The process of making decisions amongst stake holders with potentially different views and limited information is omnipresent – examples include democratic policy making, business operations and even decision of dining venue amongst 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. This is based on joint works with Ammar Ammar, David Karger, Sahand Negahban and Sewoong Oh.