Myerson Auction Research Articles

Neural Myerson Auction for Truthful and Distributed Mobile Charging in UAV-Assisted Digital-Twin Networks

Neural Myerson Auction for Truthful and Distributed Mobile Charging in UAV-Assisted Digital-Twin Networks

Two tales of platoon intelligence for autonomous mobility control: Enabling deep learning recipes

AbstractThis paper surveys recent multiagent reinforcement learning and neural Myerson auction deep learning efforts to improve mobility control and resource management in autonomous ground and aerial vehicles. The multiagent reinforcement learning communication network (CommNet) was introduced to enable multiple agents to perform actions in a distributed manner to achieve shared goals by training all agents' states and actions in a single neural network. Additionally, the Myerson auction method guarantees trustworthiness among multiple agents to optimize rewards in highly dynamic systems. Our findings suggest that the integration of MARL CommNet and Myerson techniques is very much needed for improved efficiency and trustworthiness.

A Resource Allocation Scheme with the Best Revenue in the Computing Power Network

The emergence of computing power networks has improved the flexibility of resource scheduling. Considering the current trading scenario of computing power and network resources, most resources are no longer subject to change after being allocated to users until the end of the lease. However, this practice often leads to idle resources during resource usage. To optimize resource allocation, a trading mechanism is needed to encourage users to sell their idle resources. The Myerson auction mechanism precisely aims to maximize the seller’s benefits. Therefore, we propose a resource allocation scheme based on the Myerson auction. In the scenario of the same user bidding distribution, we first combine the Myerson auction with Hyperledger Fabric by introducing a reserved price, which creates conditions for the application of blockchain in auction scenarios. Regarding different user bidding distributions, we propose a Myerson auction network model based on clustering algorithms, which makes the auction adaptable to more complex scenarios. The experimental findings show that the revenue generated by the auction model in both scenarios is significantly higher than that of the traditional sealed bid second-price auction, and can approach the expected revenue in the real Myerson auction scenario.

Neural Myerson auction for truthful and energy-efficient autonomous aerial data delivery

A successful deployment of drones provides an ideal solution for surveillance systems. Using drones for surveillance can provide access to areas that may be difficult or impossible to reach by humans or in-land vehicles gathering images or video recordings of a specific target in their coverage. Therefore, we introduces a data delivery drone to transfer collected surveillance data in harsh communication conditions. This paper proposes a Myerson auction-based asynchronous data delivery in an aerial distributed data platform in surveillance systems taking battery limitation and long flight constraints into account. In this paper, multiple delivery drones compete to offer data transfer to a single fixed-location surveillance drone. Our proposed Myerson auction based algorithm, which uses the truthful second-price auction (SPA) as a baseline, is to maximize the seller's revenue while meeting several desirable properties, i.e., individual rationality and incentive compatibility while pursuing truthful operations. On top of this SPA-based operations, a deep learning based framework is additionally designed for delivery performance improvements.

Auctions: A New Method for Selling Objects with Bimodal Density Functions

In this paper we define a new auction, called the Draw auction. It is based on the implementation of a draw when a minimum price of sale is not reached. We find that a Bayesian Nash equilibrium is reached in the Draw auction when each player bids his true personal valuation of the object. Furthermore, we show that the expected profit for the seller in the Draw auction is greater than in second-price auctions, with or without minimum price of sale. We make this affirmation for objects whose valuation can be modeled as a bimodal density function in which the first mode is much greater than the second one. Regarding the Myerson auction, we show that the expected profit for the seller in the Draw auction is nearly as good as the expected profit in the optimal auction, with the difference that our method is much more simple to implement than Myerson’s one. All these results are shown by computational tests, for whose development we have defined an algorithm to calculate Myerson auction.

On Bayesian Epistemology of Myerson Auction

Bayesian Epistemology bases its analysis of the objects under study on a prior, a (jointly known) probability distribution, which is in turn the subject matter in statistical learning, and that of machine learning at least implicitly. Our interests are in data related to bidder behavior in a game theoretical setting where the learner and the owners of data to be learned are affected by each other’s strategies of data reporting and data utilization. More specifically, the learner takes actions according to the probability governing the data distributions. The owners of data may reveal false probability distributions of their private data for their better utilities. We focus on the Myerson auction for such an issue of learning and optimization of the seller who wants to gain information on bidders’ value distributions to achieve the maximum revenue. We study bidders’ best responses against each other. We make a special effort to understand the possibility of truthful reporting of agent value distributions. For cases this is not possible, we study the dynamic properties of bidder value distribution reporting and the convergence toward a local Nash equilibria.

