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.     

Effects of Social Demand on Reports of Self-Monitored Blood Glucose in Adolescents with Type 1 Diabetes Mellitus

The purpose of this study was to test the hypothesis that social demand could substantially affect reports of self-monitored blood glucose (BG) in adolescents with insulin-dependent diabetes mellitus. Of 34 patients initially enrolled in the study, 10 were excluded because they did not bring any BG records with them to an outpatient clinic appointment. The remaining 24 patients were randomly assigned to either a low or high social demand condition that provided instructions for monitoring of BG for the week following the appointment. The subjects' BG records were quantified to provide frequency of measurement and mean reported BG for the week prior to and after the clinic visit. Five subjects did not return their BG records for the week following the intervention. The analyses were therefore based on the 19 subjects from whom complete records were obtained. The 12 subjects in the low social demand group and 7 subjects in the high social demand group were equivalent with regard to age, duration of diabetes, socioeconomic status, and glycosylated hemoglobin. Frequency of BG measurement was similar in both groups during both weeks. The mean BG value reported in the week prior to intervention was similar for the groups. However, analyses of the post-intervention BGs revealed that subjects in the low-demand group reported significantly higher BGs compared to pre-intervention and to subjects in the high-demand group. These findings suggest that self-monitoring and reporting of BG is a social behavior that is affected by the demand characteristics of the interpersonal patient-health provider relationship. Because optimal treatment planning for individuals with diabetes requires accurate BG records, care must be taken to interpret them in light of the social demand characteristics associated with clinical assessment.