231 research outputs found
A Neural Architecture for Designing Truthful and Efficient Auctions
Auctions are protocols to allocate goods to buyers who have preferences over
them, and collect payments in return. Economists have invested significant
effort in designing auction rules that result in allocations of the goods that
are desirable for the group as a whole. However, for settings where
participants' valuations of the items on sale are their private information,
the rules of the auction must deter buyers from misreporting their preferences,
so as to maximize their own utility, since misreported preferences hinder the
ability for the auctioneer to allocate goods to those who want them most.
Manual auction design has yielded excellent mechanisms for specific settings,
but requires significant effort when tackling new domains. We propose a deep
learning based approach to automatically design auctions in a wide variety of
domains, shifting the design work from human to machine. We assume that
participants' valuations for the items for sale are independently sampled from
an unknown but fixed distribution. Our system receives a data-set consisting of
such valuation samples, and outputs an auction rule encoding the desired
incentive structure. We focus on producing truthful and efficient auctions that
minimize the economic burden on participants. We evaluate the auctions designed
by our framework on well-studied domains, such as multi-unit and combinatorial
auctions, showing that they outperform known auction designs in terms of the
economic burden placed on participants
PS-TRUST: Provably Secure Solution for Truthful Double Spectrum Auctions
Truthful spectrum auctions have been extensively studied in recent years.
Truthfulness makes bidders bid their true valuations, simplifying greatly the
analysis of auctions. However, revealing one's true valuation causes severe
privacy disclosure to the auctioneer and other bidders. To make things worse,
previous work on secure spectrum auctions does not provide adequate security.
In this paper, based on TRUST, we propose PS-TRUST, a provably secure solution
for truthful double spectrum auctions. Besides maintaining the properties of
truthfulness and special spectrum reuse of TRUST, PS-TRUST achieves provable
security against semi-honest adversaries in the sense of cryptography.
Specifically, PS-TRUST reveals nothing about the bids to anyone in the auction,
except the auction result. To the best of our knowledge, PS-TRUST is the first
provably secure solution for spectrum auctions. Furthermore, experimental
results show that the computation and communication overhead of PS-TRUST is
modest, and its practical applications are feasible.Comment: 9 pages, 4 figures, submitted to Infocom 201
Efficient and Equitable Airport Slot Allocation
This paper studies slot allocation at congested airports in Europe. First, I discuss the inefficiencies of the current regulation, introduced as part of the liberalisation process of the air transport market. Then, I consider three marked based methods which are suitable to achieve a more efficient allocation of slots to airlines: congestion pricing, auctions and secondary trading. These methods are examined in terms of their ability to improve efficiency and in terms of their implications on the distribution of slots’ scarcity rents. Special attention is drawn to complementarities between slots. Finally, I propose to auction slots periodically, allowing secondary trading well before the first auction takes place. By selling slots before the first auction incumbents can be partially compensated for the subsequent withdrawal of their slots.
Deep Learning Meets Mechanism Design: Key Results and Some Novel Applications
Mechanism design is essentially reverse engineering of games and involves
inducing a game among strategic agents in a way that the induced game satisfies
a set of desired properties in an equilibrium of the game. Desirable properties
for a mechanism include incentive compatibility, individual rationality,
welfare maximisation, revenue maximisation (or cost minimisation), fairness of
allocation, etc. It is known from mechanism design theory that only certain
strict subsets of these properties can be simultaneously satisfied exactly by
any given mechanism. Often, the mechanisms required by real-world applications
may need a subset of these properties that are theoretically impossible to be
simultaneously satisfied. In such cases, a prominent recent approach is to use
a deep learning based approach to learn a mechanism that approximately
satisfies the required properties by minimizing a suitably defined loss
function. In this paper, we present, from relevant literature, technical
details of using a deep learning approach for mechanism design and provide an
overview of key results in this topic. We demonstrate the power of this
approach for three illustrative case studies: (a) efficient energy management
in a vehicular network (b) resource allocation in a mobile network (c)
designing a volume discount procurement auction for agricultural inputs.
Section 6 concludes the paper
Efficient and equitable airport slot allocation
This paper studies slot allocation at congested airports in Europe. First, I discuss the inefficiencies of the current regulation, introduced as part of the liberalisation process of the air transport market. Then, I consider three marked based methods which are suitable to achieve a more efficient allocation of slots to airlines: congestion pricing, auctions and secondary trading. These methods are examined in terms of their ability to improve efficiency and in terms of their implications on the distribution of slots? scarcity rents. Special attention is drawn to complementarities between slots. Finally, I propose to auction slots periodically, allowing secondary trading well before the first auction takes place. By selling slots before the first auction incumbents can be partially compensated for the subsequent withdrawal of their slots
Distributed and dynamic traffic congestion controls without requiring demand forecasting: Tradable network permits and its implementation mechanisms
Tohoku University赤松隆課
Efficient Three-stage Auction Schemes for Cloudlets Deployment in Wireless Access Network
Cloudlet deployment and resource allocation for mobile users (MUs) have been
extensively studied in existing works for computation resource scarcity.