Truthful electric vehicle charging via neural-architectural Myerson auction

The electric vehicle (EV) market increases due to the benefits of reducing greenhouse gas emissions using renewable energy resources. In this context, the charging scheme of electric vehicles in charging stations (CSs) is also important. Electronic devices’ charging between EV and multiple CS should consider EV’s short battery capacity, long charging time, residual energy in each CS, and time of use (ToU) for charging. In this paper, multiple CSs compete to offer electricity charging to a single EV. Based on this need, this paper proposes a deep learning-based auction which increases the charging amounts using Myerson auction while preserving truthfulness.

Oracle-efficient Online Learning and Auction Design

We consider the design of computationally efficient online learning algorithms in an adversarial setting in which the learner has access to an offline optimization oracle. We present an algorithm called Generalized Follow-the-Perturbed-Leader and provide conditions under which it is oracle-efficient while achieving vanishing regret. Our results make significant progress on an open problem raised by Hazan and Koren [31], who showed that oracle-efficient algorithms do not exist in general [30] and asked whether one can identify properties under which oracle-efficient online learning may be possible. Our auction-design framework considers an auctioneer learning an optimal auction for a sequence of adversarially selected valuations with the goal of achieving revenue that is almost as good as the optimal auction in hindsight, among a class of auctions. We give oracle-efficient learning results for: (1) VCG auctions with bidder-specific reserves in single-parameter settings, (2) envy-free item pricing in multi-item auctions, and (3) s-level auctions of Morgenstern and Roughgarden [43] for single-item settings. The last result leads to an approximation of the overall optimal Myerson auction when bidders’ valuations are drawn according to a fast-mixing Markov process, extending prior work that only gave such guarantees for the i.i.d. setting. Finally, we derive various extensions, including: (1) oracle-efficient algorithms for the contextual learning setting in which the learner has access to side information (such as bidder demographics), (2) learning with approximate oracles such as those based on Maximal-in-Range algorithms, and (3) no-regret bidding in simultaneous auctions, resolving an open problem of Daskalakis and Syrgkanis [14].

Robust Auctions for Revenue via Enhanced Competition

In “Robust Auctions for Revenue via Enhanced Competition,” T. Roughgarden, I. Talgam-Cohen, and Q. Yan revisit the classic Bulow–Klemperer result. This result compares the revenues of two well-known auction formats: the welfare-maximizing Vickrey auction and the revenue-maximizing Myerson auction. It shows that, with an extra bidder competing for the item, the Vickrey auction becomes as good as the Myerson auction in terms of revenue while maintaining independence from prior distributional information about bidders’ valuations. Unfortunately, Myerson’s toolbox for revenue-optimal auction design does not extend to combinatorial auctions with multiple heterogenous items, for which optimizing revenue remains a challenge—especially if we want auction designs that are simple and robust enough to use in practice. This paper extends the Bulow–Klemperer result to multiple heterogenous items by showing that a prior-independent, simple, welfare-maximizing auction with additional competing bidders achieves as much revenue as the ill-understood optimal auction.

不对称信息下的高校内部资源分配与激励模型研究 — 以合肥工业大学研究生分配与助研费分析为例 (Research on the Model of Internal Resource Allocation and Incentives in Universities under Asymmetric Information — Taking Postgraduate Distribution and Research Assistant Fee System of Hefei University of Technology as an Example)

