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Policy-mapping mechanisms can efficiently help to realize the exchange and the sharing of cross-domain information at low cost. However, due to concerns over policy conflicts, if not sufficient incentives, most selfish domains are often disinterested in helping others to implement policy mapping cooperatively. Thus an appropriate incentive mechanism is required. In this paper, we propose an incentive mechanism to encourage selfish domains to take part in policy mapping and resolve policy conflicts. Formulating conflict resolution as a double auction and solving Bayesian Nash equilibrium, we design the optimal asking/bidding price scheme to maximize the benefits of the domains involved. Simulations demonstrate that our approach can efficiently incentivize selfish domains to take part in cooperation.

Auction mechanisms for exchanging divisible resources (e.g., electricity, cloud resources, and network bandwidth) among distributed agents have been extensively studied. In particular, divisible double auction allows both buyers and sellers to dynamically submit their prices until convergence. However, severe privacy concerns may arise in the double auctions since all the agents may have to disclose their sensitive data such as the bid profiles (i.e., bid amounts and prices in different iterations) to other agents for resource allocation. To address such concerns, we propose an efficient and private auction system ETA by co-designing the divisible double auction mechanism with the Intel SGX, which executes the computation for auction while ensuring confidentiality and integrity for the buyers/sellers’ sensitive data. Furthermore, ETA seals the bid profiles to achieve a Progressive Second Price (PSP) auction for optimally allocating divisible resources while ensuring truthfulness with a Nash Equilibrium. Finally, we conduct experiments to validate the performance of private auction system ETA.

International audience; Local energy markets allow neighbours to exchange energy among them. Their traditional implementation using sequential auctions has proven to be inefficient and even counterproductive in some cases. In this paper we propose a combinatorial double auction for the exchange of energy for several time-slots simultaneously. We suppose that participants have a flexible demand; flexibility being obtained, for example, by the usage of a battery. We show the benefits of the approach and we provide an example of how it can improve the utility of all the participants in the market.

The lack of radio spectrum resources and low resource utilization have always been existing problems. Current researchers usually use spectrum auctions to improve the utilization of radio spectrum. The existing auction mechanisms rarely consider the attributes of the radio frequency spectrums, which is likely to cause problems that it cannot meet user needs. Based on the double auction mechanism, we propose an auction mechanism perceiving spectrum attributes to solve this problem. This mechanism can improve the satisfaction of secondary users, provide a certain incentive for high-quality service providers, and at the same time reuse spectrums. The economic robustness of the auction mechanism is of great importance. The auction mechanism proposed in this paper satisfies the three economic properties of truthfulness, individual rationality, and ex-post budget balance, so it is economically robust.

Mobile crowdsourcing (MCS) is gaining real attention in recent years as it has found widespread applications such as traffic monitoring, pollution control surveillance, locating endangered species, and many others. This paradigm of research is showing an interesting power of smart devices that are held by intelligent agents (such as human beings). In MCS, the tasks which are outsourced are executed by the task executors (intelligent agents carrying smart devices). In this paper, how overlapping tasks (with a deadline) can be disseminated in slots and leveraged as evenly as possible to the stakeholders (task executors or sellers) is addressed through a scalable scheduling (interval partitioning) and economic mechanism (double auction). It is proved that our mechanism is truthful and also shown via simulation that our proposed mechanism will perform better when the agents are manipulative in nature.

The double auction marketplaces usually charge fees to traders to make profits, but little work has been done on analyzing how marketplaces charge appropriate fees to make profits in multiple marketplaces competing environment. In this paper, we investigate this problem by using game theory. Specifically, we consider four typical types of fees, and use Fictitious Play algorithm to analyze the Nash equilibrium market selection and bidding strategy of traders in competing environment when different types of fees are charged. Building on this, we draw insights about how the marketplaces charge strategies in equilibrium when sellers and buyers have made the choice of the marketplace and bid in Nash equilibrium. Furthermore, we investigate which type of fees is more competitive in terms of maximizing profits while keeping traders staying in the marketplaces. Our experimental results provide useful insights on setting charging strategies for competing double auction marketplaces.

