Auctions and Market Design Online Seminar Series

About the Seminar

The aim of this interdisciplinary seminar is to discuss pioneering and impactful work in the broad area of market design. Theoretical, computational, and experimental work as well as field studies will be featured. A wide range of applications, ranging from online advertising and labor markets to networks and platforms, will be presented. The seminar features research talks and expository talks to highlight trends in the field.

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Organization

The seminar is organized by Ozan Candogan (Chicago Booth), Vahideh Manshadi (Yale), and Fanyin Zheng (Columbia). Akshaya Suresh (Yale) serves as student organizer.

The seminar will be bi-weekly on Fridays at 1-2 pm ET (10-11 am PT).

Seminar link

Email List

If this seminar interests you and you would like to be notified of upcoming speakers, you can join our email list.

Next Speaker

January 22 - Asuman Ozdaglar (MIT)

Title: Analysis and Interventions in Large Network GamesAsuOzdaglar
Abstract: In many social and economic settings, decisions of individuals are affected by the actions of their friends, colleagues, and peers. Examples include adoption of new products and innovations, opinion formation and social learning, public good provision, financial exchanges and international trade. Network games have emerged as a powerful framework to study these settings with particular focus on how the underlying patterns of interactions, governed by a network, affect the economic outcomes. For tractability reasons, much of the work in this area studied games with special structure (e.g., quadratic cost functions, scalar non-negative strategies) or special properties (e.g., games of strategic complements or substitutes). In this talk, we first present a unified framework based on a variational inequality reformulation of the Nash equilibrium to study equilibrium properties of network games including existence and uniqueness, convergence of the best response dynamics and comparative statics. Our framework extends the literature in multiple dimensions, covering games with general strategic interactions and multidimensional and constrained strategy sets. In the second part of the talk, we will present a new class of infinite populations games, graphon games, that can capture heterogenous local interactions. We will show that equilibria in graphon games can approximate the equilibria of large network games sampled from the graphon. We also show that (under some regularity assumptions on the graphon), interventions based on graphon games can be designed using computationally tractable optimization problems and with much less information than the entire network structure.

Schedule

Winter Series

Jan 15 Paul Milgrom (Stanford University)

Title: Investment Incentives in Near-Optimal Mechanisms milgrom-profile.jpg?itok=zbTDS0NEAbstract: In many real-world resource allocation problems, optimization is computationally intractable, so any practical allocation mechanism must be based on an approximation algorithm. We study investment incentives in strategy-proof mechanisms that use approximations. In sharp contrast with the Vickrey-Clark-Groves mechanism, for which individual returns on investments are aligned with social welfare, some algorithms that approximate efficient allocation arbitrarily well can nevertheless create misaligned investment incentives that lead to arbitrarily bad overall outcomes.  However, if a near-efficient algorithm "excludes bossy negative externalities," then its outcomes remain near-efficient even after accounting for investments. A weakening of this "XBONE" condition is necessary and sufficient for the result. Joint work with Mohammad Akbarpour, Scott Duke Kominers, and Shengwu Li.


Jan 22 Asuman Ozdaglar (MIT)

Title: Analysis and Interventions in Large Network Games AsuOzdaglarAbstract: In many social and economic settings, decisions of individuals are affected by the actions of their friends, colleagues, and peers. Examples include adoption of new products and innovations, opinion formation and social learning, public good provision, financial exchanges and international trade. Network games have emerged as a powerful framework to study these settings with particular focus on how the underlying patterns of interactions, governed by a network, affect the economic outcomes. For tractability reasons, much of the work in this area studied games with special structure (e.g., quadratic cost functions, scalar non-negative strategies) or special properties (e.g., games of strategic complements or substitutes). In this talk, we first present a unified framework based on a variational inequality reformulation of the Nash equilibrium to study equilibrium properties of network games including existence and uniqueness, convergence of the best response dynamics and comparative statics. Our framework extends the literature in multiple dimensions, covering games with general strategic interactions and multidimensional and constrained strategy sets. In the second part of the talk, we will present a new class of infinite populations games, graphon games, that can capture heterogenous local interactions. We will show that equilibria in graphon games can approximate the equilibria of large network games sampled from the graphon. We also show that (under some regularity assumptions on the graphon), interventions based on graphon games can be designed using computationally tractable optimization problems and with much less information than the entire network structure.



Jan 29 Eric Budish (University of Chicago)

Feb 12 Eva Tardos (Cornell University)

Feb 26 Nikhil Agarwal (MIT)

Mar 12 Garrett van Ryzin (Cornell University)

Mar 26 Gerard Cachon (University of Pennsylvania)


Fall Series

December 18 - Matthew Jackson (Stanford University)

Title: Learning through the Grapevine: The Impact of Noise and the Breadth and Depth of Social Networks Abstract: We examine how well people learn when information is noisily relayed from person to person; and we study how communication platforms can improve learning without censoring or fact-checking messages. We analyze learning as a function of social network depth (how many times information is relayed) and breadth (the number of relay chains accessed). Noise builds up as depth increases, so learning requires greater breadth. In the presence of mutations (deliberate or random) and transmission failures of messages, we characterize sharp thresholds for breadths above which receivers learn fully and below which they learn nothing. When there is uncertainty about mutation rates, optimizing learning requires either capping depth, or if that is not possible, limiting breadth by capping the number of people to whom someone can forward a message. Limiting breadth cuts the number of messages received but also decreases the fraction originating further from the receiver, and so can increase the signal to noise ratio. Finally, we extend our model to study learning from message survival: e.g., people are more likely to pass messages with one conclusion than another. We find that as depth grows, all learning comes from either the total number of messages received or from the content of received messages, but the learner does not need to pay attention to both.


