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VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback
[article]
2022
arXiv
pre-print
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We study a multi-round welfare-maximising mechanism design problem in instances where agents do not know their values. ...
On each round, a mechanism first assigns an allocation each to a set of agents and charges them a price; at the end of the round, the agents provide (stochastic) feedback to the mechanism for the allocation ...
Acknowledgements: We would like to thank Matthew Wright for providing feedback on an initial draft of this manuscript. ...
arXiv:2004.08924v4
fatcat:rcihknh3lne5rdbblglm7ony5a
Incentive Compatibility in Stochastic Dynamic Systems
[article]
2019
arXiv
pre-print
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Additionally, it is shown that there is a "Scaled" VCG mechanism that simultaneously satisfies incentive compatibility, social efficiency, budget balance as well as individual rationality under a certain ...
that are stochastic dynamic systems. ...
VCG mechanism with the above payment structure as a Scaled VCG (SVCG) mechanism, and c as the scaling factor. ...
arXiv:1901.01978v1
fatcat:77swn5ssqnd5hhmytgwp3mtddy
Optimization-friendly generic mechanisms without money
[article]
2021
arXiv
pre-print
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We further show that there is always a reweighing of the players' utility values such that running unit-demand VCG with reweighed utilities leads to a HZ-equilibrium prices. ...
The goal of this paper is to develop a generic framework for converting modern optimization algorithms into mechanisms where inputs come from self-interested agents. ...
This paper was influenced by discussions with Georgy Noarov, Sahil Singla, Matt Weinberg, Leeat Yariv, and Yufei Zheng. ...
arXiv:2106.07752v1
fatcat:cit3kuf6jzhidphr7b5iepszwa
Double Auctions with Two-sided Bandit Feedback
[article]
2023
arXiv
pre-print
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2022 pre-print arXiv:2208.06536v1 fatcat:gqwhadmtwfcczm4mluxyhbzj5e
We initiate the study of Double Auction markets under bandit feedback on both buyers' and sellers' side. ...
We show with confidence bound based bidding, and 'Average Pricing' there is an efficient price discovery among the participants. ...
We study the Double Auction markets under Average Mechanism with both seller and buyer learning from bandit feedback. ...
arXiv:2208.06536v2
fatcat:xy7si7n3vnfnbjfil2obdbrvsm
A Survey of Online Auction Mechanism Design Using Deep Learning Approaches
[article]
2021
arXiv
pre-print
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In this article, we summarized some common deep learning infrastructures adopted in auction mechanism designs and showed how these architectures are evolving. ...
With the advancement of computing technology and the bottleneck in theoretical frameworks, researchers are shifting gears towards online auction designs using deep learning approaches. ...
Like most other neural networks that deal with auction mechanism designs, it does not work under the setting of combinatorial valuations. ...
arXiv:2110.06880v1
fatcat:op5iia46xjfrznq7vfynbknkiq
Modeling Recommender Ecosystems: Research Challenges at the Intersection of Mechanism Design, Reinforcement Learning and Generative Models
[article]
2023
arXiv
pre-print
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2023 pre-print arXiv:2309.06375v1 fatcat:jkoga72r3jarroisqkfayfs4qm
make these mechanisms interpretable and actionable. ...
explicitly modeling the incentives and behaviors of all actors in the system -- and the interactions among them induced by the recommender's policy -- is strictly necessary if one is to maximize the value ...
Each agent (or player) g ∈ G has preferences over outcomes in the form of a utility function u g , which is generally unknown to the mechanism M . 10 The mechanism also embodies a social choice function ...
arXiv:2309.06375v2
fatcat:s7yyewc73jcllb5asniese34sa
Efficient Advert Assignment
[article]
2016
arXiv
pre-print
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For this pay-per-click market, we provide an efficient mechanism that maximizes social welfare. ...
Here, on each search occurrence, the platform solves an assignment problem and, on a slower time-scale, each advertiser submits a bid which matches its demand for click-throughs with supply. ...
After these preliminaries, in Section 4 we make the connection with mechanism design and strategic advertisers. ...
arXiv:1404.2750v6
fatcat:ufqpr4kbmjhntmnyqwtampvsq4
Efficient Advert Assignment
2016
Operations Research
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For this pay-per-click market, we provide an efficient mechanism that maximizes social welfare. ...
