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In this paper, we have proposed a completely fair energy scheduling mechanism for a smart distributed multi-microgrid system which guarantees that the energy is scheduled fairly among all the buyer microgrids (MGs). It is a non-priority based starvation free mechanism as all the buyers are treated equal and every buyer MG gets at least some portion of its energy requirement fulfilled through the internal trading. The energy scheduling is done in two phases: during phase I, energy is equally distributed among all the buyer MGs. Because of this equal distribution of energy, the buyer MGs are benefited as they are not required to purchase their entire energy requirement from utility grid at higher price. During phase II, Vickrey auction scheme is used to schedule the remaining energy amongst the buyer MGs whose energy requirements were not fulfilled during the first phase. We have evaluated the proposed mechanism for a sample instance of microgrid network by simulating the environment. The simulation demonstrates that using the proposed mechanism the total energy buying cost for all buyer MGs is reduced greatly compared with the schemes where only auctioning is used for internal trading.

The popularity of open ascending auctions is often attributed to the fact that openly observable bidding allows to aggregate dispersed information. Another reason behind the frequent utilization of open auction formats may be that they activate revenue enhancing biases. In an experiment, we compare three auctions that differ in how much information is revealed and in the potential activation of behavioral biases: (i) the ascending Vickrey auction, a closed format; and two open formats, (ii) the Japanese–English auction, and (iii) the Oral Outcry auction. Even though bidders react to information conveyed in others' bids, information aggregation fails in both open formats. In contrast, the Oral Outcry raises higher revenue than the other two formats by stimulating bidders to submit unprofitable jump bids and triggering a quasi‐endowment effect.

Problem definition : We consider a setting where online advertisers seek to acquire impressions from an advertising exchange through a multitier network of intermediaries, and study the mechanisms offered by the ad exchange and intermediaries when the advertisers’ values are private. Academic/practical relevance : As opposed to traditional manufacturer/retailer settings, intermediaries in display advertising auction off contingent goods that they purchase only if downstream buyers signal interest. This motivates our study of how intermediaries should bid on behalf of their customers in the mechanism of an upstream intermediary and how the structure of the intermediation network affects the profits of its participants. Methodology : We provide a game-theoretic model to study the mechanisms offered by the ad exchange and intermediaries within a practically relevant class of mechanisms. Results : We characterize a subgame perfect equilibrium of the game among the intermediaries and the seller, and show that the equilibrium mechanisms have a simple and appealing structure: intermediaries bid the virtual value associated with the maximum downstream report in the upstream intermediary’s mechanism, whenever this quantity is positive. Managerial implications : We show that economic incentives are not necessarily aligned along the network and that the position in the intermediation network has a significant impact on the profits of the intermediaries. That is, when advertisers’ value distribution has a “light tail,” upstream intermediaries profit more (relative to downstream ones). Moreover, as the tail of the advertisers’ value distribution gets heavier, downstream intermediaries profit more, and their profits eventually exceed those of upstream ones. In addition, we show that a horizontal merger may not be profitable for intermediaries, and we analyze the impact of market size on the profits of intermediaries.

I study auctions in which firms bid for licenses that reduce their marginal costs in a post-auction downstream market. When there are three or more firms, I show that the Vickrey--Clarke--Groves (VCG) auction maximizes consumer surplus in dominant strategies if and only if it maximizes producer surplus in dominant strategies. With two firms, the effect on consumer surplus is ambiguous. When the VCG auction does not maximize consumer surplus, I show that consumer surplus can be maximized by adding the right caps, i.e., restricting the number of licenses a bidder can win. This might lower producer surplus.

Auction designs have recently been adopted for static and dynamic resource provisioning in IaaS clouds, such as Microsoft Azure and Amazon EC2. However, the existing mechanisms are mostly restricted to simple auctions, single-objective, offline setting, one-sided interactions either among cloud users or cloud service providers (CSPs), and possible misreports of cloud user’s private information. This paper proposes a more realistic scenario of online auctioning for IaaS clouds, with the unique characteristics of elasticity for time-varying arrival of cloud user requests under the time-based server maintenance in cloud data centers. We propose an online truthful double auction technique for balancing the multi-objective trade-offs between energy, revenue, and performance in IaaS clouds, consisting of a weighted bipartite matching based winning-bid determination algorithm for resource allocation and a Vickrey–Clarke–Groves (VCG) driven algorithm for payment calculation of winning bids. Through rigorous theoretical analysis and extensive trace-driven simulation studies exploiting Google cluster workload traces, we demonstrate that our mechanism significantly improves the performance while promising truthfulness, heterogeneity, economic efficiency, individual rationality, and has a polynomial-time computational complexity.

