Auction Framework Research Articles

Multi-band spectrum auction framework based on location information in cognitive radio networks

Multi-band spectrum auction framework based on location information in cognitive radio networks

Service design of consumer data intermediary for competitive individual targeting

Individual targeting, a marketing strategy that firms target individual consumers with tailored offers, is currently a widespread practice. Customer data intermediaries (CDIs) have emerged recently to help firms learn their prospective customers and launch their target marketing campaigns. This paper uses a common-value auction framework to study how a CDI designs and differentiates its information services to help two competing firms identify and target valuable customers. We characterize the firms' equilibrium target marketing strategies. The results show that the CDI serves one firm exclusively in unpromising markets where the proportion of valuable customers is relatively low, and provides both firms with differentiated services in promising markets where the proportion of valuable customers is relatively high. In addition, the CDI differentiates its services less when the proportion of valuable customers is higher.

Bid strategies of primary users in double spectrum auctions

Extensive research in recent years has shown that dynamic spectrum sharing is a promising approach to address the artificial spectrum scarcity problem by improving spectrum utilization. This new communication paradigm, however, requires a well-designed spectrum allocation mechanism. This paper designs a double spectrum auction framework that allows unlicensed secondary users to obtain selected idle spectra assigned to licensed primary users. This is a win-win game because primary users can earn extra revenue and secondary users can obtain spectra they desperately need. The competition among primary users in the auction framework is studied combining game theory with a double spectrum auction in a non-cooperative game with the Nash Equilibrium (NE) as the best solution. Primary users use the prices obtained from the NE as their bid strategies to participate in the auction. In this auction sellers and buyers bid privately and confidentially, which means that the secondary users do not actually know the price and the spectrum size offered by the primary users, then a new net utility function was developed for the primary users with an iterative algorithm to find the Nash equilibrium point. Simulations demonstrate that this design effectively improves spectrum utilization.

Security constrained optimal bidding strategy of GenCos in day ahead oligopolistic power markets: a Cournot-based model

Restructured electricity markets may provide opportunities for producers to exercise market power, maintaining prices in excess of competitive levels. In this paper a Cournot equilibrium model is proposed to obtain generation companies’ (GenCos’) optimal bidding strategies in a day ahead oligopoly market, considering elasticity of demand, market power and transmission security constraints. In order to consider network constraints, a multiperiod auction framework is addressed to simulate market clearing mechanism by means of social welfare maximization, in which the behaviors of market participants are modeled through piecewise block curves. Impact of transmission security constraints on participants’ market power is presented. A mixed integer linear programming is employed to solve the problem, resulting supply-demand satisfaction as well as market clearing prices at each hour. A novel methodology is presented for security constrained optimal bidding strategy of GenCos through introducing heuristic effective-supply curves. Subsequently, impact of GenCos’ power exertion on market characteristics and corresponding payoffs is studied. A 9-bus IEEE test system is used to implement the proposed methodology while simulation results demonstrate the effectiveness of the framework.

Spectrum auction with interference constraint for cognitive radio networks with multiple primary and secondary users

Extensive research in recent years has shown the benefits of cognitive radio technologies to improve the flexibility and efficiency of spectrum utilization. This new communication paradigm, however, requires a well-designed spectrum allocation mechanism. In this paper, we propose an auction framework for cognitive radio networks to allow unlicensed secondary users (SUs) to share the available spectrum of licensed primary users (PUs) fairly and efficiently, subject to the interference temperature constraint at each PU. To study the competition among SUs, we formulate a non-cooperative multiple-PU multiple-SU auction game and study the structure of the resulting equilibrium by solving a non-continuous two-dimensional optimization problem, including the existence, uniqueness of the equilibrium and the convergence to the equilibrium in the two auctions. A distributed algorithm is developed in which each SU updates its strategy based on local information to converge to the equilibrium. We also analyze the revenue allocation among PUs and propose an algorithm to set the prices under the guideline that the revenue of each PU should be proportional to its resource. We then extend the proposed auction framework to the more challenging scenario with free spectrum bands. We develop an algorithm based on the no-regret learning to reach a correlated equilibrium of the auction game. The proposed algorithm, which can be implemented distributedly based on local observation, is especially suited in decentralized adaptive learning environments as cognitive radio networks. Finally, through numerical experiments, we demonstrate the effectiveness of the proposed auction framework in achieving high efficiency and fairness in spectrum allocation.