Here, on each search occurrence, the platform solves an assignment problem and, on a slower time-scale, each advertiser submits a bid which matches its demand for click-throughs with supply. ...
After these preliminaries, in Section 4 we make the connection with mechanism design and strategic advertisers. ...
doi:10.1287/opre.2016.1519
fatcat:zd7pskffvrf2xmeydxy7myb2d4
Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review
2020
Renewable & Sustainable Energy Reviews
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Mechanism design Mechanism design is a strategic variant of social choice theory. Under this theory agents are assumed to behave in a way that maximises their individual payoffs. ...
[234] use the Vickrey-Clarke-Groves (VCG) mechanism to design DR contracts, ensuring that the participating agents will reveal their true costs for participating in DR. Finally, Kota et al. ...
[115] 2014 Multi-Armed Bandit Mechanism that designs incentive offers to electricity consumers who have unknown response characteristics 153 Bakr and Cranefield [227] 2015 Shapley value (weighted ...
doi:10.1016/j.rser.2020.109899
fatcat:wgpj4awq35dfzdq7ugumtrvo7q
A Field Guide for Pacing Budget and ROS Constraints
[article]
2023
arXiv
pre-print
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2023 pre-print arXiv:2302.08530v1 fatcat:w4hrhkyhrzbhjhfzqhf3j5o7se
A popular autobidding strategy is value maximization subject to return-on-spend (ROS) constraints. ...
The purpose of this work is to theoretically and empirically compare algorithms with different degrees of coordination between these two pacing systems. ...
Much like the design of optimal auctions for utility-maximizing bidders [Myerson, 1981] , a recent line of work has focused on the design of revenue optimal mechanisms for value maximizers. ...
arXiv:2302.08530v2
fatcat:sf2yfj743zhqji2izauuxfqrvi
D3C: Reducing the Price of Anarchy in Multi-Agent Learning
[article]
2022
arXiv
pre-print
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2022 pre-print arXiv:2010.00575v4 fatcat:cdxlo4xrqnh5bhrmq3h3hlciguOther Versions
Equipped with this estimator, agents can adjust their incentives in a way that improves the efficiency incurred at a Nash equilibrium. ...
Agents do so by learning to mix their reward (equiv. negative loss) with that of other agents by following the gradient of our derived upper bound. We refer to this approach as D3C. ...
Finally, we develop a gradient estimator to minimize the agent objectives in settings with bandit feedback (e.g., RL) that enables scalable decentralization. ...
arXiv:2010.00575v5
fatcat:k5rjaw7xlzbf5oyzaap4sdfgzm
Decisions, Learning and Games: You've Got To Have Freedom
[article]
2022
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best ex-post action and commits to a randomized strategy with full support. ...
To study the effect of past experiences, we extend the standard bandit setting: after the algorithm chooses an action, the subject may actually carry out a different action. ...
Access to the value function and the reported signals allows the direct VCG mechanism to select the expert i with the highest valuation. ...
doi:10.25911/yrw3-hm53
fatcat:5s2wwmky45d6hmcnvssa5234vu
Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets
[article]
2022
arXiv
pre-print
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We formalize the problem by proposing a reinforcement learning framework that integrates optimistic value iteration with maximum weight matching. ...
At each step, the agents are presented with a dynamical context, where the contexts determine the utilities. ...
The data streams that arise from digital markets provide opportunities to cope with such challenges, via learning-based mechanism design. ...
arXiv:2203.03684v1
fatcat:4knoo3fasja4pdh2wly5kxzfbi
Multi-objective decision-theoretic planning
2016
AI Matters
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for each objective; or 2) policies can be stochastic. ...
In such cases the problem is more naturally expressed using a vector-valued reward function. ...
Therefore, it cannot be used in practice on the MPP to make the dynamic-VCG mechanism work. ...
doi:10.1145/3008665.3008670
fatcat:mpxluczzvje77h777quyj7tbdm
2019 Index IEEE Transactions on Communications Vol. 67
2019
IEEE Transactions on Communications
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Li, M., +, TCOMM Nov. 2019 7558-7572 Truthful Mechanism Design for Wireless Powered Network With Channel Gain Reporting. ...
Cheng, Q., +, TCOMM
Nov. 2019 7785-7798
Feedback
Fundamental Limits of Communication Over State-Dependent Channels
With Feedback. ...
doi:10.1109/tcomm.2019.2963622
fatcat:qd6so3reavde3eiukfknmpbfsu
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