We study information aggregation when n bidders choose, based on their private information, between two concurrent common-value auctions. There are k(s) identical objects on sale through a uniform-price auction in market s and there are an additional k(r) objects on auction in market r, which is identical to market s except for a positive reserve price. The reserve price in market r implies that information is not aggregated in this market. Moreover, if the object-to-bidder ratio in market s exceeds a certain cutoff, then information is not aggregated in market s either. Conversely, if the object-to-bidder ratio is less than this cutoff, then information is aggregated in market s as the market grows arbitrarily large. Our results demonstrate how frictions in one market can disrupt information aggregation in a linked, frictionless market because of the pattern of market selection by imperfectly informed bidders., European Research Council (ERC)

PurposeBiofortification of staple crops is a promising strategy to alleviate micronutrient deficiencies in rural populations of the developing world. The possibility to sell biofortified crops at “a good market price” plays a vital role for the acceptance by smallholder farmers. This study is therefore focused on non-farming consumers' willingness to pay (WTP) for biofortified crops.Design/methodology/approachSpecifically, we elicited non-farming consumers' WTP a premium for the improved iron content (+30% iron) in a 1kg finger millet bag using a 2nd price Vickrey auction with six auction rounds and one health- and one process-related information treatment. Due to multiple bids per subject, premiums were analyzed using a linear mixed-effects model, controlling for market feedback and auction round.FindingsDespite more than half of the respondents being skeptical toward new crop varieties, the acceptance rate was very high (98% with a WTP above zero). The average premium amounted to 27% and could be significantly increased with the provision of health-related information. In contrast, information about the breeding method was ineffective. The WTP was significantly higher for higher income and lower for higher age, education and skepticism toward new crop varieties and increased with increasing rounds.Research limitations/implicationsOur results suggest that non-farming consumers are willing to pay “a good market price” for iron-biofortified finger millet. Our analysis also confirms the importance of health-related information for raising consumers' WTP. This information supports the further development and introduction of biofortified crops to alleviate micronutrient malnutrition.Originality/valueThis study adds to the still limited literature on consumers' WTP for iron-biofortified crops in India, focusing on non-farming consumers to assess the price such crops can achieve on the market.

Behavioral biases are more pronounced for individuals with lower cognitive abilities. This paper examines what connection if any there is between cognitive ability and bidding strategy in second price auctions. Despite truthful revelation being a weakly dominant strategy, previous experiments have consistently observed overbidding, which makes use of such auctions for inferring homegrown value problematic. Examining the effect of cognitive ability is important as it may help identify when one can reliably recover values from bids. The results indicate that more cognitively able subjects behave in closer accordance with theory, and that cognitive ability partially explains heterogeneity in bidding behavior.

Internet service providers are offering shared data plans where multiple users may buy and sell their overage data in a secondary market managed by the ISP. We propose a game-theoretic approach to a software-defined network for modeling this wireless data exchange market: a fully connected, non-cooperative network. We identify and define the rules for the underlying progressive second price (PSP) auction for the respective network and market structure. We allow for a single degree of statistical freedom—the reserve price—and show that the secondary data exchange market allows for greater flexibility in the acquisition decision making of mechanism design. We have designed a framework to optimize the strategy space using the elasticity of supply and demand. Wireless users are modeled as a distribution of buyers and sellers with normal incentives. Our derivation of a buyer-response strategy for wireless users based on second price market dynamics leads us to prove the existence of a balanced pricing scheme. We examine shifts in the market price function and prove that our network upholds the desired properties for optimization with respect to software-defined networks and prove the existence of a Nash equilibrium in the overlying non-cooperative game.

