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.     

Adaptation to displacing prisms in human infants.

This research reports a first attempt to study adaptation to laterally displacing prisms in human infants of ages six to ten months. Twenty infants were tested over four days under conditions similar to those used in adult adaptation studies. Evidence was found of vigorous adaptation when infants were exposed to base-right prisms under conditions of active exposure. Detailed consideration is given to the conditions and methods of studying adaptation in infants and implications for adult studies.