Optimal social welfare:A many-to-many data transaction mechanism based on double auctions

With the development of Big Data and the Internet of Things(IoT),the data value is more significant in both academia and industry.Trading can achieve maximal data value and prepare data for smart city services.Due to data's unique characteristics,such as dispersion,heterogeneity and distributed storage,an unbiased platform is necessary for the data trading market with rational trading entities.Meanwhile,there are multiple buyers and sellers in a practical data trading market,and this makes it challenging to maximize social welfare.To solve these problems,this paper proposes a Social-Welfare-Oriented Many-to-Many Trading Mechanism(SOMTM),which integrates three entities,a trading process and an algorithm named Many-to-Many Trading Algorithm(MMTA).Based on the market scale,market dominated-side and market fixed-side,simulations verify the convergency,economic properties and efficiency of SOMTM.

Evolutionary Techniques for Reverse Auctions

Winner determination is one of the main challenges in combinatorial auctions. However, not much work has been done to solve this problem in the case of reverse auctions using evolutionary techniques. This has motivated us to propose an improvement of a genetic algorithm based method, we have previously proposed, to address two important issues in the context of combinatorial reverse auctions: determining the winner(s) in a reasonable processing time, and reducing the procurement cost. In order to evaluate the performance of our proposed method in practice, we conduct several experiments on combinatorial reverse auctions instances. The results we report in this paper clearly demonstrate the efficiency of our new method in terms of processing time and procurement cost.

Cotton Reserves Auctions Touch 57% of Release Plan but Prices Rally

With manufacturing slowly recovering amid improving economic conditions, cotton demand has increased prompting more orders among mills. However, with a deficient supply of cotton in the market, prices extend gains since Oct. 2009.

A New Protocol for Multi-Item Electronic Auctions

The authors propose a new protocol for muhizzitem electronic auctions. It has the following advantages： first, the protocol is more applicable and flexible than the previous protocols, in this protocol, each bidder can decide how many items to buy according to diiferent bidding prices, which are set by the seller at the beginning of the auction; second, privacy is well preserved, no third parties are needed in the protocol and the auction outcome is jointly computed by the bidders on their own without uncovering any additional information.

Multi-Dimensional Customer Data Analysis in Online Auctions

In this paper, we designed a customer-centered data warehouse system with five subjects： listing, bidding, transaction, accounts, and customer contact based on the business process of online auction companies. For each subject, we analyzed its fact indexes and dimensions. Then take transaction subject as example, analyzed the data warehouse model in detail, and got the multi-dimensional analysis structure of transaction subject. At last, using data mining to do customer segmentation, we divided customers into four types： impulse customer, prudent customer, potential customer, and ordinary customer. By the result of multi-dimensional customer data analysis, online auction companies can do more target marketing and increase customer loyalty.

A Few Notes on Auctions:The Boom in the Asymmetric Art Auction Market

The art market,following the example of financial markets,is divided into a primary market,where works are traded directly from artists,and a secondary market that is mainly the auction market.COVID-19 and galloping inflation have influenced the creation of a bull market in artwork.The high incomes of some buyers and inflation have influenced the emergence of the so-called“glittery”art market.Works by neglected artists have“very much taken on a life of their own”:fantasy art,works by young poster artists,casting“nightmares”-have been selling well for more than a year.People terrified of inflation are putting their money“in works of art”.The auction market dominates the primary market because anonymity makes it easier to enter the art market.The gallery market does not guarantee this anonymity.Very often,the auction market for works of art is used for money laundering.The purpose of this paper is to show that auction mechanisms are a good tool for the efficient allocation of goods and money in an era of galloping inflation,including non-standard objects such as works of art.These mechanisms,due to the information asymmetry,often lead to the generation of all kinds of pathologies and the increasing incidence of the phenomenon known as the winner’s curse.

What Drives Up Land Price in China?Evidence from Bidding Processes of Land Auctions in Beijing

The land price in big cities draws much attention and discussion for its skyrocketing appreciation.Most researches are from the macro perspective due to data restriction.This paper aims to investigate the critical factors in the price formation process of a land auction,using the listing auction micro bidding-level data in Beijing from 2013 to 2018.We construct a model for the relationship between quitting price and land,bidder's characteristics,housing market conditions and competitive intensity(including private and public signals),then we use OLS for identification.We find that competitive intensity increases the quitting price by causing competition and interaction between bidders.More importantly,we find evidence of cheating behavior in the land market.Results show that bidders have higher quitting prices when they are in a joint venture,and when a central SOE developer or a top 10 developer exist in the joint venture.We also find different behavior of developers in the short run and long run.Our research contributes to the literature of land auctions by analyzing the price formation process and developers'behavior.We also provide supporting evidence for the government to make adjustments of the auction system and identify the cheating developers.

