Over the past two decades, researchers in theoretical computer science, artificial intelligence, operations research, and economics have joined forces to understand the interplay of incentives and computation. These issues are of particular importance for the Internet, enabling the interaction of large and diverse populations. The Conference on Web and Internet Economics (WINE) is an interdisciplinary forum for exchanging ideas and results on incentives and computation arising from these various fields. WINE 2024 continues the successful tradition of the Conference on Web and Internet Economics (named Workshop on Internet & Network Economics until 2013), held annually from 2005 to the present.
WINE 2024 will take place on 2-5 December, 2024, in Edinburgh, United Kingdom, hosted by the School of Informatics, University of Edinburgh.
Contact:
general-chairs@wine2024.orgContact:
program-chairs@wine2024.orgContact:
local-support@wine2024.orgOver the past two decades, researchers in theoretical computer science, artificial intelligence, operations research, and economics have joined forces to understand the interplay of incentives and computation. These issues are of particular importance for the Internet, enabling the interaction of large and diverse populations. The Conference on Web and Internet Economics (WINE) is an interdisciplinary forum for exchanging ideas and results on incentives and computation arising from these various fields. WINE 2024 continues the successful tradition of the Conference on Web and Internet Economics (named Workshop on Internet & Network Economics until 2013), held annually from 2005 to the present.
WINE 2024 is planned as an in-person event from December 2 to December 5, 2024, hosted by the School of Informatics, University of Edinburgh.
The program will feature invited talks, tutorials, and paper presentations. All paper submissions will be peer-reviewed and evaluated based on the quality of their contribution, originality, soundness, and significance. Submissions about Web and Internet Economics are invited in, but not limited to, the following topics:
Papers deemed to be outside the scope of the WINE conference and/or not of sufficient interest to the WINE community will be desk-rejected.
Paper submission deadline: July 15, 2024, AoE
Author notification: On or before September 16, 2024
Camera-ready deadline: October 8, 2024
The program will feature three keynote talks; the keynote speakers and the topics of their talks will be announced soon.
OpenReview: https://openreview.net/group?id=WINE/2024/Conference
Please note OpenReview's moderation policy for newly created profiles:
Authors are invited to submit papers presenting original research on any research topic related to WINE 2024.
Submissions must be anonymous (see below). A submission should start with the title of the paper followed by a brief summary of the paper’s contributions. This should then be followed by a technical exposition of the main ideas and techniques used to achieve these results, including motivation and a clear comparison with related work. Even if the authors choose to publish a one-page abstract, the submission of the complete paper is necessary to facilitate a comprehensive and rigorous review process.
The submission should not exceed 12 single-spaced pages (excluding references) using reasonable margins (at least one-inch margins all around) and at least 11-points font. If the authors believe that more details are essential to substantiate the claims of the paper, they may include a clearly marked appendix (with no space limit) that will be read at the discretion of the Program Committee. It is strongly recommended that submissions adhere to the specified format and length. Submissions that are clearly too long may be desk-rejected. The above specifications are meant to provide more freedom to the authors at the time of submission. Note that accepted papers will be allocated 18 pages (including references) in the LNCS format in the proceedings (see below).
The proceedings of the conference will be published by Springer-Verlag in the ARCoSS/LNCS series, and will be available for distribution at the conference. Accepted papers will be allocated 18 pages total in the LNCS format in the proceedings. Submissions are encouraged, though not required, to follow the LNCS format (Latex, Word). More information about the LNCS format can be found on the author instructions page of Springer-Verlag.
WINE 2024 will use double-blind reviewing like all other major conferences. Submissions should not reveal the identity of the authors in any way. In particular, authors’ names, affiliations, and email addresses should not appear anywhere in the submission. (In LNCS \author{} and \institute{} fields should not be included.) Authors should refer to their prior work in a neutral manner (i.e., instead of saying “We showed …” say “XYZ et al. showed”). It is acceptable to submit work that has been presented in public (provided there are no published proceedings) or has been uploaded to arXiv or similar online archives, provided the submission itself is anonymized.
Questions regarding the submissions can be directed to the PC Co-Chairs at program-chairs@wine2024.org
See here.