Spectrum redistribution for cognitive radios using discriminatory spectrum double auction

ABSTRACTWith the reformation of spectrum policy and the development of cognitive radio, secondary users will be allowed to access spectrums licensed to primary users. Spectrum auctions can facilitate this secondary spectrum access in a market‐driven way. To design an efficient auction framework, we first study the supply and demand pressures and the competitive equilibrium of the secondary spectrum market, considering the spectrum reusability. In well‐designed auctions, competition among participants should lead to the competitive equilibrium according to the traditional economic point of view. Then, a discriminatory price spectrum double auction framework is proposed for this market. In this framework, rational participants compete with each other by using bidding prices, and their profits are guaranteed to be non‐negative. A near‐optimal heuristic algorithm is also proposed to solve the auction clearing problem of the proposed framework efficiently. Experimental results verify the efficiency of the proposed auction clearing algorithm and demonstrate that competition among secondary users and primary users can lead to the competitive equilibrium during auction iterations using the proposed auction framework. Copyright © 2011 John Wiley & Sons, Ltd.

Preference-based English reverse auctions

This paper studies English reverse auctions within a unified framework for preference-based English reverse auctions. In this context, and particularly for electronic auctions, representing and handling the buyerʼs preferences, so as to enable him/her to obtain the best possible outcome, is a major issue. Existing auction mechanisms, which are based on single or multi-attribute utility functions, are only able to represent transitive and complete preferences. It is well known, however, in the preference modeling literature that more general preference structures, allowing intransitivity and incomparability, are more appropriate to capture preferences. On the other hand, we must also consider properties on the evolution and, above all, on the outcome of any auction executed by an auction mechanism. These properties, as well as properties of non-dominance and fair competition defined for multiple criteria auctions, impose restrictions on the preference relation. This leaves room for interesting preference models to be implemented within English reverse auction mechanisms.

Spectrum Auction Framework for Access Allocation in Cognitive Radio Networks

In cognitive radio networks, there are two categories of networks on different channels: primary networks, which have high-priority access, and secondary networks, which have low-priority access. We develop an auction-based framework that allows networks to bid for primary and secondary access based on their utilities and traffic demands. The bids are used to solve the access allocation problem, which is that of selecting the primary and secondary networks on each channel either to maximize the auctioneer's revenue or to maximize the social welfare of the bidding networks, while enforcing incentive compatibility. We first consider the case when the bids of a network depend on which other networks it will share channels with. When there is only one secondary network on each channel, we design an optimal polynomial-time algorithm for the access allocation problem based on reduction to a maximum matching problem in weighted graphs. When there can be two or more secondary networks on a channel, we show that the optimal access allocation problem is NP-complete. Next, we consider the case when the bids of a network are independent of which other networks it will share channels with. We design a polynomial-time dynamic programming algorithm to optimally solve the access allocation problem when the number of possible cardinalities of the set of secondary networks on a channel is upper-bounded. Finally, we design a polynomial-time algorithm that approximates the access allocation problem within a factor of 2 when the above upper bound does not exist.

