Opinions, Conflict, and Abuse in a Networked Society (OCeANS)

• Ashish Goel, Stanford University

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

Title: Decision Making at Scale: Algorithms and Markets

Abstract:

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 our platform for participatory budgeting, which has been used in over 40 elections. We will then 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 Brandon Fain, Nikhil Garg, Vijay Kamble, David Marn, Kamesh Munagala, Benjamin Plaut, and Sukolsak Sakshuwong.

• Aris Anagnostopoulos, Sapienza University of Rome

Aris Anagnostopoulos is an Associate Professor of Computer Science and Engineering at Sapienza University of Rome. He obtained his Ph.D. from Brown University in 2006, and afterwards he did a postdoc at Yahoo! Research in Santa Clara, CA. His main research interests include randomized and approximation algorithms and their applications on data science.

Title: The Wisdom of Crowds: How wise are they and how to use their wisdom?

Abstract:

Ask 100 people how long is River Thames (what do you think?) and average their answers. The value obtained will be probably very close to the true value. Scientists have observed such phenomena, leading to coining the term "Wisdom of Crowds," which, broadly, is the observation that the aggregation of individual opinions can lead to a collective knowledge. It has a variety of applications such as tagging systems, Wikipedia, crowdsourcing, or even democracy.

In this talk I will present some research on testing and making use of the wisdom-of-crowds hypothesis. What happens when people interact and influence each other? How do conspiracy theories diffuse? When is it better to use the crowd and when to resort to experts, and how can we apply these ideas into the design of crowdsourcing algorithms? These are the types of questions that we will address in this talk. (By the way, according to Wikipedia, River Thames is 215 miles (315km) long.)

• Kristina Lerman, University of Southern California

Kristina Lerman is a Principal Scientist at the University of Southern California Information Sciences Institute and holds a joint appointment as a Research Associate Professor in the USC Computer Science Department. Trained as a physicist, she now applies network- and machine learning-based methods to problems in social computing and social media analysis. Her work explores how cognitive heuristics and network structure bias our perceptions of online information.

Title: Cognitive Biases and the Limits of Crowd Wisdom

Abstract:

The many decisions people make about what information to attend to affect emerging trends, the diffusion of information in social media, and performance of crowds in peer evaluation tasks. Due to constraints of available time and cognitive resources, the ease of discovery strongly affects how people allocate their attention. Through empirical analysis and online experiments, we identify some of the cognitive heuristics that influence individual decisions to allocate attention to online content and quantify their impact on individual and collective behavior. Specifically, we show that the position of information in the user interface strongly affects whether it is seen, while explicit social signals about its popularity increase the likelihood of response. These heuristics become even more important in explaining and predicting behavior as cognitive load increases. The findings suggest that cognitive heuristics and information overload bias collective outcomes and undermine the “wisdom of crowds” effect.