A conflict of interest (COI) is limited to the following categories:
Authors will have the opportunity to declare COIs with (Senior) Program Committee members. This must be done separately for each submission. Declaring COIs prevents the specified person from reviewing a paper, thereby constraining the matching process and potentially negatively impacting review quality. For this reason, COIs should not be declared automatically based on a prior relationship (e.g., coauthor, friend, colleague in the same institution, etc.). (Senior) Program Committee members can also declare a COI with authors as well as with specific papers. Authors are kindly asked to verify that their Open Review profiles are up-to-date with respect to relations and conflicts.
WINE 2024 adopts the official ACM policy against plagiarism.
A best paper award and a best student paper award will be given. The awarded papers will be chosen among those that appear in full length (18 pages) in the proceedings.
To accommodate the publishing traditions of different fields, authors of accepted papers can ask that only a one-page abstract of the paper appear in the proceedings, along with a URL pointing to the full paper. The authors should guarantee the link to be reliable for at least two years. This option is available to accommodate subsequent publication in journals that would not consider results that have been published in preliminary form in conference proceedings. Such papers must be submitted and formatted just like papers submitted for full-text publication. Simultaneous submission of results to another conference with published proceedings is not allowed. Results previously published or presented at another archival conference prior to WINE 2024, or published (or accepted for publication) at a journal prior to the submission deadline of WINE 2024, will not be considered. Simultaneous submission of results to a journal is allowed only if the authors intend to publish the paper as a one-page abstract in WINE 2024. Papers that are accepted and appear as a one-page abstract can be subsequently submitted for publication in a journal but may not be submitted to any other conference that has a published proceeding.
Upon submission, WINE authors would have a chance to select at most one from the following journals:
How does it work? If the authors of a paper accepted to WINE 2024 plan to use the Forward-to-Journal option, they must submit a one-page extended abstract by the deadline for the camera-ready version of the conference proceedings. The authors then have the option of submitting their journal paper by January 22, 2025, to the journal they have selected. The cover letter to the journal should specify that the submission is part of the WINE 2024 Forward-to-Journal process. The authors should also include a formal response document to the WINE 2024 conference reviews and explain how those were addressed in the revised manuscript. In case the particular journal of choice needs to have a de-anonymized version of the conference reviews on the submission, WINE 2024 will provide them upon request by the journal. Note that a journal's participation in the WINE 2024 Forward-to-Journal option does not mean that other forms of previous publication of the submission are acceptable for the journal.
What are the implications? The journal's department editor and/or associate editor can use the conference reviews to guide the decision-making process in whatever way the journal finds appropriate. We suspect the AEs might choose referees from among the set of conference reviewers, especially if they found the conference reviews informative. We would like to emphasize, however, that the conference reviewers are not required to accept such review requests. Furthermore, journals are not required to accept these papers (and may even choose to desk-reject them depending on fit).
Note: At least one author from each accepted paper must complete a (regular or student, depending on their academic status) registration to attend the conference!
Early registration | Late registration | |
---|---|---|
Regular | £370 | £450 |
Student | £310 | £400 |
Associate Professor of Computer Science
University of Salento, Italy
Abstract: We introduce a new notion of deterministic stable solution for non-cooperative games, termed μ-subsidized equilibrium. It assumes that an amount of money μ can be used to stabilize a strategy profile that, otherwise, would not be accepted by (some of) the players. Roughly speaking, a strategy profile is a μ-subsidized equilibrium if the sum of the utility losses of all players not using their best-response does not exceed μ, that is, there is enough money to refund all players experiencing a regret in the given profile. An important property of this notion is that, for a sufficiently high value of μ, a μ-subsidized equilibrium always exists and can even be computed in polynomial time; also, existence of an efficient μ-subsidized equilibrium can be guaranteed. Thus, determining for which values of μ some of these properties can or cannot be achieved becomes an intriguing question. We provide initial results towards this direction for some well-known classes of games.
