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Multiattribute Decision Making in Context: A Dynamic Neural Network Methodology
Author(s) -
Leven Samuel J.,
Levine Daniel S.
Publication year - 1996
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1207/s15516709cog2002_4
Subject(s) - computer science , dynamic decision making , artificial neural network , context (archaeology) , prospect theory , artificial intelligence , negotiation , decision maker , cognition , decision theory , object (grammar) , management science , operations research , machine learning , microeconomics , economics , mathematics , psychology , paleontology , neuroscience , political science , law , biology
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.