However, most of them failed to jointly consider the two techniques together,
and the selfishness of cloudlet and access point (AP) are ignored. Inspired by
the group-buying mechanism, this paper proposes three-stage auction schemes by
combining cloudlet placement and resource assignment, to improve the social
welfare subject to the economic properties. We first divide all MUs into some
small groups according to the associated APs. Then the MUs in same group can
trade with cloudlets in a group-buying way through the APs. Finally, the MUs
pay for the cloudlets if they are the winners in the auction scheme. We prove
that our auction schemes can work in polynomial time. We also provide the
proofs for economic properties in theory. For the purpose of performance
comparison, we compare the proposed schemes with HAF, which is a centralized
cloudlet placement scheme without auction. Numerical results confirm the
correctness and efficiency of the proposed schemes.Comment: 22 pages,12 figures, Accepted by Wireless Network
Economic regulation for multi tenant infrastructures
Large scale computing infrastructures need scalable and effi cient resource allocation mechanisms to ful l the requirements of its participants and applications while the whole system is regulated to work e ciently. Computational markets provide e fficient allocation mechanisms that aggregate information from multiple sources in large, dynamic and complex systems where there is not a single source with complete information. They have been proven to be successful in matching resource demand and resource supply in the presence of sel sh multi-objective and utility-optimizing users and sel sh pro t-optimizing providers. However, global infrastructure metrics which may not directly affect participants of the computational market still need to be addressed -a.k.a. economic externalities like load balancing or energy-efficiency.
In this thesis, we point out the need to address these economic externalities, and we design and evaluate appropriate regulation mechanisms from di erent perspectives on top of existing economic models, to incorporate a wider range of objective metrics not considered otherwise. Our main contributions in this thesis are threefold; fi rst, we propose a taxation mechanism that addresses the resource congestion problem e ffectively improving the balance of load among resources when correlated economic preferences are present; second,
we propose a game theoretic model with complete information to derive an algorithm to aid resource providers to scale up and down resource supply so energy-related costs can be reduced; and third, we relax our previous assumptions about complete information on the resource provider side and design an incentive-compatible mechanism to encourage users to truthfully report their resource requirements effectively assisting providers to make energy-eff cient allocations while providing a dynamic allocation mechanism to users.Les infraestructures computacionals de gran escala necessiten mecanismes d’assignació de recursos escalables i eficients per complir amb els requisits computacionals de tots els seus participants, assegurant-se de que el sistema és regulat apropiadament per a que funcioni de manera efectiva. Els mercats computacionals són mecanismes d’assignació de recursos eficients que incorporen informació de diferents fonts considerant sistemes de gran escala, complexos i dinàmics on no existeix una única font que proveeixi informació completa de l'estat del sistema. Aquests mercats computacionals han demostrat ser exitosos per acomodar la demanda de recursos computacionals amb la seva oferta quan els seus participants son considerats estratègics des del punt de vist de teoria de jocs. Tot i això existeixen mètriques a nivell global sobre la infraestructura que no tenen per que influenciar els usuaris a priori de manera directa. Així doncs, aquestes externalitats econòmiques com poden ser el balanceig de càrrega o la eficiència energètica, conformen una línia d’investigació que cal explorar. En aquesta tesi, presentem i descrivim la problemàtica derivada d'aquestes externalitats econòmiques. Un cop establert el marc d’actuació, dissenyem i avaluem mecanismes de regulació apropiats basats en models econòmics existents per resoldre aquesta problemàtica des de diferents punts de vista per incorporar un ventall més ampli de mètriques objectiu que no havien estat considerades fins al moment. Les nostres contribucions principals tenen tres vessants: en primer lloc, proposem un mecanisme de regulació de tipus impositiu que tracta de mitigar l’aparició de recursos sobre-explotats que, efectivament, millora el balanceig de la càrrega de treball entre els recursos disponibles; en segon lloc, proposem un model teòric basat en teoria de jocs amb informació o completa que permet derivar un algorisme que facilita la tasca dels proveïdors de recursos per modi car a l'alça o a la baixa l'oferta de recursos per tal de reduir els costos relacionats amb el consum energètic; i en tercer lloc, relaxem la nostra assumpció prèvia sobre l’existència d’informació complerta per part del proveïdor de recursos i dissenyem un mecanisme basat en incentius per fomentar que els usuaris facin pública de manera verídica i explícita els seus requeriments computacionals, ajudant d'aquesta manera als proveïdors de recursos a fer assignacions eficients des del punt de vista energètic a la vegada que oferim un mecanisme l’assignació de recursos dinàmica als usuari
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