The cost of privacy on blockchain:A study on sealed-bid auctions

In order to preserve privacy in a blockchain ecosystem,the main objective is to keep a transaction's data private,such as the sender,the receiver,and the amount transferred.The current work studies the cryptographic tools commonly used to achieve this type of privacy,primarily focusing on the Ethereum blockchain.Such tools usually require many computational and storage resources,leading to additional fees.An anonymous auction protocol was developed as a case study to explore these costs,where hiding the identity and the amount of the bids utilizes a variety of cryptographic primitives.The proposed implementation was compared against three sealed-bid auction protocols,which utilize similar cryptographic tools for preserving privacy throughout the auction process.The results show that providing an additional level of anonymity,such as hiding someone's identity,can increase the gas cost significantly,up to 2.5 times,depending on the choice of the cryptographic tools,which determine the usage of the blockchain's storage and computational resources.By adjusting the level of decentralization on the application level by moving some operations off-chain and maintaining the role of the auctioneer,we show that we can maintain anonymity while reducing the gas cost by 40%.

基于拍卖理论的动态多代理同类机调度算法

This paper addresses a multi-agent scheduling problem with uniform parallel machines owned by a resource agent and competing jobs with dynamic arrival times that belong to different consumer agents.All agents are self-interested and rational with the aim of maximizing their own objectives,resulting in intense resource competition among consumer agents and strategic behaviors of unwillingness to disclose private information.Within the context,a centralized scheduling approach is unfeasible,and a decentralized approach is considered to deal with the targeted problem.This study aims to generate a stable and collaborative solution with high social welfare while simultaneously accommodating consumer agents’preferences under incomplete information.For this purpose,a dynamic iterative auction-based approach based on a decentralized decision-making procedure is developed.In the proposed approach,a dynamic auction procedure is established for dynamic jobs participating in a realtime auction,and a straightforward and easy-to-implement bidding strategy without price is presented to reduce the complexity of bid determination.In addition,an adaptive Hungarian algorithm is applied to solve the winner determination problem efficiently.A theoretical analysis is conducted to prove that the proposed approach is individually rational and that the myopic bidding strategy is a weakly dominant strategy for consumer agents submitting bids.Extensive computational experiments demonstrate that the developed approach achieves high-quality solutions and exhibits considerable stability on largescale problems with numerous consumer agents and jobs.A further multi-agent scheduling problem considering multiple resource agents will be studied in future work.

t-Private and t-Secure Auctions

In most of the auction systems the values of bids are known to the auctioneer. This allows him to manipulate the outcome of the auction. Hence, one might be interested in hiding these values. Some cryptographically secure protocols for electronic auctions have been presented in the last decade. Our work extends these protocols in several ways. On the basis of garbled circuits, i.e., encrypted circuits, we present protocols for sealed-bid auctions that fulfill the following requirements： 1） protocols are information-theoretically t-private for honest but curious parties; 2） the number of bits that can be learned by malicious adversaries is bounded by the output length of the auction; 3） the computational requirements for participating parties are very low： only random bit choices and bitwise computation of the XOR-function are necessary. Note that one can distinguish between the protocol that generates a garbled circuit for an auction and the protocol to evaluate the auction. In this paper we address both problems. We will present a t-private protocol for the construction of a garbled circuit that reaches the lower bound of 2t ＋ 1 parties, and Finally, we address the problem of bid changes in an auction. a more randomness efficient protocol for （t ＋ 1）^2 parties

Cotton Reserves Auctions Touch 57% of Release Plan but Prices Rally

With manufacturing slowly recovering amid improving economic conditions, cotton demand has increased prompting more orders among mills. However, with a deficient supply of cotton in the market, prices

OPTIMAL RESERVE PRICE IN STATIC AND DYNAMIC SPONSORED SEARCH AUCTIONS

Sponsored search advertising is a significant revenue source for search engines. To ameliorate revenues, search engines often set fixed or variable reserve price to in influence advertisers＇ bidding. This paper studies the optimal reserve price for a generalized second-price auction （GSP） under both static and dynamic settings. We show that if advertisers＇ per-click value has an increasing generalized failure rate, the search engine＇s revenue rate is quasi-concave and hence there exists an optimal reserve price under both settings. Different from a static GSP auction where the optimal reserve price is proved to be constant, in a dynamic setting the optimal reserve price is dependent on not only advertisers＇ per-click values, but also the number of ad links sold. A search engine should gradually raise reserve price as more qualified advertisers arrive, and maintain the same threshold after all first-page positions are occupied.

