Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
ME1 - AI10: Bayesian method and machine learning application
Time:
Monday, 26/June/2023:
ME 16:30-18:00
Location:Cartier II
3rd floor
Presentations
Diversified learning: Bayesian control with multiple biased information sources
Jussi Keppo1, Michael Kim2, Xinyuan Zhang2
1NUS Business School, National University of Singapore; 2Sauder School of Business, University of British Columbia
We consider a decision-maker (DM) who can sample from multiple information sources to learn a state before making an earning decision. The DM optimizes his sampling and earning decisions to maximize his payoffs. The problem is motivated by financial and healthcare applications with multiple information sources. We develop a Bayesian control framework for this problem and solve it in the estimation and testing settings. We also develop an efficient algorithm for the general control setting.
Strategic choices and routing within service networks: modeling and estimation using machine learning