We characterize revenue maximizing mechanisms in a common value environment where the value of the object is equal to the highest of bidders’ independent signals. If the object is optimally sold with probability one, then the optimal mechanism is simply a posted price, with the highest price such that every type of every bidder is willing to buy the object. A sufficient condition for the posted price to be optimal among all mechanisms is that there is at least one potential bidder who is omitted from the auction. If the object is optimally sold with probability less than one, then optimal mechanisms skew the allocation towards bidders with lower signals. This can be implemented via a modified Vickrey auction, where there is a random reserve price for just the high bidder. The resulting allocation induces a “winner's blessing,” whereby the expected value conditional on winning is higher than the unconditional expectation. By contrast, standard auctions that allocate to the bidder with the highest signal (e.g., the first-price, second-price or English auctions) deliver lower revenue because of the winner's curse generated by the allocation rule. Our qualitative results extend to more general common value environments where the winner's curse is large.

I introduce an empirical auction model where in addition to the private value that each bidder receives upon winning the auction, losing bidders incur a negative externality that depends on the identity of the rival winner. I show how the externalities and private value distributions can be identified and estimated in such a model. I then apply the model to U.S. Forest Service timber auctions. I find that mill bidders impose significant externalities on one another of between 10% and 22% of the heterogeneous portion of their valuation. This leads the timber tracts to be misallocated in 5.2% of the auctions in my sample.

We develop a model of interdependent value auctions in which two types of bidders compete: insiders, who are perfectly informed about their value, and outsiders, who are informed only about the private component of their value. Because the bidding strategy of outsiders cannot efficiently adjust with that of insiders, the second-price auction is inefficient. The English auction has an equilibrium in which the information outsiders infer from the history of drop-out prices enables them to bid toward attaining efficiency. The presence of insiders has positive impacts on the seller's revenue. A laboratory experiment confirms key theoretical predictions, despite evidence of naive bidding.

Spatial spectrum reuse significantly enhances spectrum utilization but requires delicate design to avoid cochannel interference. Instead of focusing solely on spectrum efficiency, we consider maximizing social welfare via channel allocation. Specifically, we propose an on-demand receiver-centric mechanism with a new factor, the supply and demand relationship. The optimal channel allocation that maximizes social welfare can be achieved by the constrained Vickrey–Clarke–Groves (VCG) auction for secondary users (SUs), but with high complexity. To simplify the constrained VCG auction, we group the SUs into maximal independent groups (MIGs) using a modified Bron–Kerbosch algorithm. We prove that truthful bidding is the optimal strategy for the SUs even though they do not participate in the VCG auction for MIGs directly. Therefore, the MIGs are bidding truthfully and social welfare is maximized. We also prove that the optimal channel allocation is not unique and thus a weakly-dominant strategy for the primary user, and the VCG style pricing based on truthful bidding can be implemented by using a decision tree repeatedly. Furthermore, we approximate and simplify the optimal channel allocation with a greedy algorithm, Dijkstra's algorithm, and batch allocation. In our simulation, we compare the proposed methods and demonstrate that on-demand channel allocation increases social welfare.

We revisit the revenue comparison of standard auction formats, including first-price, second-price, and English auctions. We rank auctions according to their revenue guarantees, i.e., the greatest lower bound of revenue across all informational environments, where we hold fixed the distribution of bidders’ values. We conclude that if we restrict attention to the symmetric affiliated models of Milgrom and Weber (1982) and monotonic pure-strategy equilibria, first-price, second-price, and English auctions are revenue guarantee equivalent: they have the same revenue guarantee, which is equal to that of the first-price auction as characterized by Bergemann, Brooks, and Morris (2017). If we consider all equilibria or if we allow more general models of information, then first-price auctions have a greater revenue guarantee than all other auctions considered. (JEL D44, D83)

Received auction theory prescribes that a reserve price which maximizes expected profit should be no less than the seller's own value for the auctioned object. In contrast, a common empirical observation is that many auctions have reserve prices set below sellers' values, even at zero. This paper revisits the theory to find a potential resolution of the puzzle for second-price auctions. The main result is that an optimal reserve price may be less than the seller's value if bidders are risk averse and have interdependent values. Moreover, the resulting outcome may be arbitrarily close to that of an auction that has no reserve price, an absolute auction.

