action.
Algorithmic method for modeling human decision-making