November 13 - Winners of the Michael H. Rothkopf Junior Researcher Paper Prize

Yannai A. Gonczarowski (Microsoft Research, Postdoctoral Researcher)

Title: Matching for the Israeli "Mechinot" Gap-Year Programs: Handling Rich Diversity Requirements We describe our experience with designing and running a matching market for the Israeli "Mechinot" gap-year programs. The main conceptual challenge in the design of this market was the rich set of diversity considerations, which necessitated the development of an appropriate preference-specification language along with corresponding choice-function semantics, which we also theoretically analyze. Our contribution extends the existing toolbox for two-sided matching with soft constraints. This market has been run annually since 2018, and in 2020 matched 1,937 high-school seniors (out of a total of 6,118 candidates) to 42 different programs. Joint work with Lior Kovalio, Noam Nisan, Assaf Romm.


Irene Lo (Assistant Professor, Management Science and Engineering at Stanford University)

Title: The Cutoff Structure of Top Trading Cycles in School Choice This paper develops a tractable theoretical framework for the Top Trading Cycles (TTC) mechanism for school choice that allows quantifying welfare and optimizing policy decisions. We compute welfare for TTC and Deferred Acceptance (DA) under different priority structures, and find that the choice of priorities can have larger welfare implications than the choice of mechanism. We solve for the welfare-maximizing distributions of school quality for parametrized economies, and find that optimal investment decisions can be very different under TTC and DA. Our framework relies on a novel characterization of the TTC assignment in terms of a cutoff for each pair of schools. These cutoffs parallel prices in competitive equilibrium, with students' priorities serving the role of endowments. We show that these cutoffs can be computed directly from the distribution of preferences and priorities in a continuum model, and derive closed-form solutions and comparative statics for parameterized settings. The TTC cutoffs clarify the role of priorities in determining the TTC assignment, but also demonstrate that TTC is more complicated than DA. Joint work with Jacob Leshno.


Scott Rodilitz (PhD Student in Operations, Yale School of Management)

Title: Online Policies for Efficient Volunteer Crowdsourcing Nonprofit crowdsourcing platforms encourage volunteers to complete tasks by using nudging mechanisms to notify a subset of volunteers with the hope that at least one of them responds positively. However, since excessive notifications may reduce volunteer engagement, the platform faces a trade-off between notifying more volunteers for the current task and saving them for future ones. Motivated by these applications, we introduce the online volunteer notification problem and develop two online randomized policies that achieve constant-factor guarantees. Further we demonstrate the effectiveness of our policies by testing them on data from a volunteer-based food recovery platform. Joint work with Vahideh Manshadi.


October 30 - Jon Kleinberg (Cornell University)

Title: Fairness and Bias in Algorithmic Decision-Making Jon KleinbergAbstract: As algorithms trained via machine learning are increasingly used as a component of screening decisions in areas such as hiring, lending, and education, discussion in the public sphere has turned to the question of what it means for algorithmic classification to be fair to different groups. We consider several of the key fairness conditions that lie at the heart of these debates, and discuss recent research establishing inherent trade-offs between these conditions. We also explore how the complexity of a classification rule interacts with its fairness properties, showing how natural ways of approximating a classifier via a simpler rule can act in conflict with fairness goals. The talk will be based on joint work with Jens Ludwig, Sendhil Mullainathan, Manish Raghavan, and Cass Sunstein.


October 16 - John Birge (University of Chicago)

Title: Increasing Efficiency in Electricity Market Auctions WjAlWy9xQkanvUuhXTWBAbstract: Electricity markets in much of the world are organized with day-ahead and real-time components for energy and in some place regular auctions of financial transmission rights that depend on the energy prices. The form of these organizations leads to a degree of inherent efficiency and instability due to misalignments of incentives. This talk will describe potential mechanisms for alleviating these inefficiencies including the inclusion of some form of stochastic clearing, flexibility in the forms of bids, and penalties for inefficiencies caused by speculative bidders.






October 02 - Alvin Roth (Stanford University)

Title: Kidney Exchange: an Operations Perspective Abstract: Many patients in need of a kidney transplant have a willing but incompatible (or poorly matched) living donor. Kidney exchange programs arrange exchanges among such patient-donor pairs, in cycles and chains of exchange, so each patient receives a compatible kidney.  Kidney exchange has become a standard form of transplantation in  the  United  States  and  a  few  other  countries,  in  large  part  because  of  continued attention  to  the  operational  details  that  arose  as  obstacles  were  overcome  and  new obstacles became relevant. We review some of the key operational issues in the design of  successful  kidney  exchange  programs.  Kidney  exchange  has  yet  to  reach  its  full potential,  and  the  paper  further  describes  some  open  questions  that  we  hope  will continue to attract attention from researchers interested in the operational aspects of dynamic exchange.