Abstract: While social media pervades many aspects of our lives, it has not yet proved to be an effective tool for large scale decision making: crowds of hundreds, perhaps millions, of individuals collaborating together to come to consensus on difficult societal issues. The objective of our research is to develop an algorithmic and empirical understanding of large scale decision making, and experiment with real-life deployments of our algorithms. In this talk, we will first present our platform for voting in participatory budgeting elections, which has been used in over a dozen different elections. We will then describe the related algorithmic problem of knapsack voting, where voters have to allocate a fixed amount of funds among multiple projects. We will conclude by analyzing opinion formation processes in terms of their effect on polarization, and relate this to the design of recommendation systems for friends and contents.
About the Speaker: Ashish Goel is a Professor of Management Science and Engineering and (by courtesy) Computer Science at Stanford University, and a member of Stanford's Institute for Computational and Mathematical Engineering. He received his PhD in Computer Science from Stanford in 1999, and was an Assistant Professor of Computer Science at the University of Southern California from 1999 to 2002. His research interests lie in the design, analysis, and applications of algorithms; current application areas of interest include social networks, participatory democracy, Internet commerce, and large scale data processing. Professor Goel is a recipient of an Alfred P. Sloan faculty fellowship (2004-06), a Terman faculty fellowship from Stanford, an NSF Career Award (2002-07), and a Rajeev Motwani mentorship award (2010). He was a co-author on the paper that won the best paper award at WWW 2009, and an Edelman Laureate in 2014.
Professor Goel was a research fellow and technical advisor at Twitter, Inc. from July 2009 to Aug 2014.