For improving the utilization and alleviating the shortage of spectrum resources, centralized database-based dynamic spectrum sharing system has been proposed. However, the centralized architecture is often considered to be nontransparent and more vulnerable to be attacked. To address the above issues, in this work, we propose a blockchain-based dynamic spectrum sharing framework. The distributed architecture based on blockchain technology can bring advantages including decentralization, openness, transparency, immutability and auditability. Considering that the introduction of blockchain technology in spectrum sharing will also bring privacy issues, we design a differential privacy-based privacy-preserving double auction mechanism. The auction can be implemented in smart contracts running on the blockchain. The proposed double auction mechanism is proved to satisfy differential privacy, individual rationality, computational efficiency, and truthfulness. The extensive experiments demonstrate the effectiveness of the proposed dynamic spectrum sharing scheme.

Under-utilization of wireless spectrum by the licensed owners creates spectrum holes which can be opportunistically exploited by secondary users (SUs). By incorporating dynamic spectrum access techniques, Cognitive Radio (CR) emerges as a novel technology which facilitates redistribution of the spectrum holes dynamically. In this context, this paper proposes a double auction framework for CR networks which addresses the channel allocation problem to boost the spectrum utilization efficiency. Multi-winner allocation relates to the condition where a common channel can be utilized by multiple non-interfering SUs which induces spectrum reuse. However, a single SU obtains at most one channel. To avoid disturbances during data transmission, availability time of a channel plays a key role. In this paper, bid submission from SUs rely on channel availability time, and to lease the idle channels, licensed users specify certain ask values. The proposed double auction mechanism develops winner determination and pricing strategies which are proved to be truthful and significantly improves the usage of the radio spectrum. Network simulations validate the improved performance of the proposed auction model compared to the McAfee auction.

Nowaday’s energy demand and energy production are increasing. Renewable energy resources will play an important role in managing future production of electricity due to an increase in the development of societies. The centralized energy trading system faces a challenge in terms of fair energy distribution. Centralized existing energy trading system totally relies on a central system or third party, because the third party has many drawbacks in the form of record tampering or record altering. The fair transaction is the main issue in the energy trading sector. When the bitcoin is introduced in the market, the trust of Blockchain technology is increased. We proposed a Blockchain based energy trading system in peer to peer networks. Blockchain technology provides trust, security, and transparency for energy trading. In Blockchain technology, there is no necessary need of the third party in the energy supply sector. In our proposed paper, we facilitate the prosumer who produces renewable energy and sells surplus energy to the consumer. We achieved transparency, accuracy, efficiency in our proposed paper. Using a double auction process, we obtain low energy price and acheived consumer trust in energy trading.

The growing number of Renewable Energy Sources (RES) in the energy system provides new market approaches according to price and decentralized generation of electricity. Local market, in which consumers and prosumers can trade locally by generation of electricity through RES directly within their community. This approach creates a balance between generation and consumption in a decentralized manner. In this paper, a distributed technology of Blockchain is used, which highlights the decentralized nature of local market. It provides a decentralized market platform for trading locally without the need of central intermediary through Periodic Double Auction (PDA) mechanism. With the introduction of Smart Grid (SG) systems, there have been improvements in how utility companies interact with customers with regards of electricity usage. However, there is a tendency for the data of users to be compromised in SG. In this proposed system, users are able to do trading through PDA and get access of their own previous history. The blockchain provides transparency, traceability and is utilized to mitigate the above mentioned problems. Smart contacts, are used to exclude the third party to provide a transparent system between users in the network.

Virtualization technology plays an important role in cloud computing. Virtual machine (VM) migration can reduce the cost of cloud computing data centers. In this paper, a double auction-based VM migration algorithm is proposed, which takes the cost of communication between VMs into account under normal operation situation. The algorithm of VM migration is divided into two parts: (1) selecting the VMs to be migrated according to the communication and occupied resources factors of VMs, (2) determining the destination host for VMs which to be migrated. We proposed VMs greedy selection algorithm (VMs-GSA) and VM migration double auction mechanism (VMM-DAM) to select VMs and obtain the mappings between VMs and underutilized hosts. Compared with other existing works, the algorithms we proposed have advantages.

Having demonstrated the impact of evolution on price patterns, we test its robustness: the code is changed, products are added, and variables altered. To help explain the results, an old experiment called a double auction is built using Netlogo to demonstrate the difference between short-term and long-term pricing dynamics.

