Note: To attend these talks, you should first register for EC’21. After that, you will be able to access the virtual venue.
Tuesday, July 20 at 11AM-12PM Eastern Time
Leeat Yariv (Princeton University)
Disentangling Exploration from Exploitation
Abstract: A key tension in the study of experimentation revolves around the exploration of new possibilities and the exploitation of prior discoveries. Starting from Robbins (1952), a large literature in economics and statistics has married the two: Agents experiment by selecting potentially risky options and observing their resulting payoffs. This framework has been used in many applications, ranging from pricing decisions to labor market search. Nonetheless, in many applications, agents’ exploration and exploitation need not be intertwined. An investor may study stocks she is not invested in, an employee may explore alternative jobs while working, etc. The current paper focuses on the consequences of disentangling exploration from exploitation.
This talk will cover some insights generated from work joint with Alessandro Lizzeri (Princeton University) and Eran Shmaya (Stony Brook University). We consider the classical Poisson bandit problem that has served as the canonical model for experimentation. We fully characterize the solution when exploration and exploitation are disentangled, both for the “good news” and “bad news” settings. We illustrate the stark differences the optimal exploration policy exhibits compared to the standard setting. In particular, we show that agents optimally utilize the option to observe projects different than the ones they act on. In the good news case, the optimal policy entails the continued exploration of a singular arm — no matter how pessimistic the decision-maker becomes about that arm — until news arrives. In contrast, in the bad news, exploration can involve the use of more than a single arm, but entails at most one switch. In all settings, the separation of exploration from exploitation guarantees asymptotic efficiency.
Bio: Leeat Yariv is the Uwe E. Reinhardt Professor of Economics at Princeton University. She is also the director of the Princeton Experimental Laboratory for the Social Sciences (PExL), which she opened. She is the lead editor of AEJ: Micro and has served on the editorial boards of multiple journals. She is a member of the American Academy of Arts and Sciences and a fellow of the Econometric Society and the Society for the Advancement of Economic Theory. She is also a research associate of the National Bureau of Economic Research (NBER) and a research fellow of the Center for Economic and Policy Research (CEPR). Yariv’s work focuses on market design, social networks, and political economy. She uses theory, lab experiments, and field studies to understand how individuals connect to one another and how they make decisions, on their own and collectively.
Thursday, July 22 at 11AM-12PM Eastern Time
Ashish Goel (Stanford University)
Research Directions in Deliberative and Participatory Democracy
Abstract: Social choice is often thought of as the study of voting methods. But collective decision making often involves negotiation and deliberation. We will briefly describe three approaches that include a market, a bargaining step, or a deliberative step. The first direction involves designing a public decision market. The second involves sequential bargaining. And the third is deliberative polling. We will also describe an online deliberation platform that we are developing which incorporates automated moderation tools, and our recent efforts at enhancing the scope of participatory budgeting in the US. For each direction, we will list several open problems that we believe will expand the field of social choice and improve the state of collective decision making.
This represents joint work with current and past students in the Stanford Crowdsourced Democracy Team, the Stanford Center for Deliberative Democracy, and Kamesh Munagala’s research group at Duke University.
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.