Short Bio: Vittorio Bilò is an Associate Professor of Computer Science at the Department of Mathematics and Physics "Ennio De Giorgi" of the University of Salento. He received Ph.D. (2005) and M.S. (2001) degrees in Computer Science from the University of L'Aquila. His research interests lie in the area of Algorithm Design and Analysis with applications to Game Theory, Fair Division and Interconnection Networks. His Ph.D. thesis "Pricing and Equilibria in Non-Cooperative Networks" was named the best Italian Ph.D. thesis in Theoretical Computer Science of 2005 by the Italian Chapter of the European Association for Theoretical Computer Science.
Gordon McKay Professor of Computer Science
Harvard University, USA
Abstract: When information is generated, collected, and disseminated by people, ensuring its integrity is critical for downstream use. I will discuss some of our efforts to improve information integrity through the intentional design of mechanisms. For example, we propose using information design as a tool for social media platforms to combat the spread of misinformation. As platforms can predict the popularity and misinformation states of to-be-shared posts, and users are motivated to only share popular content, platforms can strategically reveal this informational advantage to discourage users from sharing misinformed content. We characterize the platform’s optimal information design scheme and the resulting utility when the platform has access to an imperfect classifier for predicting post status, and we analyze the convergence of the optimal scheme in a performative process.
Short Bio: Yiling Chen is a Gordon McKay Professor of Computer Science at Harvard University. Her research spans computer science, economics, and social sciences, focusing on the social dimensions of computational systems. Her work has received best paper awards at several conferences, including ACM EC, AAMAS, ACM FAccT (formerly ACM FAT*), and ACM CSCW. She has co-chaired major conferences such as WINE (2013), ACM EC (2016), HCOMP (2018), and AAAI (2023) and has served as an associate editor for several leading journals.
Staff Research Scientist
Google Switzerland
Abstract: Combinatorial contracts are emerging as a key paradigm in algorithmic contract design, paralleling the role of combinatorial auctions in algorithmic mechanism design. In this paper we study natural combinatorial contract settings involving teams of agents, each capable of performing multiple actions. This scenario extends two fundamental special cases previously examined in the literature, namely the single-agent combinatorial action model of [Duetting et al., 2021] and the multi-agent binary-action model of [Babaioff et al., 2012, Duetting et al., 2023].
We study the algorithmic and computational aspects of these settings, highlighting the unique challenges posed by the absence of certain monotonicity properties essential for analyzing the previous special cases. To navigate these complexities, we introduce a broad set of novel tools that deepen our understanding of combinatorial contracts environments and yield good approximation guarantees.
Our main result is a constant-factor approximation for submodular multi-agent multi-action problems with value and demand oracles access. This result is tight: we show that this problem admits no PTAS (even under binary actions). As a side product of our main result, we devise an FPTAS, with value and demand oracles, for single-agent combinatorial action scenarios with general reward functions, which is of independent interest. We also provide bounds on the gap between the optimal welfare and the principal's utility. We show that, for subadditive rewards, perhaps surprisingly, this gap scales only logarithmically (rather than linearly) in the size of the action space.
Joint work with Tomer Ezra, Michal Feldman, and Thomas Kesselheim (paper)
Short Bio: Paul Dütting is a Staff Research Scientist at Google Switzerland and a visiting faculty member at the London School of Economics (LSE). He specializes in the intersection of economics and computation. Previously, he was an Associate Professor of Mathematics at LSE and held postdoctoral positions at Stanford University, Cornell University, and ETH Zürich. Paul earned his PhD in Computer Science from EPFL Lausanne under the supervision of Monika Henzinger. His research has been recognized with Best Paper awards at WWW 2024, EC 2019, and EC 2012, as well as an LSE Excellence in Education Award.
K. T. Li Professor of Engineering
Stanford University, USA
&
Visiting Chair Professor
Shanghai Jiao Tong University and Chinese University of Hong Kong (ShenZhen), China
Abstract: This talk aims to respond to the question: are the classical mathematical optimization/game models, theories, and algorithms remaining valuable in the AI and LLM era? We present several cases to show how mathematical programming and AI/Machine-Learning technologies complement each other. In particular, we discuss advances in quantitative modeling and LP, SDP and/or Market-Equilibrium computing aided by LLM, First-Order methods and the GPU Implementation. On the other hand, we describe how classic optimization techniques can be applied to accelerate the LLM Training and Fine-Tuning.
