False-Name Bidding and Economic Efficiency in Combinatorial Auctions

This paper presents a method for discovering and detecting shill bids in combinatorial auctions. The Vickrey-Clarke-Groves Mechanism is one of the most important combinatorial auctions because it can satisfy the strategy-proof property, individual rationality, and Pareto efficiency, that is, it is the only mechanism that simultaneously satisfies these properties. As Yokoo et al. pointed out, false-name bids and shill bids pose an emerging problem for auctions, since on the Internet it is easy to establish different e-mail addresses and accounts for auction sites. Yokoo et al. proved that VCG cannot satisfy the false-name-proof property, and they also proved that no auction protocol can satisfy all three of the above properties and the false-name proof property simultaneously. Their approach concentrates on designing a new mechanism that has desirable properties, but this is quite complicated. As a new approach against shill-bids, in this paper, we design a mechanism that utilizes VCG and an algorithm for finding potential shill bids. Our mechanism is quite simple compared with Yokoo's approaches [11][12][13]. Our mechanism can judge whether there might be a shill bid from the results of the VCG procedure. We prove a theorem stating that shill bidders cannot increase their utilities unless all shill bidders win in the auction. Based on this theorem, our proposed mechanism compares the agents' utilities in a conventional auction with those in an auction where a shill bidder does not join in the auction. When these agents' utilities are different between the above cases, such agents might be shill bidders. Then, our mechanism allocates items to the shill bidders as a group from the set of items obtained through successful bids by the agent in the conventional auction. This process prevents shill bidders from increasing unfair profits. Furthermore, even though shill bidders participate in the auction, the seller's profit does not decrease using our proposed method. Thus, our mechanism detects shill bids when it only detects the possibility of shill bids. Our proposed method has the following three key advantages. First, we propose a method to detect shill bidders by comparison between bidders utilities. Our method is superior than existing complex mechanisms in the point of view of generalization and wide-use, because our auction mechanism employs only VCG. Second, even though there are shill bidders in an auction, incentive compatibility property is preserved using our mechanism. Finally, the schemer, in our mechanism, does never have incentive to make shill bidders. The schemer's utility does not increase in our mechanism even though a schemer make shill bidders. Namely, not to make shill bidders is dominant strategy for the schemer.

The effect of false-name bids in combinatorial auctions: new fraud in internet auctions

Traditional auction mechanisms support price negotiations on a single item. The Internet allows for the exchange of much more complex offers in real-time. This is one of the reasons for much research on multidimensional auction mechanisms allowing negotiations on multiple items, multiple units, or multiple attributes of an item, as they can be regularly found in procurement. Combinatorial auctions, for example, enable suppliers to submit bids on bundles of items. A number of laboratory experiments has shown high allocative efficiency in markets with economies of scope. For suppliers it is easier to express cost savings due to bundling (e. g., decreased transportation or production costs). This can lead to significant savings in total cost of the procurement manager. Procurement negotiations exhibit a number of particularities: It is often necessary to consider qualitative attributes or volume discounts in bundle bids. These complex bid types have not been sufficiently analyzed. The winner determination problem requires the consideration of a number of additional business constraints, such as limits on the spend on a particular supplier or the number of suppliers. Iterative combinatorial auctions have a number of advantages in practical applications, but they also lead to new problems in the determination of ask prices. In this paper, we will discuss fundamental problems in the design of combinatorial auctions and the particularities of procurement applications.Reprint of an article from WIRTSCHAFTSINFORMATIK 47(2)2005:126–134.

Proxy bidding has been proposed for combinatorial auctions as a means to speed up the auctions, to simplify the user interface, and to limit strategic behavior. The only previously known solution method for proxy bidding in combinatorial auctions requires the auctioneer to run the auction with myopic bidders to determine the outcome. In this paper we present a radically different approach that computes the bidders' allocation of their attention across the bundles only at the points at which they change their bidding patterns. This algorithm has several advantages over alternatives, including that it computes exact solutions and is invariant to the magnitude of the bids. We present a general framework and apply it to Ausubel and Milgrom's APA mechanism and our own simple combinatorial proxy auction. We present an example in which the approach is applied to a multistage proxy auction, and report on some preliminary computational results.

