About this Workshop
The 2024 INFORMS Workshop on Market Design will be held on July 8th, 2024 in conjunction with the EC 2024 conference. The workshop will bring together researchers and practitioners that work on market design, and will represent a broad range of perspectives from more theoretical to more applied and empirical work. As with previous iterations, the workshop is sponsored and organized by the INFORMS Section on Auctions and Market Design, i.e., the workshop is a successor of
Workshop Theme
Market design is a field of applied and theoretical research that sits comfortably on the intersection of computer science, economics, and operations research. In recent decades, the theory and applications of market design have blossomed. In this workshop, we will focus on a set of promising, new applications of market design. In particular, we are interested in applications of market design which involve optimization with complex allocation constraints, vast datasets and machine learning, and dynamic pricing issues. We also want to explore research areas which, despite receiving theoretical attention, have not yet made an impact on practice. We are also interested in empirical work on evaluating mechanisms and approaches in practice. Topics include but are not limited to:
Machine learning and generative AI in market design
Mathematical optimization in markets
Pricing and competitive equilibria in markets
Iterative multi-object auctions
Matching with constraints and complex preferences
Workshop Format
The workshop will be held in conjunction with EC 2024 at Yale University in New Haven. Please see the main EC website for details on how to register for the conference and the workshop.
The workshop will consist of 6-8 invited talks, from academics and practitioners in Computer Science, Economics, and Operations Research. Speakers and program will be posted on this website soon.
In addition to the invited talks, there will be a contributed poster session. Please see the rubric "Call for Posters" below on instructions how to submit your work.
Confirmed Speakers
Shuchi Chawla
The University of Texas at Austin
Negin Golrezaei
MIT
Yannai Gonczarowski
Harvard University
Thodoris Lykouris
MIT
Aranyak Mehta
Google Research
Zhuoran Yang
Yale University
Program (in Progress)
8:00-8:30 Breakfast
8:30-9:15 Yannai Gonczarowski (Harvard)
9:15-10:00 Zhuoran Yang (Yale)
STRIDE: A Tool-Assisted LLM Agent Framework for Strategic and Interactive Decision-Making
Large Language Models (LLMs) like GPT-4 have revolutionized natural language processing, showing remarkable linguistic proficiency and reasoning capabilities. However, their application in strategic multi-agent decision-making environments is hampered by significant limitations including poor mathematical reasoning, difficulty in following instructions, and a tendency to generate incorrect information. These deficiencies hinder their performance in strategic and interactive tasks that demand adherence to nuanced game rules, long-term planning, exploration in unknown environments, and anticipation of opponents' moves. To overcome these obstacles, this paper presents a novel LLM agent framework equipped with memory and specialized tools to enhance their strategic decision-making capabilities. We deploy the tools in a number of economically important environments, in particular bilateral bargaining and multi-agent and dynamic mechanism design. We employ quantitative metrics to assess the framework's performance in various strategic decision-making problems. Our findings establish that our enhanced framework significantly improves the strategic decision-making capability of LLMs. While we highlight the inherent limitations of current LLM models, we demonstrate the improvements through targeted enhancements, suggesting a promising direction for future developments in LLM applications for interactive environments.
Joint work with Chuanhao Li, Runhan Yang, Tiankai Li, Milad Bafarassat, Dirk Bergemann
Link to paper: https://arxiv.org/abs/2405.16376
10:00-10:30 Coffee break
10:30-11:00 Lightning Talks (Poster session)
11:00-12:00 Poster Session
12:00-13:30 Lunch break
13:30-14:15 Thodoris Lykouris (MIT)
Learning to Defer in Content Moderation: The Human-AI Interplay
Ensuring successful content moderation is vital for a healthy online social platform where it is necessary to responsively remove harmful posts without jeopardizing non-harmful content. Due to the high-volume nature of online posts, human-only moderation is operationally challenging, and platforms often employ a human-AI collaboration approach. A typical heuristic estimates the expected harmfulness of incoming posts and uses fixed thresholds to decide whether to remove the post and whether to send it for human review. This disregards the uncertainty in the machine learning estimation, the time-varying element of human review capacity and post arrivals, and the selective sampling in the dataset (humans only review posts filtered by the admission algorithm). We introduce a model to capture this human-AI interplay. Our algorithm observes contextual information for posts, makes classification and admission decisions, and schedules posts for human review. Only admitted posts receive human reviews on their harmfulness. These reviews help educate the machine-learning algorithms but are delayed due to congestion in the human review system. We propose a near-optimal learning algorithm that balances the classification loss from a selectively sampled dataset, the idiosyncratic loss of non-reviewed posts, and the delay loss of having congestion in the human review system. To the best of our knowledge, this is the first result for online learning in contextual queueing systems and hence our analytical framework may be of independent interest.
Joint work with Wentao Weng.