Chinese abstract: 当前，我国高校内部的资源管控和绩效评价与激励存在困难。由于教师活动的信息不对称和其从事教学和科研等多任务的特征，基于客观产出指标的评价和激励等机制容易导致“唯论文、唯职称、唯学历、唯奖项”等问题。在此背景下，本文结合了多任务多代理人的委托代理模型与Myerson拍卖模型，构建了不对称信息情况下的集高校内部资源分配、定价、教师绩效评价与激励设计为一体的决策支持理论模型。通过对模型理论特征的分析，本文获得了最优激励系数、最优固定工资、最优资源分配量、教师最优努力程度与效率类型等多个变量之间的理论关系。最后，在以合肥工业大学研究生分配与助研费设置为背景的案例分析中，本文分析现行方案与最优方案的差异以及提出助研费的设置应综合考虑和相应调整高校产出目标、激励程度等多个因素。本文构建的模型及分析结论可为高校完善管理会计与控制系统提供理论指导，有助于提高高校内部资源配置和办学效率，解决由研究生助研费制度实行引起的争论。 English abstract: At present, there are difficulties in resource management and performance evaluation and incentive in universities in China. Due to the asymmetry information of teachers' activities and the characteristic of their multitasking in teaching and research, the evaluation and incentive mechanism based on objective output indicators easily leads to the problem of thesis only, title only, academic qualifications only, awards only . In this context, this paper combines the multi-task and multi-agent principal-agent model with the Myerson auction to construct a decision support model which integrates the internal resource allocation and pricing, teacher performance evaluation and incentive design of universities under asymmetry information. Through the analysis of the theoretical characteristics of the model, this paper obtains the theoretical relationship among multiple variables such as the optimal incentive coefficient, the optimal fixed salary, the optimal resource allocation amount, the teacher's optimal effort and the type of efficiency. Finally, in the case study of postgraduate distribution and research assistant fee system in Hefei University of Technology, this paper analyzes the differences between the current scheme and the optimal scheme, and proposes that when setting the research assistant fee, multiple factors such as the output goal and incentive level of universities should be comprehensively considered and adjusted accordingly. The model and conclusions in this paper can provide theoretical guidance for improving the management accounting and control system in universities, help to improve the internal resource allocation and school running efficiency, and resolve disputes caused by the implementation of the postgraduate research assistant fee system.

Tight revenue gaps among simple and optimal mechanisms

Consider a fundamental problem in microeconomics: selling a single item to a number of potential buyers, who independently draw their values from regular and publicly known distributions. There are four mechanisms widely studied in the literature and widely used in practice: Myerson Auction (OPT), Sequential Posted Pricing (SPM), Second-Price Auction with Anonymous Reserve (AR) , and Anonymous Pricing (AP). OPT is revenue-optimal but complicated, which also experiences several practical issues such as fairness. AP is the simplest mechanism, but also generates the lowest revenue among these four mechanisms. SPM and AR are of intermediate complexity and revenue. A quantitative approach to comparing the relative power of these mechanisms is to study their revenue gaps, each of which is defined as the largest ratio between the revenues from a pair of mechanisms. This letter surveys some recent developments on establishing tight revenue gaps, and highlights some open questions.

Tight Revenue Gaps Among Simple Mechanisms

We consider a fundamental problem in microeconomics: selling a single item to a number of potential buyers, whose values are drawn from known independent and regular (not necessarily identical) distributions. There are four widely used and widely studied mechanisms in the literature: Myerson Auction (OPT), Sequential Posted-Pricing (SPM), Second-Price Auction with Anonymous Reserve (AR), and Anonymous Pricing (AP). OPT is revenue-optimal but complicated and also experiences several issues in practice such as fairness; AP is the simplest mechanism but also generates the lowest revenue among these four mechanisms; SPM and AR are of intermediate complexity and revenue. We explore revenue gaps among these mechanisms, each of which is defined as the largest ratio between revenues from a pair of mechanisms. We establish two tight bounds and one tighter bound: 1. SPM vs. AP: this ratio studies the power of discrimination in pricing schemes. We obtain the tight ratio of constant ${\cal{C}}^* \approx {2.62}$, closing the gap between $[\frac{e}{e - 1}, e]$ left before. 2. AR vs. AP: this ratio measures the relative power of auction scheme vs. pricing scheme, when no discrimination is allowed. We attain the tight ratio of $\frac{\pi^2}{6} \approx 1.64$, closing the previously known bounds $[\frac{e}{e - 1}, e]$. 3. OPT vs. AR: this ratio quantifies the power of discrimination in auction schemes and is previously known to be somewhere between [2, e]. The lower bound of 2 was conjectured to be tight by Hartline and Roughgarden [Proceedings of the 10th ACM Conference on Electronic Commerce, 2009, pp. 225--234] and Alaei et al. [Games Econom. Behav., 118 (2019), pp. 494--510]. We acquire a better lower bound of 2.15 and thus disprove this conjecture.