Identification and Estimation of Level-K Auctions

I develop a structural econometric framework for first-price auctions by generalizing the assumption of Bayesian Nash Equilibrium within the context of a level-k behavioral model, which nests equilibrium by allowing bidders to hold heterogeneous beliefs about opponents' bidding strategies. While behavioral heterogeneity causes identification to fail under benchmark equilibrium conditions, independence and exclusion restrictions recover identification of the joint distribution over valuations and bidder-types in heterogeneous populations. Establishing consistent maximum likelihood sieve estimation with an upper semicontinuous population log-likelihood function leads to a natural semi-nonparametric maximum likelihood estimator based on Legendre polynomials. The level-k model introduces a mixture structure to the estimation problem, requiring a generalized expectation maximization algorithm. Presenting evidence from a pilot study of vintage computer auctions, I find a high level of bidder sophistication in the field. To further apply the econometric framework, I characterize expected revenues in first price auctions with level-k bidders, establishing a partial identification result for expected revenues in unidentified models. An empirical analysis of USFS timber auctions finds that a misspecified equilibrium optimal reserve price could reduce expected revenues up to 30% relative to an unbinding reserve price.

Online auctions and generalized secretary problems

We present generalized secretary problems as a framework for online auctions. Elements, such as potential employees or customers, arrive one by one online. After observing the value derived from an element, but without knowing the values of future elements, the algorithm has to make an irrevocable decision whether to retain the element as part of a solution, or reject it. The way in which the secretary framework differs from traditional online algorithms is that the elements arrive in uniformly random order. Many natural online auction scenarios can be cast as generalized secretary problems, by imposing natural restrictions on the feasible sets. For many such settings, we present surprisingly strong constant factor guarantees on the expected value of solutions obtained by online algorithms. The framework is also easily augmented to take into account time-discounted revenue and incentive compatibility. We give an overview of recent results and future research directions.

A modular framework for iterative combinatorial auctions

We describe a modular elicitation framework for iterative combinatorial auctions. The framework includes proxy agents, each of which can adopt an individualized bidding language to represent partial value information of a bidder. The framework leverages algorithms from query learning to elicit value information via bids as the auction progresses. The approach reduces the multi-agent elicitation problem to isolated, single-agent learning problems, with competitive equilibrium prices used to facilitate auction clearing even without complete learning.

Supplier selection in dynamic competitive environments

Supplier selection is one of the most critical decisions in a supply chain. While good suppliers can contribute to the supply chain's overall performance, incorrect selection can drive the whole supply chain into disarray. In this paper, we focus on the problem of supplier selection in a manufacturing firm. We allow each supplier to compete with each other to be selected by the buyer for procurement. The competition is modelled in an auction framework as a bidding process where a supplier cannot observe immediate actions of other suppliers but has complete knowledge of their previous actions. We allow a supplier to use this knowledge in guessing other suppliers' future actions and bid accordingly. Our model enables repeated games, which can be assumed to be more flexible compared to most game theory applications in the supplier selection literature. Reinforcement learning and fictitious play are used in the auction framework to implement repeated games.

Towards real-time dynamic spectrum auctions

In this paper, we propose a low-complexity auction framework to distribute spectrum in real-time among a large number of wireless users with dynamic traffic. Our design consists of a compact and highly expressive bidding format, two pricing models to control tradeoffs between revenue and fairness, and fast auction clearing algorithms to achieve conflict-free spectrum allocations that maximize auction revenue. We develop analytical bounds on algorithm performance and complexity to verify the efficiency of the proposed approach. We also use both simulated and real deployment traces to evaluate the auction framework. We conclude that pricing models and bidding behaviors have significant impact on auction outcomes and spectrum utilization. Any efficient spectrum auction system must consider demand and spectrum availability in local regions to maximize system-wide revenue and spectrum utilization.

Silent interests and all-pay auctions

If firms compete in all-pay auctions with complete information, silent shareholdings introduce asymmetric externalities into the all-pay auction framework. If the strongest firm owns a large share in the second strongest firm, this may make the strongest firm abstain from bidding. As a consequence, equilibrium profits of both firms may increase, but the prize may be allocated less efficiently. The reverse ownership structure is also likely to increase the profits of the firms involved in the ownership relationship but without these negative efficiency effects.