Optimal bundles for sponsored search auctions via bracketing scheme

Sponsored search auction has been recently studied and auctioneer's revenue is an important consideration in probabilistic single-item second-price auctions. Some papers have analyzed the revenue maximization problem on different methods to bundle contexts. In this paper, we propose a more flexible and natural method which is called the bracketing method. We prove that finding a bracketing scheme that maximizes the auctioneer's revenue is strongly NP-hard. Then, a heuristic algorithm is given. Experiments on three test cases show that the revenue of the optimal bracketing scheme is very close to the optimal revenue without any bundling constraint, and the heuristic algorithm performs very well. Finally, we consider a simpler model that for each row in the valuation matrix, the non-zero cells have the same value. We prove that the revenue maximization problem with Kanonymous signaling scheme and cardinality constrained signaling scheme in this simpler model are both NP-hard.

Winner determination problem with loss-averse buyers in reverse auctions

Reverse auctions have been widely adopted for purchasing goods and services. This paper considers a novel winner determination problem in a multiple-object reverse auction in which the buyer involves loss-averse behavior due to uncertain attributes. A corresponding winner determination model based on cumulative prospect theory is proposed. Due to the NP-hard characteristic, a loaded route strategy is proposed to ensure the feasibility of the model. Then, an improved ant colony algorithm that consists of a dynamic transition strategy and a Max-Min pheromone strategy is designed. Numerical experiments are conducted to illustrate the effectiveness of the proposed model and algorithm. We find that under the loaded route strategy, the improved ant colony algorithm performs better than the basic ant colony algorithm. In addition, the proposed model can effectively characterize the buyer's loss-averse behavior.

Optimal Procurement Management by Reverse Auctions for a Price-Setting Newsvendor

In this paper, we study the optimal procurement management by reverse auctions for a price-setting newsvendor(retailer) in a single period setting. The retailer facing price-dependent stochastic demand first designs a procurement contract and then invites the suppliers to bid for this contract in the reverse auction. The winning supplier produces and delivers the demanded quantity.The retailer obtains the procurement quantity and simultaneously determines the retail price. By using the price elasticity of the lost-sales rate, we show that the retailer’s expected profit(excluding the procurement cost) is a concave function of the purchased quantity, which can be used to obtain the optimal procurement and retail pricing decisions for the retailer. Further, when the underlying random term of demand function is normally distributed under left-truncation(at 0), we get the analytical expressions of the purchased quantity and expected profit function for the retailer. Moreover, some numerical examples are given.

Multi-Blockchain Based Data Trading Markets With Novel Pricing Mechanisms

In the era of big data,there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive data.Data security and data pricing,however,are still widely regarded as major challenges in this respect,which motivate this research on the novel multi-blockchain based framework for data trading markets and their associated pricing mechanisms.In this context,data recording and trading are conducted separately within two separate blockchains:the data blockchain(DChain) and the value blockchain(VChain).This enables the establishment of two-layer data trading markets to manage initial data trading in the primary market and subsequent data resales in the secondary market.Moreover,pricing mechanisms are then proposed to protect these markets against strategic trading behaviors and balance the payoffs of both suppliers and users.Specifically,in regular data trading on VChain-S2D,two auction models are employed according to the demand scale,for dealing with users’ strategic bidding.The incentive-compatible Vickrey-Clarke-Groves(VCG)model is deployed to the low-demand trading scenario,while the nearly incentive-compatible monopolistic price(MP) model is utilized for the high-demand trading scenario.With temporary data trading on VChain-D2S,a reverse auction mechanism namely two-stage obscure selection(TSOS) is designed to regulate both suppliers’ quoting and users’ valuation strategies.Furthermore,experiments are carried out to demonstrate the strength of this research in enhancing data security and trading efficiency.