Spatial spectrum reuse enables better utilization of limited spectral resources to achieve higher system throughput. However, improving the system throughput or spectrum efficiency does not necessarily translate to the satisfaction of more secondary users (SUs) according to their demands. To improve user satisfaction, user characteristics involving the supply and demand relationship need to be considered and thus enable heterogeneous channel valuations in spatial spectrum reuse. In this paper, we design a channel transaction mechanism for non-symmetric networks and maximize user satisfaction in consideration of multi-level flexible channel valuations of the SUs. Specifically, we introduce a Vickrey–Clarke–Groves (VCG) auction, in which the participants are limited to the allowable user crowds. To facilitate the bid formation, we transform the constrained VCG auction to a step-by-step decision process. Meanwhile, the SUs in a coalition play a coalitional game with transferable utilities. We use the Shapley value to realize fair payoff distribution among the SUs in a coalition. Furthermore, we approach the optimal channel allocation via finding the longest path in a directed acyclic graph, a greedy algorithm, and batch allocation. In our simulation, we compare the low-complexity algorithms and demonstrate the efficiency of the channel transaction mechanism.

Peer to Peer (P2P) Energy trading in a lower voltage distribution system is one of the effective approaches to increase renewable energy penetration from decentralized generators (DG). In this paper, an energy auction is proposed as a marketplace and mechanism of the market design is established to ensure a fair and efficient bidding in the auction. An optimal bidding strategy is very important to the energy auction. To solve this problem, the Bayesian Game Theory is adopted as the strategy in the energy auction to enable efficient and cost-effective bidding for each buyer. This paper proposes a procedure for the energy auction to ensure that the power losses are within acceptable range. An algorithm to transform the power loss issue to become part of bidding strategy of Bayesian Game Theory. The proposed method is showed that it has maximized the utility for prosumer on a typical distribution network.

Energy storage and virtual power plant technologies have been developed and become important technical means to enhance power system stability and reduce real-time dispatching costs. In this study, the dispatching capability and dispatching cost characteristics of the virtual power plants are analysed firstly in detail, as well as the dispatching difficulties under the traditional market modes. Hence, virtual power plant real-time bidding package model and virtual auction-based real-time power market mechanism are proposed. Data-driven virtual power plant real-time packing method and bidding package model integrated virtual Vickrey–Clarke–Groves auction model are put forward. Finally, the feasibility and validity of the proposed mechanism and method are verified by case studies and result in analyses of the IEEE-30 bus test system with multiple virtual power plants, providing a scientific foundation and a practical solution to the real-time power market.

Analysis of standard auction rules when bidders are risk averse is usually carried out under the assumption that the seller is able to set an optimal reserve. The role of entry fees has been generally overlooked in that analysis. We consider bidders with constant absolute risk aversion and show that reserve price is an essential tool in the second price auction while entry fee is essential in the first price auction. Furthermore, setting a reserve price and entry fee combination optimally may change some of the rankings of the standard auctions that hold under optimal reserves. (JEL D44)

Known dynamic implementations of the Vickrey–Clarke–Groves mechanism in general private-value auction settings utilize non-linear (not additively-separable over goods) and non-anonymous (bidder-specific) prices. The need for non-linear and non-anonymous prices — a complication that is often difficult to implement in practice — arises from limiting attention to elicitation processes based on demand queries (i.e., asking bidders to report their demands at posted prices). In this paper, we relax this restriction and allow the auctioneer to supplement demand queries with marginal value queries (i.e., requests to report value differences between pairs of commodity bundles) as needed. This added flexibility enables an iterative ascending auction design that achieves efficiency despite using linear and anonymous prices.

With the emergence of big data computing and analysis, cloud computing services become more and more popular, which has recently drawn researchers’ great attentions to develop various new applications and mechanisms. In this paper, we consider the on-demand mechanism design in the infrastructure as a service (IaaS), including resource allocation and pricing issues under dynamic scenarios. Most of existing works on mechanism design assumed static and independent individual utility, while the cloud computing services are provided in a dynamic environment. To solve such problems, we start with analyzing the Google cluster-usage dataset to draw the statistical and stochastic characteristics of the IaaS consumers and providers. Based on the characteristics mined from real data, we propose a stochastic matching algorithm with Markov Decision Process (MDP), which aims at optimizing the long-term system efficiency, with its online version using Q-learning method to address the imperfect model estimation problem. We further design an efficient (EF), incentive compatible (IC), individual rational (IR) auction mechanism, which is an extension of traditional Vickrey-Clarke-Groves (VCG) mechanism. The proposed mechanism is studied under two application scenario: quality sensitive services, where unilateral MDP-VCG auction is implemented; and quality insensitive services, where MDP-VCG double auction is implemented. To verify the performance of our proposed mechanism, we conduct experiment using the Google dataset and show that the proposed MDP-based VCG auction mechanism can achieve EF, IC and IR properties simultaneously.