Short Bio: Yinyu Ye is currently the K.T. Li Professor of Engineering at Department of Management Science and Engineering and Institute of Computational and Mathematical Engineering, Stanford University; and visiting chair professor of Shanghai Jiao Tong University. His current research topics include Continuous and Discrete Optimization, Data Science and Applications, Algorithm Design and Analyses, Algorithmic Game/Market Equilibrium, Operations Research and Management Science etc.; and he was one of the pioneers on Interior-Point Methods, Conic Linear Programming, Distributionally Robust Optimization, Online Linear Programming and Learning, Algorithm Analyses for Reinforcement Learning&Markov Decision Process and nonconvex optimization, and etc. He and his students have received numerous scientific awards, himself including the 2006 INFORMS Farkas Prize (Inaugural Recipient) for fundamental contributions to optimization, the 2009 John von Neumann Theory Prize for fundamental sustained contributions to theory in Operations Research and the Management Sciences, the inaugural 2012 ISMP Tseng Lectureship Prize for outstanding contribution to continuous optimization (every three years), the 2014 SIAM Optimization Prize awarded (every three years).
WINE 2024 will feature tutorials, designed to highlight emerging topics.
WINE 2024 will host only in-person tutorial sessions, held as one-day events on December 2, 2024. Each tutorial session will be scheduled for a 2-hour time slot.
Tutorial proposals should ideally be one page in length and include the following:
Tutorial proposals should be emailed to: program-chairs@wine2024.org
Aggelos Kiayias - University of Edinburgh, UK and Input Output Global (IOG)
Elias Koutsoupias - University of Oxford, UK
Evangelos Markakis - Athens University of Economics & Business, Greece and Input Output Global (IOG)
This tutorial proposal aims to provide an overview of game-theoretic and economic aspects arising in the design and analysis of blockchain protocols. In recent years, blockchain systems have emerged as a game-theoretic arena, fostering a wide range of economic interactions among their stakeholders. To provide some prominent examples, many protocols use auctions or other forms of mechanisms for determining the transactions to be included in a block. These mechanisms, whose purpose is to control congestion of transactions and to compensate the users for their resources, give rise to new research questions in mechanism design. As a second example, along a different dimension, the switch from Proof of Work to Proof of Stake systems has made it easier for all users to participate in the block production process via staking (i.e., by delegating their stake to the actual block validators of the protocol). This in turn has intensified the need for designing appropriate reward-sharing schemes, so that these collaborating entities can share their profits. Beyond such interactions, blockchain communities also envision themselves as self-governed distributed organizations, with their own governance systems. Governance in this context may refer both to elections over critical parameters of a protocol but also to elections for allocating treasury funds for improvement proposals (participatory budgeting). Setting up voting rules in this domain comes with its own challenges and incentive considerations, especially given the anonymity of the users. To this end, during the tutorial, we plan to present recent research and highlight open problems and new directions mainly on the following selected list of topics: 1) transaction fee mechanism design, regarding the analysis of policies for congestion control, 2) design and analysis of reward-sharing schemes, aiming at distributing rewards from block production to the players involved, and 3) governance in blockchain systems, concerning decision-making processes in decentralized autonomous organizations.
Jiarui Gan - University of Oxford, UK
Jibang Wu - University of Chicago, USA
Haifeng Xu - University of Chicago, USA
The principal-agent problem is a well-established model in microeconomics that addresses challenges arising from misaligned interests and information in economic interactions. Over time, the model has evolved along with the increasing complexity of modern decision-making systems. There is growing interest in developing unified theories and computational techniques to solve these problems, with focuses on incentive structures, information asymmetry, as well as sophisticated real-world applications involving unknown or dynamic environments. This tutorial will provide both foundational and cutting-edge insights into the principal-agent problem, covering basic models, computational methods for optimization and learning, as well as practical applications in the areas of contract design, information design, and multi-agent reinforcement learning.
