Collaborative Decision Making
The process of making decisions amongst stake holders with potentially different views and limited information is omnipresent : democratic policy making, business management, hiring decisions or even deciding venue for meal amongst a group of friends. Efficient automation of collaborative decision making requires developing (a) useful human-interface to seek information, (b) statistical model to capture human uncertainty, and (c) efficient inference algorithm. We have develop such a framework in the context of two scenarios: micro-task crowd sourcing and rank aggregation. We shall discuss the associated statistical models, algorithms and their information optimality.
Devavrat Shah is currently an associate professor with the department of Electrical Engineering and Computer Science, MIT. He is a member of the Laboratory for Information and Decision Systems (LIDS) and Operations Research Center (ORC). His research focus is on theory of large complex networks which includes network algorithms, stochastic networks, network information theory and large scale statistical inference. He has received several conference best paper awards in the area of communication, machine learning and operations research.
He was awarded the first ACM SIGMETRICS Rising Star Award 2008 for his work on network scheduling algorithms. He received the 2010 Erlang Prize from INFORMS which is given to a young researcher for outstanding contributions to applied probability. His work (co-authored with Jinwoo Shin) was adjudged the best publication in Applied Probability in the period of 2010-2013.