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CAREER: Optimal Mechanism Design without Monetary Transfers

CAREER: Optimal Mechanism Design without Monetary Transfers
职业:无需货币转移的最优机制设计
批准号:
2047907
负责人:
Vasilis Gkatzelis
金额:
$59.98万
依托单位:
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2021
资助国家:
美国
项目状态:
未结题
起止时间:
2021-02-15 至 2026-01-31

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中文摘要
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英文摘要
One of the goals that lie at the core of computer science, as well as operations research and economics, is the effective utilization of scarce resources. For example, operating systems are designed to effectively utilize a computer's memory and processing units, and network protocols are designed to effectively utilize a networks' bandwidth. More broadly, a large fraction of the long literature on algorithm design and optimization is motivated by resource-allocation problems that arise in a wide variety of domains, ranging from project management and business administration to government policy and market design. Achieving effective resource-allocation outcomes is particularly challenging in multiagent systems, where a set of self-interested agents compete for shared resources. For example, in large computer networks there are multiple users that compete for the network's shared computational resources, such as its bandwidth, or access to its servers. Each agent's goal is to maximize its own utility, and the goal of the designer is to achieve system-level efficiency despite the agents' competing preferences. Without carefully designed resource-allocation mechanisms, the available resources would be underutilized, and massive amounts of social utility would be wasted; thus, it is imperative that these mechanisms are designed to the highest standard. The focus of this project is on the design of multiagent resource-allocation mechanisms that take into consideration the preferences of the participating agents and seek to maximize fairness and efficiency in the resulting outcomes. To address the competing incentives of the participating agents, the field of mechanism design in economics has provided very useful tools. By far the most effective among them is the use of monetary payments: charging for the use of the resources can ensure that only the agents who need them the most would be interested in paying the price. However, the use of monetary payments is often undesired or even infeasible, e.g., due ethical, legal, or practical considerations, so the mechanism needs to eschew monetary transfers. However, the vast majority of the literature on mechanism design has focused on the use of monetary payments, so money-free mechanisms are not well-understood. This project considers canonical domains of mechanism design without money from the perspective of the designer, with the goal of developing a coherent theory regarding what can and cannot be achieved in the absence of money. As a substitute for monetary payments, money-free mechanisms can instead penalize the agents by intentionally keeping some of the resources unallocated (a tool known as "money-burning"). This way, the improved incentives come at a cost in social utility, introducing novel trade-offs for the designer who needs to strike a balance between incentives and effectiveness. The main questions that this project focuses on are: 1) What incentives can the mechanism provide to the participants in the absence of money, and what cost in social utility do these improved incentives require? 2) From an algorithmic perspective what are the best worst-case approximation guarantees that can be achieved given the computational and informational constraints that these mechanisms may face?This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
期刊论文(12)
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会议论文
DOI: 10.5555/3535850.3535875
发表时间: 2022-01
期刊: ArXiv
影响因子: --
作者: [I. Caragiannis;Vasilis Gkatzelis;Alexandros Psomas;Daniel Schoepflin]
通讯作者: I. Caragiannis;Vasilis Gkatzelis;Alexandros Psomas;Daniel Schoepflin
Learning-Augmented Mechanism Design: Leveraging Predictions for Facility Location
学习增强机制设计:利用设施位置预测
DOI: 10.1145/3490486.3538306
发表时间: 2022
期刊: Proceedings of the 23rd ACM Conference on Economics and Computation
影响因子: --
作者: [Agrawal, Priyank, Balkanski, Eric, Gkatzelis, Vasilis, Ou, Tingting, Tan, Xizhi]
通讯作者: Tan, Xizhi
Optimal Data Acquisition with Privacy-Aware Agents
通过具有隐私意识的代理实现最佳数据采集
DOI: 10.1109/satml54575.2023.00023
发表时间: 2023
期刊: 2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML
影响因子: --
作者: [Cummings, Rachel, Elzayn, Hadi, Pountourakis, Emmanouil, Gkatzelis, Vasilis, Ziani, Juba]
通讯作者: Ziani, Juba
PROPm Allocations of Indivisible Goods to Multiple Agents
PROPm 将不可分割的货物分配给多个代理
DOI: --
发表时间: 2021
期刊: 30th International Joint Conference on Artificial Intelligence (IJCAI 2021
影响因子: --
作者: [Baklanov, Artem, Garimidi, Pranav, Gkatzelis, Vasilis, Schoepflin, Daniel]
通讯作者: Schoepflin, Daniel
11
    Collaborative Research: AF: Small: Mechanisms with Predictions
    • 批准号:
      2210502
    • 项目类别:
      Standard Grant
    • 资助金额:
      $30.0万
    • 财政年份:
      2022
    • 负责人:
      Vasilis Gkatzelis
    • 依托单位:
    AF:Small: The Efficiency of Clock Auctions
    • 批准号:
      2008280
    • 项目类别:
      Standard Grant
    • 资助金额:
      $35.8万
    • 财政年份:
      2020
    • 负责人:
      Vasilis Gkatzelis
    • 依托单位:
    CRII: AF: Practical Auction Design Using the Deferred-Acceptance Framework
    • 批准号:
      1755955
    • 项目类别:
      Standard Grant
    • 资助金额:
      $17.49万
    • 财政年份:
      2018
    • 负责人:
      Vasilis Gkatzelis
    • 依托单位:
    海外基金