Argyrios Deligkas - Royal Holloway University of London, UK
Nash equilibrium is the standard solution concept in game theory since it provides pre- dictions for the behavior of interacting agents. One of the most fundamental problems in the field is to find a Nash equilibrium of a game. In this tutorial, we will cover the recent advances on the field both in terms of upper bounds and lower bounds. Partici- pants will learn the fundamental theory of two-player games and how to compute Nash equilibria in these games, will see several different kinds of many player games, and the computational challenges behind the computation of Nash equilibria.
The tutorial will cover several recent advancements on the topic of equilibrium computation. For upper bounds the tutorial will explain the recent results for bimatrix game that improved tha approximation ratios after 15 and 7 years respectively. Furthermore, will explain in detail the recently proposed framework of Pure-Circuit that has improved the accessibility and understanding on the topic and has made the derivation of strong PPAD-hardness (and in some cases tight) results much easier.
Yuxuan Lu - Peking University, China
Shengwei Xu - University of Michigan, USA
Eliciting high-quality information from the crowd has become increasingly important, with applications spanning data labeling, reputation systems, peer grading, and peer review. Incentive mechanisms have been proposed to motivate truthful and informative reports by rewarding them more than untruthful or uninformative reports, including scoring rules, peer prediction, and Bayesian Truth Serum. Provable theoretical guarantees have been achieved.
However, traditional approaches are limited to relatively simple report formats, such as probability forecasts or multiple-choice answers. Recent studies have expanded these techniques to the domain of text-based reports, leveraging advancements in Large Language Models (LLMs), which significantly increases the applicability of Information Elicitation mechanisms, especially in applications where textual feedback is common and highly informative, such as academic peer reviews, online business reviews, and social media comments. Moreover, recent studies also suggest that peer prediction mechanisms can be utilized to evaluate LLMs by assessing their ability to generate informative content.
In addition to these current developments, this tutorial will also introduce a practical toolkit that leverages cloud computing resources, Google Colab, to deploy and fine-tune open-source LLMs for information elicitation research. This toolkit is designed specifically for theorists and researchers who may not have access to significant computational resources (GPUs) or extensive experience with LLM coding.
Online Matching Meets Sampling Without Replacement
Zhiyi Huang, Chui Shan Lee, Jianqiao Lu, Xinkai Shu
Proportionally Representative Clustering
Jeremy Vollen, Haris Aziz, Barton E. Lee, Shosuke Xiang You Morota Chu
The Fairness of Maximum Nash Social Welfare Under Matroid Constraints and Beyond
Yuanyuan Wang, Xin Chen, Qingqin Nong
Optimism in the Face of Ambiguity Principle for Multi-Armed Bandits
Mengmeng Li, Daniel Kuhn, Bahar Taskesen
How much should you pay for restaking security?
Tarun Chitra, Mallesh Pai
Splitting Guarantees for Prophet Inequalities via Nonlinear Systems
Johannes Brustle, Sebastian Perez-Salazar, Victor Verdugo
Robust Optimal Selling Mechanism under Non-linear Utility
Pin Gao, Yicheng Liu, Shixin Wang, Zhen Wang, Zizhuo Wang
Simultaneous vs Sequential: Optimal Assortment Recommendation in Multi-Store Retailing
Xiao Chen, Yan Liu, Yicheng Liu, Zizhuo Wang
An Algorithmic Theory of Simplicity in Mechanism Design
Diodato Ferraioli, Carmine Ventre
Information Acquisition in Fragmented Markets
Xian Wu, Mengjia Xia
Strategic Facility Location via Predictions
Qingyun Chen, Nick Gravin, Sungjin Im
Enabling Asymptotic Truth Learning in a Social Network
Kevin Lu, Jordan Chong, Matt Lu, Jie Gao
When to Push Ads: Optimal Mobile Ad Campaign Strategy under Markov Customer Dynamics
Guokai Li, Pin Gao, Zizhuo Wang
LP-based Control for Network Revenue Management under Markovian Demands