A multi-agent system for supporting the electronic contracting of food grains

With increasing competition and better transportation facilities, the relationships between supply chain actors are getting more complex and individual profit margins are shrinking. Decisions at different levels of the supply chain can no longer be considered independent, since they may influence profitability across the supply chain. Information technology based solution frameworks offer a way to more effectively integrate decision-making by enabling better knowledge sharing and facilitating more transparent economic transactions. This paper proposes the use of a multi-agent system to represent and integrate the decision-making processes of various actors within the food grain supply chain. Within the context of such a system, it presents an internet-based combinatorial auction framework as a technique for implementing electronic contracting (e-contracting) of food grains. The discussion focuses on the relationship between producers and millers, and presents preliminary simulation results that demonstrate the applicability and effectiveness of software agents within the proposed auction environment.

Implementation of the revenue-maximizing auction by an ignorant seller

In a general auction framework with independent private values, we propose a game, with a simple economic interpretation, that allows to implement the revenue-maximizing auction outcome when the seller ignores the distributions of the different bidders’ valuations. In this detail-free implementation procedure, an ascending-price contest is organized and the winner volunteers a payment to the seller; this payment can then be challenged by another bidder who knows the distribution of the winner’s valuation.

Combinatorial Auctions in the Procurement of Transportation Services

Most shippers use annual auctions to procure transportation services, leading to annual contracts. By using combinatorial auctions they can reduce their operating costs while protecting carriers from winning lanes that do not fit their networks, thereby improving carriers' operations as well. Combinatorial auctions account for carriers' economies of scope, which many consider more important than economies of scale in transportation operations. Any transportation procurement procedure, however, must account for level of service and other nonprice variables, which are as important as price in determining which carrier should serve what lane. These considerations can be incorporated into the combinatorial auction framework easily and holistically. After several years of using this approach, leading shippers have adopted it, and several software providers offer the requisite software.

Intelligent Support for Sophisticated e-Commerce Services: An Agent-Based Auction Framework Modeled after the New York Stock Exchange Specialist System

The rapid development of commerce on the World Wide Web has been accompanied by the creation of new business models and customer relationships involving the use of some form of middleware. Frequently, the middleware is described as a "service." E-services are much like software agents, in that they are characterized by autonomy, perception, and intelligence and have been used primarily for search and data mining activities on the Web. A parallel trend in electronic commerce is the development of auction markets in contrast to traditional fixed posted price models. Most Web-based auction markets are structured on the "English Auction" model where a reserve price is established and bids are increased until the market is cleared.

Intelligent Support for Sophisticated e-Commerce Services: An Agent-Based Auction Framework Modeled after the New York Stock Exchange Specialist System

Intelligent Support for Sophisticated e-Commerce Services: An Agent-Based Auction Framework Modeled after the New York Stock Exchange Specialist System

Does The Option to Cancel an Order in a Double Auction Market Matter?

The double auction trading environment has become one of the most experimentally-investigated trading environments. This paper seeks is to determine whether the ability of traders to cancel orders, before their potential execution, in a double auction framework, can affect price variables and the volume of orders and transactions. Not all researchers in the experimental markets literature allow that option. The vast majority of experimental markets researchers do not report whether they allowed that option or not. Because of the non-standardized use of the cancellation rule in the experimental-markets literature, presenting evidence on the possible effect of the cancellation option will be of interest to those conducting research in an experimental-market context. Our results indicate that the option to cancel affects trading volume more than price-associated variables. We find that players use the option to cancel if the option is given to them. We also find that the number of submitted orders and the number of transactions is higher when we allow the players to cancel their orders. We present evidence that the obtained findings are not only due to budget constraints but also due to changes in the bidding behavior of the players. We do not find differences in the standard deviation of the limits of the standing submitted orders or in the price of the transactions.