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Representative and Deliberative Social Choice

Ashish Goel – Professor, Stanford

Thu, 27-Apr-2023 / 4:00pm / Packard 202

Abstract

In this talk, we will touch upon two aspects of social choice that have gained additional salience in the digital age: representation and deliberation. In the process, we will also touch upon two popular social choice platforms that our group has developed.

We will first use our experiences with the Stanford Participatory Budgeting Platform to illustrate the difficulty of getting outcomes which represent the views of an entire population. One possible approach towards achieving representative outcomes is to up-weight the opinions of underrepresented demographic minorities. We suggest another complementary approach that attempts to take under-represented minority interests into account without any a priori notion of the demographic composition of this under-represented minority. The core of this approach is the representational polytope – the set of all possible weight vectors that can be assigned to participants which are consistent with some small fraction of the overall population being under-represented. We then consider all outcomes which are optimum for some weight vector in the representational polytope. While this seems like a difficult object to work with, we show that in many common cases (including participatory budgeting), it is computationally tractable to test membership in this outcome set. We will illustrate this approach with results from a multi-week budgetary feedback process that we ran with the city of Austin in 2020; the George Floyd murder happened in the middle of this process, making issues of representation both more complex and more salient.

We will then describe the Stanford Deliberation platform, which is a video-conferencing platform for civic deliberations that incorporates an automated moderator. We will provide empirical results from this platform, and then also formally describe a sequential negotiation process that has provably good properties.

This represents joint work with several members and collaborators of the Stanford Crowdsourced Democracy Team, including Lodewijk Gelauff, Sukoslak Sakshuwong, Kamesh Munagala, Brandon Fain, Mohak Goyal, Sahasrajit Sarmasarkar, and Sungjin Im.

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, 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, an Edelman Laureate in 2014, and a co-winner of the SigEcom Test of Time Award in 2018. Professor Goel was a research fellow and technical advisor at Twitter, Inc. from July 2009 to Aug 2014.