  This paper addresses the tradeoffs which need to be considered in reasoning
using probabilistic network representations, such as Influence Diagrams (IDs).
In particular, we examine the tradeoffs entailed in using Temporal Influence
Diagrams (TIDs) which adequately capture the temporal evolution of a dynamic
system without prohibitive data and computational requirements. Three
approaches for TID construction which make different tradeoffs are examined:
(1) tailoring the network at each time interval to the data available (rather
then just copying the original Bayes Network for all time intervals); (2)
modeling the evolution of a parsimonious subset of variables (rather than all
variables); and (3) model selection approaches, which seek to minimize some
measure of the predictive accuracy of the model without introducing too many
parameters, which might cause "overfitting" of the model. Methods of evaluating
the accuracy/efficiency of the tradeoffs are proposed.
