On fair price discrimination in multi-unit markets

Flammini, Michele; Mauro, Manuel; Tonelli, Matteo

doi:10.1016/j.artint.2020.103388

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…Following the conference version of our paper [44], there have been followup works in multi-unit markets and envy-free pricing; we mention the most closely related ones. Flammini, Mauro, and Tonelli [45] consider a framework for capturing the setting of fair discriminatory pricing in multi-unit markets where the agents are related via an underlying graph and each agent is only aware of the prices of the neighboring agents. In the extreme case where the graph is complete, each agent must pay the same price per unit, while in the case where the graph has no edges, each agent can be charged a different price per unit.…”

Section: Related Workmentioning

confidence: 99%

“…In the extreme case where the graph is complete, each agent must pay the same price per unit, while in the case where the graph has no edges, each agent can be charged a different price per unit. Flammini, Mauro, Tonelli, and Vinci [46] considered the envyfree pricing via an underlying graph and obtained bounds on the revenue for topologies inspired by social networks, such as where the nodes have a power law degree distribution.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Walrasian pricing in multi-unit auctions

Brânzei¹,

Filos-Ratsikas

Miltersen

et al. 2023

Artificial Intelligence

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Walrasian pricing in multi-unit auctions

Brânzei¹,

Filos-Ratsikas

Miltersen

et al. 2023

Artificial Intelligence

View full text Add to dashboard Cite

“…The authors modeled the PAL user's features as the multivariable unordered time series to predict the spectrum information. The authors in [50] proposed to detect the spectrum resources using MLbased clustering techniques. The authors also investigated the use of Q-learning, deep learning, kernel-based learning, and transfer-based learning.…”

Section: Related Workmentioning

confidence: 99%

Channel Allocation to GAA Users Using Double Deep Recurrent Q-Learning Based on Double Auction Method

Abbass,

Hussain,

Abbas

et al. 2023

IEEE Access

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The SAS-CBRS framework is being tested to share the federally held spectrum with licensed users and opportunistic users to maximize the underutilized spectrum's utility and overcome spectrum scarcity. In the SAS-CBRS framework, radio resources are assigned to the incumbent access (IA), primary access licensees (PAL), and general authorized access (GAA) users according to the given priority. The SAS-CBRS three-tier framework is different from the conventional cognitive radio networks (CRN) as it involves a central entity that acts as a server called a spectrum access system (SAS). The methods to assign the resources using the SAS are still in the research phase. Yet, no standard method is defined by the FCC for resource allocation. The current CRN methods cannot be directly applied because of the addition of the third tier and a central server. Moreover, strict rules are defined for using the 3.5 GHz spectrum band for communication. In this paper, a novel DDRQ-SAS algorithm integrated with the double auction (DA) algorithm is proposed that uses deep recurrent double Q-learning. The DDRQ-SAS is used by the SAS to hold a spectrum auction and create a spectrum pool to get information on PAL channels. PAL operators use the DA algorithm to generate the asking prices intelligently for their available idle channels and the GAA users will use the DA algorithm to intelligently bid for their preferred channels. The DDRQ-SAS-DA algorithm allows the GAA users to get the guaranteed QoS offered by the PAL operators in an auction. GAA users maintain the preference list of the PAL reserved idle channels and bid intelligently based on the available QoS. SAS completes the transaction by allocating the channels to the winning GAAs. The defined problem is also modeled using the double auction multi-winner multi-channel technique and the TDSA-PS algorithm. Numerical results show that the proposed DDRQ-SAS-DA algorithm provides up to 20% better QoS at higher loads for GAA users, generates 24% more revenue for PAL operators, and is 1.6 times more efficient in assigning 500 GAA users.

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