Link to paper: https://arxiv.org/pdf/2402.12237
14:15-15:00 Negin Golrezaei (MIT)
Bidding in Uniform Price Auctions for Value Maximizing Buyers
We study the problem of bidding in uniform price auctions widely used in practice. Although these auctions are non-truthful for bidders with quasilinear utility functions, several empirical findings suggest that the auction format induces truthful bidding from the bidders. We attribute this difference in theory and practice to the assumption of the behavioral model of the bidders. In this pursuit, we study uniform price auctions in a repeated setting from the perspective of a value-maximizing buyer who aims to maximize their acquired cumulative value across T rounds, subject to per-round return-on-investment (RoI) constraints. For a RoI-constrained, value-maximizing buyer, we study a generalized version of the uniform bidding format, commonly used in practice, which we term as m-uniform bidding. To characterize the optimal m-uniform bid, we introduce and study the notion of universally feasible (UF) bidding policies, which are robust, meaning that RoI feasibility is obtained regardless of the competitors' bids. We show that the optimal class of UF bidding policies is essentially a generalization of truthful bidding policies, which depends only on the valuation curve of the bidder and target RoI. To measure the performance of UF bidding policies against the optimal bidding policy that is not necessarily UF, we introduce a metric called the Price of Universal Feasibility (PoUF) and establish that PoUF is at most 2, irrespective of m and the upper bound is tight. We further compare the generalized m-uniform bidding interface against the classical uniform bidding format under which m=1, showing the total value under m-uniform bidding increases at most by a factor of m. Numerical simulations on semi-synthetic data demonstrate that UF bidding policies perform significantly better than the derived theoretical bounds.
Joint work with Sourav Sahoo.
Link to paper: arxiv.org/abs/2406.03674
15:00-15:30 Coffee Break
15:30-16:15 Aranyak Mehta (Google)
AI/ML + Econ/Algos: Topics at the Intersection
We will overview a few recent research directions at the intersection of Machine Learning and Algorithms/Economics.
(1.) Can Machine Learning learn Algorithms? In [1, 2], we ask whether it is possible to train networks to discover "TCS-style" algorithms, those whose performance is measured in the worst-case. Can we train them tabula-rasa, and if so, how do the learned algorithms compare with known "human generated" algorithms? We investigate this in the context of a motivating algorithms problem -- the "Adwords" online allocation problem.
(2.) Auctions with LLM Summaries. In [3], we consider the question of how to run ad auctions when an LLM summarizes multiple ad assets into a commercial summary. We provide a feasible and generalizable scheme in which advertiser bids are inputs into the LLM based (RAG) allocation, with good auction incentives.
(3.) Low-regret dynamics in auction bidding. In [4] we study the equilibria among bidders playing low-regret bidding strategies in the context of simple second- or first-price auctions, and compare them to equilibria predicted by rational behavior in economic theory.
Talk based on:
[1]: A new dog learns old tricks: RL finds classic optimization algorithms. Weiwei Kong, Christopher Liaw, Aranyak Mehta, D. Sivakumar (ICLR 2019)
https://openreview.net/pdf?id=rkluJ2R9KQ
[2]: Learning Robust Algorithms for Online Allocation Problems Using Adversarial Training. (ICLR multiagent workshop 2022) Goran Zuzic, Di Wang, Aranyak Mehta, D. Sivakumar.
https://arxiv.org/abs/2010.08418
[3]: Auctions with LLM Summaries (KDD'24, to appear) Kumar A. Dubey, Zhe Feng, Rahul Kidambi, Aranyak Mehta, Di Wang
https://arxiv.org/abs/2404.08126
[4]: Convergence Analysis of No-Regret Bidding Algorithms in Repeated Auctions. (AAAI 2021) Zhe Feng, Guru Guruganesh, Christopher Liaw, Aranyak Mehta, Abhishek Sethi. https://arxiv.org/abs/2009.06136
16:15-17:00 Shuchi Chawla (U Texas at Austin)
Call for Posters
Update: The submission deadline has passed. See list of accepted posters below.
Call for Posters: The 2024 INFORMS Workshop on Market Design (EC 2024, Yale)
The 2024 INFORMS Workshop on Market Design will be held on July 8th, 2024 in conjunction with the EC 2024 conference at Yale U, New Haven. The workshop is sponsored by the INFORMS Section on Auctions and Market Design.
In addition to invited talks the workshop will feature a one hour poster session on recent work broadly related to market design, including applied, theoretical and empirical work. To submit a poster for consideration, please send an abstract of no more than 300 words via email to informs-market-design-workshop@googlegroups.com
Submission deadline: 11:59 pm PT on Monday, June 24.
Accept/reject notifications: Wednesday, June 26.
For each author, please indicate if they are a PhD student or postdoc, and if they are on the academic job market. Job market candidates are especially encouraged to submit their work.
Accepted Posters
Jerry Anunrojwong, Columbia. Battery Operations in Electricity Markets: Strategic Behavior and Distortions. [link]
Giannis Fikioris, Cornell. Online Resource Sharing via Dynamic Max-Min Fairness: Efficiency, Robustness and Non-Stationarity. [arXiv]
Yoav Gal-Tzur, Tel Aviv University. The Query Complexity of Contracts. [arXiv]
Anand Kalvit, Stanford. Incentivized Exploration via Filtered Posterior Sampling. [arXiv]
Soonbong Lee, Yale. Dynamic Matching with Post-allocation Service and its Application to Refugee Resettlement. [SSRN]
Andreas Maggiori, Columbia. Fair Secretary with ML-Advice (Title TBC). [link]
Aniket Murhekar, University of Illinois Urbana-Champaign. Fair Allocation of Chores (Title TBC). [link]
Farzad Pourbabaee, Caltech. Individual and Collective Welfare in Risk Sharing with Many States. [arXiv]
Matias Romero, Columbia. Potential-Based Greedy Matching for Dynamic Pooling of Delivery Orders. [link]
Sourav Sahoo, MIT. Bidding in Uniform Price Auctions for Value Maximizing Buyers. [arXiv]
Brian Zhang, Carnegie Mellon. Computing Optimal Equilibria and Mechanisms via Learning in Zero-Sum Extensive-Form Games. [arXiv]
