Multiattribute Decision Making in Context: A Dynamic Neural Network Methodology

A theoretical structure for multiattribute decision making is presented, based on a dynamical system for interactions in a neural network incorporating affective and rational variables. This enables modeling of problems that elude two prevailing economic decision theories: subjective expected utility theory and prospect theory. The network is unlike some that fit economic data by choosing optimal weights or coefficients within a predetermined mathematical framework. Rather, the framework itself is based on principles used elsewhere to model many other cognitive and behavioral data, in a manner approximating how humans perform behavioral functions. Different, interconnected modules within the network encode (a) attributes of objects among which choices are made, (b) object categories, (c) and goals of the decision maker. An example is utilized to simulate the actual consumer choice between old and new versions of Coca-Cola. Potential applications are also discussed to market decisions involving negotiations between participants, such as international petroleum traders.     

Not all forms of misbehavior are created equal: Differential personality facet–counterproductive work behavior relations

We examined whether four personality traits—trait aggression, trait industriousness, trait deceptiveness, and trait self-control—were differentially related to 11 narrow-bandwidth CWB facets: (a) property destruction, (b) inappropriate verbal actions, (c) inappropriate physical actions, (d) poor attendance, (e) poor quality work, (f) unsafe behavior, (g) theft and related behavior, (h) misuse of information, (i) misuse of time and resources, (j) alcohol use, and (k) drug use. Based on responses from 404 employed participants recruited using Mechanical Turk, we found that each narrow-bandwidth personality trait often yielded stronger relationships with overall CWB than with narrow-bandwidth CWB facets.