We develop an auction model for the case of interdependent values and multidimensional signals in which agents' signals are correlated. We provide conditions under which a modification of the Vickrey auction which includes payments to the bidders will result in an ex post efficient outcome. Furthermore, we provide a definition of informational size such that the necessary payments to bidders will be arbitrarily small if agents are sufficiently informationally small. The efficiency of market processes has been a central concern in economics since its inception. Auction mechanisms constitute a very important class of market processes, yet the analysis of auctions has typically focused on their revenue generating properties rather than their efficiency properties. This is partly due to the fact that, for many of the problems typically studied, efficiency is trivial. When bidders have private values, a standard Vickrey auction guarantees that the object will be sold to the buyer with the highest value for the object. In the case of pure common values-that is, when all buyers have the same value for the object-any outcome that with probability one assigns the object to some bidder will be efficient. The intermediate case in which bidders' values are not identical but may depend on other bidders' signals is more problematic. When bidders' values are interdependent in this way, any single bidder's value may depend on the information of other agents and, hence, he may not even know his own value. It is not clear what it would mean for an agent to bid his true value, even before we ask if it is optimal for him to do so. Several papers have studied efficient auctions with interdependent values and independent types.1 While this is a natural place to begin, the independence assumption is not compelling for many problems. A prototypical problem is one in which an object is to be sold, and individual bidders have private information about the object (say the quantity or quality of oil in a tract to be sold) that affects other agents' values for the object. When bidders' types include information about objective characteristics of the object being sold, their types will typically not be independent. When agents' types are statistically dependent, we show that there exist efficient auction mechanisms for interdependent value auction problems that are essentially Vickrey auctions augmented by payments to (not from) the agents. Most importantly, we link the

Auctions where landholders submit conservation projects for consideration in return for payment have recently gained popularity. Flexible versions of these, combinatorial auctions, which allow for the simultaneous submission of bids on different combinations of projects, have recently been tested for conservation purposes. Combinatorial auctions allow bidders to exploit synergies in the execution of multiple conservation activities while the auction mechanism stimulates competition among potential participants, enabling the auctioneer (or government) to secure a cost-effective set of conservation projects. Combinatorial auctions have the potential to improve outcomes beyond what is possible with the simple auctions explored in trials over the last decade in many parts of Australia. This article reviews the state of the art in the design of combinatorial auctions with a particular focus on conservation auctions. Key design issues identified are the problem of determining the winning bid, pricing formats, and iterative bidding formats where bidders are able to revise their bids before a final selection is made.

Market Design for Renewable Energy Auctions: An Analysis of Alternative Auction Formats

Diffusion auction is a new model in auction design. It can incentivize the buyers who have already joined in the auction to further diffuse the sale information to others via social relations, whereby both the seller's revenue and the social welfare can be improved. Diffusion auctions are essentially non-typical multidimensional mechanism design problems and agents' social relations are complicatedly involved with their bids. In such auctions, incentive-compatibility (IC) means it is best for every agent to honestly report her valuation and fully diffuse the sale information to all her neighbors. Existing work identified some specific mechanisms for diffusion auctions, while a general theory characterizing all incentive-compatible diffusion auctions is still missing. In this work, we identify a sufficient and necessary condition for all dominant-strategy incentive-compatible (DSIC) diffusion auctions. We formulate the monotonic allocation policies in such multidimensional problems and show that any monotonic allocation policy can be implemented in a DSIC diffusion auction mechanism. Moreover, given any monotonic allocation policy, we obtain the optimal payment policy to maximize the seller's revenue.

