June 16th, 2016
YouTube competes with Hollywood as an entertainment channel, and also supplements Hollywood by acting as a distribution mechanism. Twitter has a similar relationship to news media, and Coursera to Universities. But there are no online alternatives for making democratic decisions at large scale as a society. In this talk, we will describe two algorithmic approaches towards large scale decision making that we are exploring.
a) Knapsack voting and participatory budgeting: All budget problems are knapsack problems at their heart, since the goal is to pack the largest amount of societal value into a budget. This naturally leads to “knapsack voting” where each voter solves a knapsack problem, or comparison-based voting where each voter compares pairs of projects in terms of benefit-per-dollar. We analyze natural aggregation algorithms for these mechanisms, and show that knapsack voting is strategy-proof. We will also describe our experience with helping implement participatory budgeting in close to two dozen cities and municipalities, and briefly comment on issues of fairness.
b) Triadic consensus: Here, we divide individuals into small groups (say groups of three) and ask them to come to consensus; the results of the triadic deliberations in each round form the input to the next round. We show that this method is efficient and strategy-proof in fairly general settings, whereas no pair-wise deliberation process can have the same properties.
This is joint work with Tanja Aitamurto, Brandon Fain, Anilesh Krishnaswamy, David Lee, Kamesh Munagala, and Sukolsak Sakshuwong. Bio:
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, 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 was a research fellow at Twitter from 2009-14 where he designed and prototyped Twitter’s monetization and personalization algorithms. Professor Goel is also Principal Scientist at Teapot, Inc.