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.     

Clues to Suicide,Reconsidered

This brief paper focuses on a rather recondite aspect of the suicidal scenario: dissembling (feigning, pretending, withholding). Most individuals who commit suicide exhibit prodromal indicia in the month before. These verbal and behavioral clues are found in about 90% of psychological autopsies of suicidal deaths. As for the other 10% who mask or hide their lethal intentions, we do well to assume that even some hidden clues might be deciphered by the skillful clinician or the alert friend or colleague.     

Mobilization of Cognitive Resources and the Generation Effect

The generation effect refers to the memory advantage of words that have been generated rather than read. Such a read-generate comparison confounds qualitative task differences and raises methodological problems. A revised methodology is proposed circumventing these problems in that the encoding task is held constant and all stimuli have to be generated, but the degree of generativeness (i.e. the amount of cueing) is varied. In Experiment 1, 1, the (refined version of the) generation effect is demonstrated in a within-subjects design; with increasing generation activity left to the subject, free recall performance increases. No effect is obtained for degree of target masking. The same finding is replicated and shown to be independent of self-paced study time when generative activity is manipulated between subjects (Experiment 2) or within subjects (Experiment 3). As all learning trials involve generation, encoding time is controlled statistically, and free recall is used as a measure of memory, this refined generation effect cannot be explained as an artifact of selective attention or elaboration. Rather, generative activity seems to increase the mobilization of cognitive resources. This motivational account is supported by Experiment 4 showing an enhanced generation effect for positive mood.