Boosted Second-price Auctions for Heterogeneous Bidders

The second price auction has been the prevalent auction format used by advertising exchanges because of its simplicity and desirable incentive properties. However, even with an optimized choice of reserve prices, this auction is not revenue optimal when the bidders are heterogeneous and their valuation distributions differ significantly. In order to optimize the revenue of advertising exchanges, we propose an auction format called the boosted second price auction, which assigns a boost value to each bidder. The auction favors bidders with higher boost values and allocates the item to the bidder with the highest boosted bid. We propose a data-driven approach to optimize boost values using the previous bids of the bidders. Our analysis of auction data from Google’s online advertising exchange shows that the boosted second price auction with data-optimized boost values outperforms the second price auction and empirical Myerson auction by up to 6% and 3%, respectively.

Optimal Design for Multi-Item Auctions: A Robust Optimization Approach

In this paper, we revisit the auction design problem for multi-item auctions with budget constrained buyers by introducing a robust optimization approach to model (a) concepts such as incentive compatibility and individual rationality that are naturally expressed in the language of robust optimization and (b) the auctioneer’s beliefs on the buyers' valuations of the items. Rather than using probability distributions (the classical probabilistic approach) or an adversarial model to model valuations, we introduce an uncertainty set based model for these valuations. We construct these uncertainty sets to incorporate historical information available to the auctioneer in a way that is consistent with limit theorems of probability theory or knowledge of the probability distribution. In this setting, we formulate the auction design problem as a robust optimization problem and provide a characterization of the optimal solution as an auction with reservation prices, thus extending the work of Myerson [Myerson RB (1981) Optimal auction design. Math. Oper. Res. 6(1):58–73] from single item without budget constraints to multiple items with budgets, potentially correlated valuations, and uncertain budgets. Unlike the Myerson auction where the reservation prices do not depend on the item, the reservation prices in our approach are a function of both the bidder and the item. We propose an algorithm for calculating the reservation prices by solving a bilinear optimization problem that, although theoretically difficult in general, is numerically tractable for the polyhedral uncertainty sets we consider. Moreover, this bilinear optimization problem reduces to a linear optimization problem for auctions without budget constraints and the auction becomes the classical second price auction. We report computational evidence that suggests the proposed approach (a) is numerically tractable for large scale auction design problems with the polyhedral uncertainty sets we consider, (b) leads to improved revenue compared to the classical probabilistic approach when the true distributions are different from the assumed ones, and (c) leads to higher revenue when correlations in the buyers' valuations are explicitly modeled.

How to approximate optimal auctions

Bayesian auction design investigates how to sell scarce resources to agents with private values drawn according to known distributions. A natural objective in this setting is revenue maximization. The seminal work of Roger Myerson presents a recipe for designing revenue-maximizing auctions in single-parameter settings. Myerson defines a function that maps each agent's value to a new number called the virtual value . The optimal auction then chooses the solution with maximum virtual surplus subject to the feasibility constraints. Often this underlying optimization problem is NP-hard and is complicated by the fact that virtual values may be negative. Prior work approximates the optimal auction by approximating this optimization problem. Such a solution can be interpreted as approximating the optimal auction point-wise, i.e., for each realization of values the revenue of the derived auction is close to that of the Myerson auction. In this letter, we suggest a different approach: Approximate the maximum virtual surplus on average with respect to the induced virtual value distributions . Our approach has the advantage that sometimes the point-wise optimization problem is not approximable to within any reasonable factor. However, by leveraging the fact that any distribution of virtual values has non-negative expectation, it is sometimes possible to get good average-case approximations. Furthermore, the optimal auction is itself an average-case guarantee: it maximizes the revenue on average with respect to the distributions, but it may lose substantial revenue on certain value profiles. Thus our average-case guarantee is without loss of generality. We showcase our approach using the problem of selling goods with positive network externalities. For this problem, the underlying optimization problem is inapproximable, yet with our approach we are able to prove that a natural greedy auction is a constant-factor approximation.