Haixiang Lan, Guillermo Gallego, Zizhuo Wang, Yinyu Ye
Repeated Bidding with Dynamic Value
Benjamin Heymann, Alexandre Gilotte, Rémi Chan-Renous
Truthful Budget Aggregation: Beyond Moving-Phantom Mechanisms
Mark Berg, Rupert Freeman, Ulrike Schmidt-Kraepelin, Markus Utke
Interconnected Conflict
Marcin Konrad Dziubiński, Sanjeev Goyal, Junjie Zhou
Almost Envy-free Allocation of Indivisible Goods: A Tale of Two Valuations
Alireza Kaviani, Masoud Seddighin, AmirMohammad Shahrezaei
Ex-post Individually Rational Bayesian Persuasion
Jiahao Zhang, Shuran Zheng, Renato Paes Leme, Steven Wu
Computing Competitive Equilibrium for Chores: Linear Convergence and Lightweight Iteration
He Chen, Chonghe Jiang, Anthony Man-Cho So
Tree Splitting Based Rounding Scheme for Weighted Proportional Allocations with Subsidy
Xiaowei Wu, Shengwei Zhou
Gradual Matching with Affirmative Action
Kriti Manocha, Bertan Turhan
Churning While Experimenting: Maximizing User Engagement in Recommendation Platforms
Michael L Hamilton, Raghav Singal
Stochastic Online Metric Matching: Adversarial is no Harder than Stochastic
Amin Saberi, Mingwei Yang, Sophie H. Yu
Infrequent Resolving Algorithm for Online Linear Programming
Guokai Li, Zizhuo Wang, Jingwei Zhang
Fair and Almost Truthful Mechanisms for Additive Valuations and Beyond
Biaoshuai Tao, Mingwei Yang
A Computer-aided Approach for Approximate Nash Equilibria
Xiaotie Deng, Dongchen Li, Hanyu Li
Regulating Discriminatory Pricing in the Presence of Tacit Collusion
Zongsen Yang, Xiao Lei, Pin Gao
Logarithmic Comparison-Based Query Complexity for Fair Division of Indivisible Goods
Xiaolin Bu, Zihao Li, Shengxin Liu, Jiaxin Song, Biaoshuai Tao
Grace Period is All You Need: Individual Fairness without Revenue Loss in Revenue Management
Patrick Jaillet, Chara Podimata, Zijie Zhou
When Should you Offer an Upgrade: Online Upgrading Mechanisms for Resource Allocation
Patrick Jaillet, Chara Podimata, Andrew Vakhutinsky, Zijie Zhou
A Fair Allocation is Approximately Optimal for Indivisible Chores, or Is It?
Bo Li, Ankang Sun, Shiji Xing
How to Implement Soft Reserves in India: A Market Design Approach with Historical Perspective
Bertan Turhan, Orhan Aygun
Computing Most Equitable Voting Rules
Lirong Xia
Beyond the worst case: Distortion in impartial culture electorates
Ioannis Caragiannis, Karl Fehrs
Proportional Dynamics in Linear Fisher Markets with Auto-bidding: Convergence, Incentives and Fairness
Juncheng Li, Pingzhong Tang
Data-Scarce Identification of Game Dynamics via Sum-of-Squares Optimization
Iosif Sakos, Antonios Varvitsiotis, Georgios Piliouras
Optimal Scoring Rule Design under Partial Knowledge
Yiling Chen, Fang-Yi Yu
The Impact of Generative Artificial Intelligence on Market Equilibrium: Evidence from a Natural Experiment
Kaichen Zhang, Zixuan Yuan, Hui Xiong
Best-of-Both-Worlds Fair Allocation of Indivisible and Mixed Goods
Xiaolin Bu, Zihao Li, Shengxin Liu, Xinhang Lu, Biaoshuai Tao
Generalized Principal-Agency: Contracts, Information, Games and Beyond
Jiarui Gan, Minbiao Han, Jibang Wu, Haifeng Xu
Continuous Social Networks
Julián Chitiva, Xavier Venel
Fair Interventions in Weighted Congestion Games
Miriam Fischer, Martin Gairing, Dario Paccagnan
Edge Arrival Online Matching: The Power of Free Disposal on Acyclic Graphs
Tianle Jiang, Yuhao Zhang
Fair Ordering in Replicated Systems via Streaming Social Choice
Geoffrey Ramseyer, Ashish Goel
Continuous-Time Best-Response and Related Dynamics in Tullock Contests with Convex Costs
Edith Elkind, Abheek Ghosh, Paul W. Goldberg
Mechanism Design with Delegated Bidding
Gagan Aggarwal, Marios Mertzanidis, Alexandros Psomas, Di Wang
Informational Size in School Choice
Di Feng, Yun Liu
Static Pricing for Online Selection Problem and its Variants
Bo Sun, Hossein Nekouyan Jazi, Xiaoqi Tan, Raouf Boutaba
Time-Efficient Algorithms for Nash-Bargaining-Based Matching Market Models
Ioannis Panageas, Thorben Tröbst, Vijay Vazirani
An impossibility result for strongly group-strategyproof multi-winner approval-based voting
Ioannis Caragiannis, Rob LeGrand, Evangelos Markakis, Emmanouil Pountourakis
When does additional information lead to longer travel time in multi-origin–destination networks?