The standard revenue-maximizing auction discriminates against a priori stronger bidders so as to reduce their information rents. We show that such discrimination is no longer optimal when the auction’s winner may resell to another bidder, and the auctioneer has non-Bayesian uncertainty about such resale opportunities. We identify a “worst-case” resale scenario, in which bidders’ values become publicly known after the auction and losing bidders compete Bertrand-style to buy the object from the winner. With this form of resale, misallocation no longer reduces the information rents of the high-value bidder, as he could still secure the same rents by buying the object in resale. Under regularity assumptions, we show that revenue is maximized by a version of the Vickrey auction with bidder-specific reserve prices, first proposed by Ausubel and Cramton (2004). The proof of optimality involves constructing Lagrange multipliers on a double continuum of binding non-local incentive constraints.

Experimental auctions are a popular and useful tool in understanding demand for food and agricultural products. Bidding behavior often deviates from theoretical predictions in traditional Vickrey and Becker–DeGroot–Marschak (BDM) auction mechanisms. We propose and explore the bidding behavior and demand revealing properties of a hybrid first price‐Vickrey auction and a hybrid first price‐BDM mechanism. Results from a between‐sample, induced value experiment reveal that, relative to traditional mechanisms, the hybrid first price‐Vickrey auction and hybrid first price‐BDM mechanism significantly reduce participants’ likelihood of overbidding, and on average yield bids closer to true valuations. We discuss potential limitations to these hybrid mechanisms as well as implications for their use in eliciting homegrown values that are important for agribusiness and food marketing research. [EconLit citations: C8, C9, Q1, Q13]

We consider pricing schemes for matching customers and providers on double-sided markets for electronic services. While existing second-best solutions are incentive compatible, the associated payment functions are difficult to implement in real-world settings. Based on the Vickrey–Clarke–Groves (VCG) and the k -pricing mechanism, we propose two straightforward payment schemes that offer a practical alternative to the second-best solution. Our experiments provide evidence that the VCG payments fail to implement incentive compatibility. This failure is due to the interdependency of the participants’ utilities.

As a departure from much of the existing literature that aims at maximizing the expected revenue from a single auction, this research work is concerned with the design of discrete Dutch auctions to achieve the optimal balance between the selling price of the object and the auction duration so that the auctioneer's expected revenue per unit of time is as high as possible. The new objective is especially important in trading perishable products or services when multiple auctions are to be conducted by the auctioneer for different sellers during a limited period of time. Our findings indicate that the proposed discrete Dutch auction models generate higher average revenues per unit of time for the auctioneer than their counterparts without consideration of time. This is achieved by reducing the duration of each auction in exchange for more auctions to be held within the given time frame so that the small decrease in the revenues from the longer auctions is more than compensated for by the large increase in the total revenues from the shorter auctions. The numerical results show that the proposed discrete Dutch auction design becomes extremely useful when the number of bidders becomes large. It is also shown that complete knowledge about the number of bidders in the auction results in a higher auctioneer's expected revenue per unit of time.

We study a pricing and allocation problem of a seller of multiple units of a homogeneous item, and present a semi-market mechanism in the form of an iterative ascending-bid auction. The auction elicits buyers' preferences over a set of options offered by the seller, and processes them with a random-priority assignment scheme to address buyers' "fairness" expectations. The auction's termination criterion is derived from a mixed-integer programming formulation of the preference-based capacity allocation problem. We show that the random priority- and preference-based assignment policy is a universally truthful mechanism which can also achieve a Pareto-efficient Nash equilibrium. Computational results demonstrate that the auction mechanism can extract a substantial portion of the centralized system's profit, indicating its effectiveness for a seller who needs to operate under the "fairness" constraint., NA