Event Entry
What and Who
Decision making at scale: Algorithms, Mechanisms, and Platforms
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.
Date, Time and Location
Abstract
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.
Contact
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