Electronic Auction Scheme Based on Smart Contract and IPFS

Sealed-bid auctions are a vital transaction tool in the e-commerce field.Traditional centralized auction schemes typically result in severe threats to data integrity,information transparency,and traceability owing to their excessive reliance on third parties,and blockchain-based auction schemes generally suffer from high storage costs and are deficient in functional and architectural design.To solve these problems,this study presents a sealed-bid auction scheme that removes the third-party based on an Ethereum smart contract,ensuring data integrity,openness,and transparency in the execution process.The commitment mechanism and distributed storage system help to significantly reduce the user’s storage cost and protect the privacy of user bids.For the functional design,this study introduces a fulltext-retrieval and dispute-processing module for commodities,which reduces the defects existing in the functional module design of existing auction systems.Furthermore,a prototype auction system on the Ethereum test chain is built to validate the proposed scheme.Experiments show that compared with traditional storage methods,indirect storage based on a distributed storage system of texts and images can reduce the storage cost by at least 50%while ensuring data integrity.Finally,the gas cost at each stage of the auction scheme and the time required for the full-text retrieval of products are recorded to evaluate the scheme performance and analyze the test results.

Cooperative management of an emission trading system:a private governance and learned auction for a blockchain approach

Although blockchain technology has received a significant amount of cutting-edge research on constructing a novel carbon trade market in theory,there is little research on using blockchain in carbon emission trading schemes(ETS).This study intends to address existing gaps in the literature by creating and simulating an ETS system based on blockchain technology.Using the ciphertext-policy attributed-based encryption algorithm and the Fabric network to build a platform may optimize the amount of data available while maintaining privacy security.Considering the augmentation of information interaction during the auction process brought about by blockchain,the learning behavior of bidding firms is introduced to investigate the impact of blockchain on ETS auction.In particular,implementing smart contracts can provide a swift and automatic settlement.The simulation results of the proposed system demonstrate the following:(1)fine-grained access is possible with a second delay;(2)the average annual compliance levels increase by 2%when bidders’learning behavior is considered;and(3)the blockchain network can process more than 350 reading operations or 7 writing operations in a second.Novel cooperative management of an ETS platform based on blockchain is proposed.The data access control policy based on CP-ABE is used to solve the contradiction between data privacy on the firm chain and government supervision.A learned auction strategy is proposed to suit the enhancement of information interaction caused by blockchain technology.This study provides a new method for climate change policymakers to consider the blockchain application of the carbon market.

Low Complexity Joint Spectrum Resource and Power Allocation for Ultra Dense Networks

In this paper,we propose a low complexity spectrum resource allocation scheme cross the access points(APs)for the ultra dense networks(UDNs),in which all the APs are divided into several AP groups(APGs)and the total bandwidth is divided into several narrow band spectrum resources and each spectrum resource is allocated to APGs independently to decrease the interference among the cells.Furthermore,we investigate the joint spectrum and power allocation problem in UDNs to maximize the overall throughput.The problem is formulated as a mixed-integer nonconvex optimization(MINCP)problem which is difficult to solve in general.The joint optimization problem is decomposed into two subproblems in terms of the spectrum allocation and power allocation respectively.For the spectrum allocation,we model it as a auction problem and a combinatorial auction approach is proposed to tackle it.In addition,the DC programming method is adopted to optimize the power allocation subproblem.To decrease the signaling and computational overhead,we propose a distributed algorithm based on the Lagrangian dual method.Simulation results illustrate that the proposed algorithm can effectively improve the system throughput.

Design of the contingent royalty rate as related to the type of investment

This study investigates the design of the royalty rate in a first-price auction across three types of investments:incremental and lumpy with or without an exogenously given intensity.A bidder’s investment cost comprises private information.This,together with the stochastic evolution of the price of the output generated from the auctioned project,precludes the seller from setting the exact dates of investment with the winner.However,the seller can set the royalty rate to equate the winner’s royalty payment with the winner’s information rent so that the winner acts as if to maximize the seller’s revenue.We derive two main conclusions.First,compared with the case in which investment is lumpy with an exogenously given intensity,the seller can set a lower royalty rate on incremental investment because she can collect additional royalty payments from the winner,who has the option to later expand capacity.Second,the impact of output price uncertainty on the optimal royalty rate for the three types of investments exhibits two different patterns.When investment is either incremental or lumpy with an exogenously given intensity,greater output price uncertainty reduces the royalty rate.When investment is lumpy with variable intensity,greater output uncertainty raises the royalty rate.Our results imply that auctioneers may charge differential royalty rates for different types of investments.