Xujin Chen, Xiao-Dong Hu, Xinqi Jing, Zhongzheng Tang
Matching with Nested and Bundled Pandora Boxes
Robin Bowers, Bo Waggoner
Market Design for Capacity Sharing in Networks
Saurabh Amin, Patrick Jaillet, Haripriya Pulyassary, Manxi Wu
Auctioning with Strategically Reticent Bidders
Jibang Wu, Ashwinkumar Badanidiyuru, Haifeng Xu
Packing a Knapsack with Items Owned by Strategic Agents
Javier Cembrano, Max Klimm, Martin Knaack
Barter Exchange with Bounded Trading Cycles
Yuval Emek, Matan-El Shpiro
Optimal Guarantees for Online Selection Over Time
Sebastian Perez-Salazar, Victor Verdugo
The Complexity of Symmetric Bimatrix Games with Common Payoffs
Abheek Ghosh, Alexandros Hollender
Impartial Selection Under Combinatorial Constraints
Javier Cembrano, Max Klimm, Arturo Merino
Mechanism Design for Exchange Markets
Yusen Zheng, Yukun Cheng, Chenyang Xu, Xiaotie Deng
Pandora's Box Problem Over Time
Georgios Amanatidis, Federico Fusco, Rebecca Reiffenhäuser, Artem Tsikiridis
Price of Non-discrimination in Public Combinatorial Contracts
Yiding Feng, Mengfan Ma, Mingyu Xiao
Price Competition in Linear Fisher Markets: Stability, Equilibrium and Personalization
Juncheng Li, Pingzhong Tang
Aggregation of Antagonistic Contingent Preferences: When Is It Possible?
Xiaotie Deng, Biaoshuai Tao, Ying Wang
Convergence to Equilibrium of No-regret Dynamics in Congestion Games
Volkan Cevher, Wei Chen, Leello Dadi, Jing Dong, Ioannis Panageas, Stratis Skoulakis, Luca Viano, Baoxiang Wang, Siwei Wang
The assignment of papers to session slots (see above for numbering) can be found in the document below:
The main conference venue is the School of Informatics of the University of Edinburgh.
Please note: Edinburgh is a popular destination, especially in December. Therefore all conference attendees are strongly encouraged to make their travel arrangements as early as possible!
Participants can benefit from a discounted rate at these four hotels, which are affiliated with the University of Edinburgh, by using the code "EVENT".
Notice, however, that availability is already limited, and may depend on the specific choice of days, and type of room.
Furthermore, here is a selection of hotels that are in close proximity to the main conference venue, and therefore might be of interest to potential conference participants:
Contact:
local-support@wine2024.orgWe are happy to announce that, following the main WINE 2024 programme, a satelite workshop on game-theoretic aspects of blockchains will take place on Friday, December 6, 2024. The workshop is organized, in collaboration with IOG, by
The purpose of this one-day event is to bring together researchers in algorithmic game theory and distributed algorithms to investigate game theoretic problems in the field of blockchain protocols and distributed ledgers.
All WINE 2024 attendees are welcome to participate! (free of charge)
More details to be announced soon.