One of the major problems in current implementations of iterative double auction is that they rely on a trusted third party to handle the auction process. This imposes the risk of single point of failures and monopoly. In this paper, we aim to tackle this problem by proposing a novel decentralized and trustless framework for iterative double auction based on blockchain. Our design adopts the smart contract and state channel technologies to enable a double auction process among parties that do not trust each other, while minimizing the blockchain transactions. We provide a formal development of the framework and highlight the security of our design against adversaries.

Double auction-based mechanisms have gained considerable attention as autonomous cloud service negotiation because it has been proven to be economically efficient and possesses the ability to accommodate multiple buyers and sellers. The main objective of this paper was to outline the limitations of the practically tested double auction mechanisms in cloud service negotiations; and subsequently suggest future vision. Ten practically proven double auction-based mechanisms have been surveyed from the research database, whereby most of them were found to employ the double auction protocol and two variations of the double auction protocol, namely continuous double auction and combinatorial double auction. Comparisons on the types, objectives, parameters, and results between the selected mechanisms are presented in this paper. Moreover, it was identified that the surveyed double auction mechanisms were unable to address multi-attributes heterogeneous cloud services because it required a long execution time. Nevertheless, future vision managed to be suggested to solve these issues.

In this paper producers and distributors in supply chain need to agree on orders quantities and schedules. Instead of classical integrated production-distribution planning model we propose a reformulation in form of centralized auction, oriented on maximization of individual benefits. Production costs and inventory costs are handled as parts of competitive offers/bids. We reformulate the inventory costs, so they can be interpreted as costs of reduction or increase in ordered quantities. Ordered distribution quantities are balanced in each period with offered production. We point to the similarity of the proposed model to the centralized double energy market for demands with shifting capabilities. This allows us to use pricing model based on uplifts minimization, originally developed for pool-based auction. As a result we obtain prices complemented with minimum uplifts that support competitive producers and distributors to follow schedules efficient for entire supply chain. This novel coordination mechanism is formulated as a set of two clearing and pricing mixed integer linear programming problems (MILP). Simple example illustrates the approach.

In cloud computing resource allotment is one of the most demanding areas. Resources are attempt through fixed price model by the cloud provider and users, which is not much efficient and justified scenario. The optimal processing cost for each task by using resource bidding procedures which consider the impact of cost on long-term trade. Most of the existing resource allocation techniques focus on static task based allocation. The dynamic resource bidding price model based on auction is efficient and achieves optimal cost computation. The proposed dynamic model namely Double Auction Procurement Game for Resource Allocation (DAPGRA) uses winner determination scheme for cost computation to achieve optimal resource allocation for tasks. The technique takes into account requirements of both users and Cloud Service Providers (CSPs) and calculates the final cost, based on the trade information. Results show that proposed schemes/mechanisms outperform other existing schemes/mechanism.

A combinatorial double auction is a type of double-side auction which makes buyers and sellers trade goods more conveniently than multiple combinatorial auctions. However, the winner determination problem (WDP) in combinatorial double auctions poses a challenge due to computation complexity. Particle swarm optimization (PSO) is one of the well-known meta-heuristic approaches to deal with complex optimization problems. Although there are many studies on combinatorial auctions, there is little study on application of PSO approach in combinatorial double auctions. In this paper, we consider combinatorial double auction problem in which there are transaction costs, supply constraints and non-negative surplus constraints. We formulate the WDP of combinatorial double auction problem as an integer programming problem formulation. As standard discrete PSO algorithm suffers from the premature convergence problem, we adopt a cooperative coevolution approach to develop a discrete cooperative coevolving particle swarm optimization (DCCPSO) algorithm that can scale with the problem better. The effectiveness of the proposed algorithm is illustrated by several numerical examples by comparing the results with standard DPSO algorithm.

