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1. Coasian Equilibria in Sequential Auctions

   https://doi.org/10.2139/ssrn.4632787  

   Liu, Qingmin; Mierendorff, Konrad; Shi, Xianwen (November 2023, SSRN Electronic Journal)

   We study stationary equilibria in a sequential auction setting. A seller runs a sequence of standard first-price or second-price auctions to sell an indivisible object to potential buyers. The seller can commit to the rule of the auction and the reserve price of the current period but not to reserve prices of future periods. We prove the existence of stationary equilibria and establish a uniform Coase conjecture—at any point in time and in any stationary equilibrium, the seller’s profit from running sequential auctions converges to the profit of running an efficient auction as the period length goes to zero. 

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2. Risk-averse and strategic prosumers: A distributionally robust chance-constrained MPEC approach

   https://doi.org/10.1007/s12667-022-00561-0  

   Ramyar, Sepehr; Tanaka, Makoto; Chen, Yihsu (December 2022, Energy Systems)

   Under the ruling of FERC Order 2222, prosumers who own distributed energy resources are expected to play an essential role that can significantly affect the market outcomes. This paper assesses the market power potential of a risk-averse prosumer by designating it as a Stackelberg leader in the market. We formulate the problem as a mathematical program with equilibrium constraints with a distributionally robust chance-constrained framework to account for the renewable generation uncertainty. The numerical results demonstrate that the prosumer’s strategy depends on the magnitude of renewable generation uncertainty and the degree of risk aversion, which jointly affect the perceived quantity of its renewable resources. The risk-averse Stackelberg case always yields a higher payoff for the prosumer compared to the Cournot and perfect competition cases. Moreover, it is more potent for the prosumer to exercise buyer’s market power rather than seller’s market power. The situation worsens when the prosumer is less risk averse. This highlights the importance of understanding the role of the prosumer acting either as a consumer or as a producer and the risk faced by the prosumer when evaluating its interaction with the wholesale market. 

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3. Memtrade: Marketplace for Disaggregated Memory Clouds

   https://doi.org/10.1145/3589985  

   Maruf, Hasan Al; Zhong, Yuhong; Wang, Hongyi; Chowdhury, Mosharaf; Cidon, Asaf; Waldspurger, Carl (May 2023, Proceedings of the ACM on Measurement and Analysis of Computing Systems)

   We present Memtrade, the first practical marketplace for disaggregated memory clouds. Clouds introduce a set of unique challenges for resource disaggregation across different tenants, including resource harvesting, isolation, and matching. Memtrade allows producer virtual machines (VMs) to lease both their unallocated memory and allocated-but-idle application memory to remote consumer VMs for a limited period of time. Memtrade does not require any modifications to host-level system software or support from the cloud provider. It harvests producer memory using an application-aware control loop to form a distributed transient remote memory pool with minimal performance impact; it employs a broker to match producers with consumers while satisfying performance constraints; and it exposes the matched memory to consumers through different abstractions. As a proof of concept, we propose two such memory access interfaces for Memtrade consumers -- a transient KV cache for specified applications and a swap interface that is application-transparent. Our evaluation using real-world cluster traces shows that Memtrade provides significant performance benefit for consumers (improving average read latency up to 2.8X) while preserving confidentiality and integrity, with little impact on producer applications (degrading performance by less than 2.1%). 

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4. Online Learning in Stackelberg Games with an Omniscient Follower

   Zhao, Geng; Zhu, Banghua; Jiao, Jiantao; Jordan, Michael (August 2023, Proceedings of Machine Learning Research)

   We study the problem of online learning in a two-player decentralized cooperative Stackelberg game. In each round, the leader first takes an action, followed by the follower who takes their action after observing the leader’s move. The goal of the leader is to learn to minimize the cumulative regret based on the history of interactions. Differing from the traditional formulation of repeated Stackelberg games, we assume the follower is omniscient, with full knowledge of the true reward, and that they always best-respond to the leader’s actions. We analyze the sample complexity of regret minimization in this repeated Stackelberg game. We show that depending on the reward structure, the existence of the omniscient follower may change the sample complexity drastically, from constant to exponential, even for linear cooperative Stackelberg games. 

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5. Selling Data To a Machine Learner: Pricing via Costly Signaling

   Chen, Junjie; Li, Minming; Xu, Haifeng (August 2022, Proceedings of the 39th International Conference on Machine Learning (ICML 2022))

   We consider a new problem of selling data to a machine learner who looks to purchase data to train his machine learning model. A key challenge in this setup is that neither the seller nor the machine learner knows the true quality of data. When designing a revenue-maximizing mechanism, a data seller faces the tradeoff between the cost and precision of data quality estimation. To address this challenge, we study a natural class of mechanisms that price data via costly signaling. Motivated by the assumption of i.i.d. data points as in classic machine learning models, we first consider selling homogeneous data and derive an optimal selling mechanism. We then turn to the sale of heterogeneous data, motivated by the sale of multiple data sets, and show that 1) on the negative side, it is NP-hard to approximate the optimal mechanism within a constant ratio e/(e+1) + o(1); while 2) on the positive side, there is a 1/k-approximate algorithm, where k is the number of the machine learner’s private types. 

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