Talk Abstract: Bayesian networks, extended to Bayesian decision networks (BDNs, aka influence diagrams), support decision making under uncertainty in an ideal (normative) way. That means they can provide guidance to people prepared to learn how to build and test them and then meticulously analyse a decision problem and tailor a BDN to it. In other words, BDNs are used by almost no one for real decision making. In this talk I’ll outline what’s involved in all of that in case you really want to do it (or just understand what’s involved).
But for the vast majority of people there’s a better way: pay (directly or indirectly) someone to build a GUI front-end that will hide the details from the user. For any particular decision problem there will be invariants that can be pre-built into a BDN. All that a user/customer need do is enter the user’s specific context: general background info plus the needs and preferences that specific user has. The BDN can then assess the different choices available and their user-specific value. I will compare and contrast this with what’s most commonly available, CHOICE-like comparative tables of costs and benefits of each alternative, which puts the entire burden on the customer, leading to highly suboptimal decisions.
Bayesian decision making is the wave of the future, so we may as well catch it now.
Speaker: Dr Kevin Korb is a Reader in the Clayton School of Information Technology, Monash University and co-founder and Director of Bayesian Intelligence Pty Ltd. BI delivers training and Bayesian network modeling solutions to business and government.
Korb received his PhD in philosophy of science from Indiana University in 1992. His research interests include Bayesian philosophy of science, causal discovery algorithms, Bayesian networks, evolutionary artificial life simulation, and the epistemology of simulation. He is the author of “Bayesian Artificial Intelligence” (CRC, 2010) and “Evolving Ethics” (Imprint Academic, 2010), co-founder of the journal Psyche, the Association for the Scientific Study of Consciousness and the Australasian Bayesian Network Modelling Society (ABNMS) and Chair of the IEEE Computational Intelligence Society in Victoria.