With the emerging technologies of Internet of Things (IOTs), the capabilities of mobile devices have increased tremendously. However, in the big data era, to complete tasks on one device is still challenging. As an emerging technology, crowdsourcing utilizing crowds of devices to facilitate large scale sensing tasks has gaining more and more research attention. Most of existing works either assume devices are willing to cooperate utilizing centralized mechanisms or design incentive algorithms using double auctions. There are two cases that may not practical to deal with, one is a lack of centralized controller for the former, the other is not suitable for the seller device’s resource constrained for the later. In this paper, we propose a truthful incentive mechanism with combinatorial double auction for crowd sensing task assignment in device-to-device (D2D) clouds, where a single mobile device with intensive sensing task can hire a group of idle neighboring devices. With this new mechanism, time critical sensing tasks can be handled in time with a distributed nature. We prove that the proposed mechanism is truthful, individual rational, budget balance and computational efficient.

Arriving in Tucson amidst the summer monsoon wind, rain, and lightning, I enroll a small assorted and assertive mix of undergraduate and graduate students ranging from engineering to economics to see what we can learn together from the pursuit of new projects to be defined in-process. I put them through a few of the standard “hand-run” experiments to give them experience from the subject’s perspective, and we read a couple of my first papers. It was early in the semester when Mike Vannoni, an engineering undergraduate, asked if I knew of the PLATO teaching computer system in the library. No, I did not. Mike said that he thought it could be used to run our interactive economics experiments. We checked with the physics professor in charge and the class agreed that this had great potential. We soon all had ideas, projects, and energy for the semester. Without it being any part of our intentions, we were defining a new future for experimental economics and more remarkably, for economic science generally. Several students learned PLATO programming to run the first market and group decision experiments in the laboratory; several enrolled in follow-on special studies courses to continue their projects. Arlington Williams created the first-in-the-world electronic bid-ask double auction trading system, in operation and under continuous development for decades. That class alone produced several joint publications exploring the performance of a variety of auctions, trading, and public good provision mechanisms. For 26 exciting years, we built large on the original Purdue Foundation, a glorious second Camelot for me and many students and faculty.

We revisit the inefficiency of the uniform price auction, one of the standard multi-unit auction formats, for allocating multiple units of a single good. In the uniform price auction, each bidder submits a sequence of non-increasing marginal bids, one for each additional unit. The per unit price is then set to be the highest losing bid. We focus on the pure Nash equilibria of such auctions, for bidders with submodular valuation functions. Our result is a tight upper and lower bound on the inefficiency of equilibria, showing that the Price of Anarchy is bounded by 2.188. This resolves one of the open questions posed in previous works on multi-unit auctions.

In this paper, on the basis of the analysis of common market model and some economic theories in the cloud computing resource management process, we propose a cloud resource management model based on combinatorial double auction. In order to solve the winner determination problem (WDP) in the combinatorial double auction, a cloud resource combinatorial double auction algorithm based on genetic algorithm and simulated annealing algorithm is proposed. Simulation results reveal that the algorithm combines genetic algorithm with simulated annealing algorithm (SAGA) outperforms genetic algorithm on fitness value and stability, and as the number of bidders increase, the solution have higher fitness value can be obtained.

In recent years, the auction has been widely applied in wireless communications for spectrum allocation. In this paper, we investigate the online spectrum double auction problem and propose a Profit concerning and Truthful Online spectrum double auction Mechanism (PTOM). Different from most previous works, we consider the dynamic arrival of primary users (PUs) and secondary users (SUs) and allow SUs to request distinct time slots for using the spectrum. By introducing the priority bid, we capture the online and location associated feature to improve the spectrum utility. Based on the priority bid, we design an efficient admission and pricing rule to improve the auctioneer’s profit. Theoretical analyses are provided to prove that our mechanism has nice economic properties including individual rationality, budget-balance and resistance of time-based and value-based cheating.

One of the recent focus within the auction field has been multi-attribute auctions where buyer is not restricted to selecting the best option only by price but also other attributes. Due to the increase in the awareness of securing private information, in this paper, we design a secure reverse multi-attribute first-price auction scheme, in which the auction is processed on the bidders' encrypted bids by multiple auctioneer servers. As a result, auctioneer servers can determine the winner without knowing the real value of bids, which let bidder's privacy would not be revealed. At last, an analysis on the privacy of bids is conducted.

