Intelligent Group Decision Systems and Preference Learning

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Recent technological advances (e.g., Internet, email, and online social networks) have united information and people, but have accelerated the explosive growth of choices and information. This information and choice overload has made decision making harder, which can paralyze decision making for tasks ranging from simple daily-life decisions to high-risk business decisions. To overcome this decision-making complexity, intelligent decision systems (e.g., recommender systems) will play a crucial role in the near future to assist or automate our decision-making processes.******One of the main endeavors in any intelligent decision system is learning the user preferences upon which decisions are ultimately made. Current systems (e.g., recommender systems) require users to constantly report their preferences (e.g., rate movies, books, etc.). This requirement imposes high cognitive burden on users, and consequently reduces their effectiveness. This research program's approach to preference learning is to minimize this burden, by requiring as little explicit information as possible from users to make intelligent decision systems practical and help preserve individual's privacy. This approach is possible due to the increasing availability of user behaviour data generated through online social networks, e-marketplaces, or any other Web or mobile applications. The focus of this proposal is group decision problems, which involve making decisions for a group of individuals who have their own personal and possibly conflicting preferences. Group decision problems are prevalent (e.g., decisions for social groups, business organizations, public policies, etc.). The problem of preference learning in group decision making is more predominant as the decision maker needs to learn a collection of group preferences, which can be challenging to obtain. ******The long-term objective of this research program is the development of practical group decision making algorithms, and their empirical and theoretical assessment. The program focuses on both preference learning---by reducing the explicit information needed for decision making---and the design of robust computationally-efficient algorithms, which can handle uncertain and noisy preferences. A variety of group decision problems will be studied, including voting, matching, and assignment. The program encompasses several key methodological themes such as stochastic optimization for incomplete preferences, probabilistic models for preference learning, and theoretical and empirical analyses of performance of algorithms for group decision making. The output of this program will help pave the way for intelligent decision systems to assist group decision making for individuals, organizations, businesses, and nations in a myriad of domains including health care, entertainment, public policy, marketing, and finance.