Related papers: Flexible Bidding in Service-Oriented Combinatorial…
Online auction scenarios, such as bidding searches on advertising platforms, often require bidders to participate repeatedly in auctions for identical or similar items. Most previous studies have only considered the process by which the…
Efficient and reliable resource allocation within densely-deployed massive IoT networks remains a key challenge due to resource constraints among low-size, weight, and power (SWaP) IoT devices and within the network and limitations of…
In online combinatorial allocations/auctions, n bidders sequentially arrive, each with a combinatorial valuation (such as submodular/XOS) over subsets of m indivisible items. The aim is to immediately allocate a subset of the remaining…
Situations where a group of agents come together to jointly buy a resource that they individually cannot afford to buy are commonly observed in markets. For example in the US market for radio spectrum, a recent proposal invited small firms…
We study a seller who sells a single good to multiple bidders with uncertainty over the joint distribution of bidders' valuations, as well as bidders' higher-order beliefs about their opponents. The seller only knows the (possibly…
We show that every universally truthful randomized mechanism for combinatorial auctions with submodular valuations that provides $m^{\frac 1 2 -\epsilon}$ approximation to the social welfare and uses value queries only must use…
This paper studies how to maximize a spectrum database operator's expected revenue in sharing spectrum to secondary users, through joint pricing and admission control of spectrum resources. A unique feature of our model is the consideration…
I construct a novel random double auction as a robust bilateral trading mechanism for a profit-maximizing intermediary who facilitates trade between a buyer and a seller. It works as follows. The intermediary publicly commits to charging a…
Preference elicitation is a major challenge in large combinatorial auctions because the bundle space grows exponentially in the number of items. Recent work has used machine learning (ML) algorithms to identify a small set of bundles to…
With the rapidly increased penetration of renewable generations, incentive-based demand side management (DSM) shows great value on alleviating the uncertainty and providing flexibility for microgrid. However, how to price those demand…
In this paper, we model the various wireless users in a cognitive radio network as a collection of selfish, autonomous agents that strategically interact in order to acquire the dynamically available spectrum opportunities. Our main focus…
To address the demand of exponentially increasing end users efficient use of limited spectrum is a necessity. For this, spectrum allocation among co-existing operators in licensed and unlicensed spectrum band is required to cater to the…
The rising share of volatile renewable generation increases the demand for flexibility in the electricity grid. Flexible capacity can be offered by industrial energy systems through participation on either the continuous intraday,…
Promising federated learning coupled with Mobile Edge Computing (MEC) is considered as one of the most promising solutions to the AI-driven service provision. Plenty of studies focus on federated learning from the performance and security…
This paper develops algorithms to solve strong-substitutes product-mix auctions. That is, it finds competitive equilibrium prices and quantities for agents who use this auction's bidding language to truthfully express their…
We study risk-free bidding strategies in combinatorial auctions with incomplete information. Specifically, what is the maximum profit that a complement-free (subadditive) bidder can guarantee in a multi-item combinatorial auction? Suppose…
We study buyer-optimal procurement mechanisms when quality is contractible. When some costs are borne by every participant of a procurement auction regardless of winning, the classic analysis should be amended. We show that an optimal…
Contract theory studies how a principal can incentivize agents to exert costly, unobservable effort through performance-based payments. While classical economic models provide elegant characterizations of optimal solutions, modern…
To address the exponentially increasing data rate demands of end users, necessitates efficient spectrum allocation among co-existing operators in licensed and unlicensed spectrum bands to cater to the temporal and spatial variations of…
We develop a novel optimization model to maximize the profit of a Demand-Side Platform (DSP) while ensuring that the budget utilization preferences of the DSP's advertiser clients are adequately met. Our model is highly flexible and can be…