The following HTML text is provided to enhance online
readability. Many aspects of typography translate only awkwardly to HTML.
Please use the page image
as the authoritative form to ensure accuracy.
Modeling Human and Organizational Behavior: Application to Military Simulations
individuals to represent differences in risk attitudes. Convex-shaped functions result in risk-seeking decisions, concave-shaped functions result in risk aversion, and S-shaped functions result in risk seeking in the domain of losses and risk aversion in the domain of gains (see Tversky and Kahneman, 1992). Individual differences in optimistic versus pessimistic opinions are represented in rank-dependent utility theory by variation in the shape of the weighting function across individuals (see Lopes, 1987). Aggressive versus passive inclinations can be represented in multiattribute expected-utility models as differences in the tradeoff weights decision makers assign to enemy versus friendly losses, with aggressive decision makers placing relatively less weight on the latter. Rational versus irrational thinking can be manipulated by varying the magnitude of the error variance in random-utility models, with smaller error variance resulting in choices that are more consistent with the idealistic utility models. Impulsive versus compulsive (or deliberative) tendencies are represented in sequential sampling models by the magnitude of the inhibitory threshold, with smaller thresholds being used by impulsive decision makers. Expert versus novice differences can be generated from an adaptive planning model by varying the amount of training that is provided with a particular decision task.
Decision theorists have not considered state factors to the same extent as individual difference factors. However, several observations can be made. Fatigue and stress tend to limit attentional capacity, and these effects can be represented as placing greater weight on a single most important attribute in a multiattribute decision model, sampling information from the most important dimension in a sequential sampling decision model, or limiting the planning horizon to immediate consequences in an adaptive planning model. Fear is an important factor in approach-avoidance models of decision making, and increasing the level of fear increases the avoidance gradient or attention paid to negative consequences (Lewin, 1935; Miller, 1944; Coombs and Avrunin, 1977; Busemeyer and Townsend, 1993). The state of health of the decision maker will affect the importance weights assigned to the attributes of safety and security in multiattribute decision models.
INCORPORATING JUDGMENTAL ERRORS INTO DECISION MODELS
The models discussed thus far reflect a top-down approach: decision models of great generality are first postulated and then successively modified and extended to accommodate empirical detail. As noted earlier, a second tradition of research on decision making takes a bottom-up approach. Researchers in this tradition have identified a number of phenomena that are related to judgmental