At PA Consulting we have developed several models using Bayesian
Networks in areas such as fraud detection and drug discovery.
We have also been in conversations with clients to implement
"hybrid" systems that utilize results from both BBNs and SD
models. The current thinking on market modeling is to use an
SD model to calculate "macro" market variables, and use BBNs
to represent customer choice at the individual product level
(which is a process surrounded by substantial uncertainty).
True integration would be harder; it would require reading results
from a BBN at every DT in the simulation, and using those results
to modify variables in the SD model. While this is possible with
some API programming in most platforms, I guess that for an SD model
that has more than 50 variables or so the simulation time may become
prohibitive (of course, Moores law is still operating...)
In the literature for BBNs youll find descriptions of "Dynamic
Bayesian Networks" which are BBNs replicated over time and connected.
With some development, this approach could replace simpler SD models
where many cause-effect relationships between variables are
probabilistic and uncertain. But for larger models it would still be
impractical. One of the limitations of current BBN implementations is
that continuous variables need to be "discretized", which results in
some loss of accuracy. In a model with many time steps, this error
would accumulate and materially distort the results.
So rather than integrating these models at the simulation step level,
I lean towards using the methodologies in complementary ways. Longer-
term dynamic problems that can be analyzed at a higher level of
aggregation should be approach using System Dynamics. Uncertainty
about cause-effect relationships is best dealt with using sensitivity
BBNs work much better for decision making under uncertainty at a more
From: Carlos Ariza <Carlos.Ariza@paconsulting.com>