Title: Addressing the Practical Challenges of Group Decision
Support in a Data-rich World
Speaker: Craig Boutilier
Abstract: In a variety of social, business, political and economic contexts,
decision making often involves groups of people or multiple stakeholders.
Automated decision support for groups is considerably more difficult than
for a single stakeholder since it requires aggregating the preferences of
members of the group to determine optimal decisions. This brings with it
the need to assess far more preference information, and often demands
sophisticated mechanisms and optimization techniques to limit
opportunities for manipulation.
These problems are widely studied with the field of computational social
choice. In this talk, I’ll outline some of the models and methods my
group has been developing to address the informational and computational
challenges needed to make decision support for groups more practical and
widely applicable. This includes: robust optimization methods for handling
partial preference information; interactive elicitation techniques to
minimize the amount of preference information needed to compute optimal
decisions; optimization methods for combinatorial group decisions;
preference learning from choice data; and optimization and elicitation
techniques that exploit probabilistic preference models derived from data.
I’ll conclude with a discussion of some emerging opportunities, and
associated challenges, in group decision support.
Bio: Craig Boutilier is Principal Scientist at Google (currently on leave from
his position as Professor in the Department of Computer Science at the
University of Toronto and Canada Research Chair in Adaptive Decision
Making for Intelligent Systems). He received his Ph.D. in Computer
Science from the University of Toronto in 1992, and worked as an Assistant
and Associate Professor at the University of British Columbia from 1991
until his return to Toronto in 1999. He co-founded Granata Decision
His current research efforts focus on various aspects of decision making
under uncertainty: preference elicitation, mechanism design, game theory
and multiagent decision processes, economic models, social choice,
computational marketing and advertising, Markov decision processes,
reinforcement learning and probabilistic inference.
Boutilier was Editor-in-Chief of the Journal of Artificial Intelligence
Research (JAIR), and Program Chair for the 21st International Joint
Conference on Artificial Intelligence (IJCAI-09) and the 16th Conference
on Uncertainty in Artificial Intelligence (UAI-2000) He is a Fellow of the
Royal Society of Canada (RSC), the Association for Computing Machinery
(ACM) and the Association for the Advancement of Artificial Intelligence