Ashish Goel
Stanford MS&E
Decision Making at Scale: Algorithms, Mechanisms, and Platforms for Crowdsourced Democracy

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 Washington has no such counterpart; there are no online alternatives for making democratic decisions at large scale as a society. As opposed to building consensus and compromise, public discussion boards often devolve into flame wars when dealing with contentious socio-political issues. In this talk, we will describe some of the challenges in and approaches for solving this broad problem.

We will first describe an opinion formation process that leads to polarization. We will then describe three algorithmic approaches towards large scale decision making that we are exploring, with some preliminary experimental results:

a) 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 inout 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.

b) Knapsack voting and comparison based voting: All budget problems are knapsack problems at their heart, since the goal is to pack as much "societal value" into a "spending capacity". We will describe our experience with implementing knapsack voting in Chicago's 49th ward and Vallejo, and show protocols that are weakly strategy-proof. We will also show that comparison voting can elicit an approximate Condorcet winner efficiently when one exists, and describe our experience with comparison-based voting in an experiment in Finland.

c) Probabilistic consensus: We will describe a probabilistic voting game which rewards consensus, and present open problems.

This is joint work with Tanja Aitamurto, Pranav Dandekar, Anilesh Krishnaswamy, Helene Landermore, David Lee, and Sukolsak Sakshuwong.

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.