Shared parking problem: A novel truthful double auction mechanism approach

A well-known problem in combinatorial auctions (CAs) is that the value space grows exponentially in the number of goods, which often puts a large burden on the bidders and on the auctioneer. In this paper, we introduce a new design paradigm for CAs based on machine learning (ML). Bidders report their values (bids) to a proxy agent by answering a small number of value queries. The proxy agent then uses an ML algorithm to generalize from those bids to the whole value space, and the efficient allocation is computed based on the generalized valuations. We introduce the concept of "probably approximate efficiency (PAE)" to measure the efficiency of the new ML-based auctions, and we formally show how the generelizability of an ML algorithm relates to the efficiency loss incurred by the corresponding ML-based auction. To instantiate our paradigm, we use support vector regression (SVR) as our ML algorithm, which enables us to keep the winner determination problem of the CA tractable. Different parameters of the SVR algorithm allow us to trade off the expressiveness, economic efficiency, and computational efficiency of the CA. Finally, we demonstrate experimentally that, even with a small number of bids, our ML-based auctions are highly efficient with high probability.

The question of transparency has become a key point of contention between buyers and sellers of display advertising space: ads are allocated via complex, black-box auction systems whose mechanics can be difficult to model let alone optimize against. Motivated by this concern, this paper takes the perspective of a single advertiser and develops statistical tests to confirm whether an underlying auction mechanism is dynamically incentive compatible (IC), so that truthful bidding in each individual auction and across time is an optimal strategy. The most general notion of dynamic-IC presumes that the seller knows how buyers discount future surplus, which is questionable in practice. We characterize dynamic mechanisms that are dynamic-IC for all possible discounting factors according to two intuitive conditions: the mechanism should be IC at each stage in the usual sense, and expected present utility (under truthful bidding) should be independent of past bids. The conditions motivate two separate experiments based on bid perturbations that can be run simultaneously on the same impression traffic. We provide a novel statistical test of stage-IC along with a test for utility-independence that can detect lags in how the seller uses past bid information. We evaluate our tests on display ad data from a major ad exchange and show how they can accurately uncover evidence of first- or second-price auctions coupled with dynamic reserve prices, among other types of dynamic mechanisms.

Algorithm for optimal winner determination in combinatorial auctions

Recent advances in Fourier analysis have brought new tools to efficiently represent and learn set functions. In this paper, we bring the power of Fourier analysis to the design of combinatorial auctions (CAs). The key idea is to approximate bidders' value functions using Fourier-sparse set functions, which can be computed using a relatively small number of queries. Since this number is still too large for practical CAs, we propose a new hybrid design: we first use neural networks (NNs) to learn bidders’ values and then apply Fourier analysis to the learned representations. On a technical level, we formulate a Fourier transform-based winner determination problem and derive its mixed integer program formulation. Based on this, we devise an iterative CA that asks Fourier-based queries. We experimentally show that our hybrid ICA achieves higher efficiency than prior auction designs, leads to a fairer distribution of social welfare, and significantly reduces runtime. With this paper, we are the first to leverage Fourier analysis in CA design and lay the foundation for future work in this area. Our code is available on GitHub: https://github.com/marketdesignresearch/FA-based-ICAs.

Trustful double auction design for Peer-to-Peer energy trading between interconnected micro-grids with supply–demand imbalance

In the traditional double auction market, the number of achievable tradable pairs are usually limited by the market size. To solve this problem, we propose new double auction mechanisms based on social networks to realize market expansion. In this paper, we examine the double auction market, which consists of a group of sellers and a group of buyers, where the sellers provide the same type of items, and the buyers respectively purchase one from them. In addition, every buyer can invite other potential buyers to enter the market through social networks. The goal of this paper is to propose mechanisms such that they can encourage buyers already in the market to invite other potential buyers to join the auction through social networks, and achieve an effective allocation of merchandises and increase profits for sellers, which cannot be achieved under the existing double auction mechanism. We found that the extended McAfee auction mechanism cannot motivate existing buyers to invite other potential ones, while the traditional Vickrey-Clark-Groves (VCG) mechanism cannot guarantee to break even. To solve these problems, we propose two mechanisms, which are called information network auction (INA) mechanism and double network auction (DNA) mechanism. Both of these mechanisms can encourage old buyers in the market to invite new potential customers to participate. Moreover, INA focuses on achieving more effective allocation and DNA focuses on ensuring a balance between income and expenditure.