Beyond Voting: Markets and Platforms for Societal Decision Making

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 some algorithmic and market-inspired approaches towards
large scale decision making that we are exploring. In particular, we
will describe three recent results:

(1) We will describe Stanford's Participatory Budgeting (PB) platform,
used by many cities in North America, along with novel voting methods
inspired by PB, including knapsack voting. We will also show how a
series of incremental votes can lead to an optimum solution to many
budgeting problems. The incremental voting algorithms are inspired by
prediction markets, where each subsequent participant provides a small
correction to the market

(2) We will describe how one can construct a market for
public-decision-making inspired by the celebrated work of Foley and
others on public good markets

3) We will describe a deliberation mechanism where a group comes to a
decision by a series of pairwise negotiations. We will show that this
results in provably good decisions on median spaces.

The above results are in increasing order of interaction among
decision makers — in the first, individuals are reacting to an entire
decision made by the rest of the society; in the second, individuals
are participants in a market that looks very much like a traditional
Fisher market, and in the third, participants interact with other
participants directly as opposed to via aggregated prices.

This represents joint work with Tanja Aitamurto, Brandon Fain, Nikhil
Garg, Vijay Kamble, Anilesh Krishnaswamy, David Marn, Kamesh Munagala,
Benjamin Plaut, 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, 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.

Professor Goel was a research fellow and technical advisor at Twitter, Inc. from July 2009 to Aug 2014.