A general framework of connectionist
decision support systems for
multiple criteria decision making under
certainty is presented. The connectionist
decision support systems are driven
by preferential data and based on prescriptive
decision models, with artificial
neural networks in their core. They are
able to interact with decision makers and
construct, verify, and validate themselves
autonomously from available preferential
data. As time elapses, the decision support
systems are also capable of refining
their internal representations accordingly
to adapt to potential changes of decision
makers’ preference and decision environments
over time. These desirable characteristics
potentiate further development
of the connectionist decision support systems