In this paper, we analyse how double auction marketplaces set an effective pricing policy to determine the transaction prices for matched buyers and sellers. We analyse this problem by considering continuous privately known trader types. Furthermore, we consider two typical pricing policies: equilibrium k pricing policy and discriminatory k pricing policy. We firstly investigate how to determine the transaction prices to reach the maximal allocative efficiency in an isolated marketplace when the traders adopt Bayes-Nash equilibrium bidding strategies. We find that when the marketplace adopts discriminatory k pricing policy, the maximal allocative efficiency is reached by setting k = 0.41 or 0.59. We find that equilibrium k pricing policy provides higher allocative efficiency than discriminatory k pricing policy. We further discuss how different pricing policies can affect traders' Bayes-Nash equilibrium bidding strategies. Furthermore, we extend the analysis to the setting with two marketplaces competing against each other to attract traders. We find that the marketplace using equilibrium k pricing policy is more likely to beat the marketplace using discriminatory k pricing policy, where all traders converge to the marketplace using equilibrium k pricing policy in Bayes-Nash equilibrium. Our analysis can provide meaningful insights for designing an effective pricing policy.

In this chapter, we will introduce static homogeneous spectrum auction, first in forward auction format, then in double auction format. The key of the auction design is to leverage spectrum reusability while guaranteeing economic properties.

This paper presents the design and simulation of direct exchange mechanisms for pricing European options. It extends McAfee’s single-unit double auction to multi-unit format, and then applies it for pricing options through aggregating agent predictions of future asset prices. We will also propose the design of a combinatorial exchange for the simulation of agents using option trading strategies. We present several option trading strategies that are commonly used in real option markets to minimise the risk of future loss, and assume that agents can submit them as a combinatorial bid to the market maker. We provide simulation results for proposed mechanisms, and compare them with existing Black-Scholes model mostly used for option pricing. The simulation also tests the effect of supply and demand changes on option prices. It also takes into account agents with different implied volatility. We also observe how option prices are affected by the agents’ choices of option trading strategies.

Auction has been regarded as one of the promising methods for the scarce resources allocation due to its fairness. Thus, spectrum auction is an efficient way to allocate licensed spectrum to new demanders for mitigating the spectrum scarcity. Most of the existing studies assume that the spectrum resources are homogeneous. However, spectrums with different frequencies are intrinsically heterogeneous due to their different licensed areas and interference ranges. In this paper, we concentrate on the heterogeneity of spectrum resources and propose a strategyproof double auction mechanism STRUCTURE. The STRUCTURE assumes that all the buyers are selfish and rational, and they will submit their bids for each interested spectrum. To achieve the strategyproofness, many existing double spectrum auction mechanisms adopt the bid-independent methods to construct buyer groups, which may cause unfairness for the buyers with high bid values. To tackle this, we turn to choose a bid-related buyer group construction algorithm, which is more suitable for the laws of market and can further avoid the collusion between buyers. After that, we propose a collusion-free allocation mechanism and a bid-independent payment mechanism to ensure the strategyproofness for both buyers and sellers. Simulation results show that the proposed mechanism significantly improves the spectrum utilization with low running time. Furthermore, we also find that the buyers with higher bid values have a higher winning ratio than the buyers with low bids in the STRUCTURE.

In the secondary spectrum market, more and more primary users (PUs) release their idle spectrum to secondary users (SUs). While some of the existing auction mechanisms are truthful, few of them emphasize achieving a high usage rate. Even the SUs get the channel they require, the spectrum resource is still wasted in the spare time. In this paper, we propose a Reusable Truthful Double Auction (RTDA) mechanism for spectrum management, which considers temporal reuse and improve the usage rate significantly. Mathematical inference and game theory is used to prove that RTDA is economic-robust. The simulation results show that RTDA significantly improves the spectrum usage rate. In certain scenario, the usage rate can reach up to \(100\%\).

In this paper we investigate the possibility of spontaneous segregation into groups of traders that have to choose among several markets. Even in the simplest case of two markets and Zero Intelligence traders, we are able to observe segregation effects below a critical value Tc of the temperature T; the latter regulates how strongly traders bias their decisions towards choices with large accumulated scores. It is notable that segregation occurs even though the traders are statistically homogeneous. Traders can in principle change their loyalty to a market, but the relevant persistence times become long below Tc.

In this chapter, we consider the problem of redistribution of spectrums via a double auction and propose truthful auctions mechanism for TV channels.