An Analysis of Design Problems in Combinatorial Procurement Auctions

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

Activity rules have emerged in recent years as an important aspect of practical auction design. The role of an activity rule in an iterative auction is to suppress strategic behavior by bidders and promote simple, continual, meaningful bidding and thus, price discovery. These rules find application in the design of iterative combinatorial auctions for real world scenarios, for example in spectrum auctions, in airline landing slot auctions, and in procurement auctions. We introduce the notion of strong activity rules, which allow simple, consistent bidding strategies while precluding all behaviors that cannot be rationalized in this way. We design such a rule for auctions with budget-constrained bidders, i.e., bidders with valuations for resources that are greater than their ability to pay. Such bidders are of practical importance in many market environments, and hindered from bidding in a simple and consistent way by the commonly used revealed-preference activity rule, which is too strong in such an environment. We consider issues of complexity, and provide two useful forms of information feedback to guide bidders in meeting strong activity rules. As a special case, we derive a strong activity rule for non-budget-constrained bidders. The ultimate choice of activity rule must depend, in part, on beliefs about the types of bidders likely to participate in an auction event because one cannot have a rule that is simultaneously strong for both budget-constrained bidders and quasi-linear bidders.

Combinatorial auctions, where bidders can submit bids on bundles of items, are attractive when the bidders' valuations on bundles exhibit complementarity and/or substitutability. An important problem is that of eliciting the bidders' preferences so that they do not have to bid on all combinations. We present a new family of preference elicitation algorithms. The algorithms in this family do not rely on absolute bids, but rather on relative (differential) value information. This holds the promise to reduce revelation and communication significantly. We develop a differential-elicitation algorithm that finds the efficient allocation of items to the bidders, and as a side-effect, the Vickrey payments (which make truthful bidding incentive compatible). We also present two auction mechanisms that use differential elicitation.

In recent years, auction theory has been extensively studied and many state-of-art solutions have been proposed aiming at allocating scarce resources (e.g. spectrum resources in wireless communications). Unfortunately, most of these studies assume that the auctioneer is always trustworthy in the sealed-bid auctions, which is not always true in a more realistic scenario. On the other hand, performance guarantee, such as social efficiency maximization, is also crucial for auction mechanism design. Therefore, the goal of this work is to design a series of strategyproof and privacy preserving auction mechanisms that maximize the social efficiency. To make the designed auction model more general, we allow the bidders to express their preferences about multiple items, which is often regarded as the multi-unit auction. As computing an optimal allocation in multi-unit auction is NP-hard, we design a set of near optimal allocation mechanisms with privacy preserving separately for: (1) The auction aims at identical multi-items trading; and (2) The auction aims at distinct multi-items trading, which is also known as combinatorial auction. To the best of our knowledge, we are the first to design strategyproof multi-unit auction mechanisms with privacy preserving, which maximize the social efficiency at the same time. The evaluation results corroborate our theoretical analysis, and show that our proposed methods achieve low computation and communication complexity.

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.

Combinatorial auctions are multiple-item auctions in which bidders may place bids on any package (subset) of goods. This additional expressibility produces benefits that have led to combinatorial auctions becoming extremely important both in practice and in theory. In the computer science community, auction design has focused primarily on computational practicality and incentive compatibility. The latter concerns mechanisms that are resistant to bidders misrepresenting themselves via a single false identity; however, with modern forms of bid submission, such as electronic bidding, other types of cheating have become feasible. Prominent amongst them is false-name bidding; that is, bidding under pseudonyms. For example, the ubiquitous Vickrey-Clarke-Groves (VCG) mechanism is incentive compatible and produces optimal allocations, but it is not false-name-proof–bidders can increase their utility by submitting bids under multiple identifiers. Thus, there has recently been much interest in the design and analysis of false-name-proof auction mechanisms. These false-name-proof mechanisms, however, have polynomially small efficiency guarantees: they can produce allocations with very low economic efficiency/social welfare. In contrast, we show that, provided the degree to which different goods are complementary is bounded (as is the case in many important, practical auctions), the VCG mechanism gives a constant efficiency guarantee. Constant efficiency guarantees hold even at equilibria where the agents bid in a manner that is not individually rational. Thus, while an individual bidder may personally benefit greatly from making false-name bids, this will have only a small detrimental effect on the objective of the auctioneer: maximizing economic efficiency. So, from the auctioneer's viewpoint the VCG mechanism remains preferable to false-name-proof mechanisms.

Combinatorial auction, where bidders can bid on bundles of items, has been the subject of increasing interest in recent years. Although much research work has been conducted on combinatorial auctions, most has been focusing on the winner determination problem. A largely unexplored area of research in combinatorial auctions is the design of bidding strategies, in particular, those that can be used in open and dynamic markets in which market situation is generally constantly changing. Obviously, a good bidding strategy to be used in such realistic markets cannot be obtained by analytical methods, which require all market information be known before a solution can possibly be found. Machine learning based approaches are not completely appropriate, either, as an optimal strategy learned from one market generally no longer perform well when the market situation changes. In this paper, we propose a new adaptive bidding strategy for combinatorial auction-based resource allocation problem in such dynamic markets. A bidder adopting this strategy constantly perceives the market situation, and adaptively reviews and adjusts his bid determination, thus responding to the dynamic market in a timely manner. Experiment results show that agents adopting this adaptive bidding strategy perform very well, even without any prior knowledge about the market.

Iterative combinatorial auctions (ICAs) are IT-based economic mechanisms where bidders submit bundle bids in a sequence and an auctioneer computes allocations and ask prices in each auction round. The literature in this field provides equilibrium analysis for ICAs with nonlinear personalized prices under strong assumptions on bidders' strategies. Linear pricing has performed very well in the lab and in the field. In this paper, we compare three selected linear price ICA formats based on allocative efficiency and revenue distribution using different bidding strategies and bidder valuations. The goal of this research is to benchmark different ICA formats and design and analyze new auction rules for auctions with pseudodual linear prices. The multi-item and discrete nature of linear price iterative combinatorial auctions and the complex price calculation schemes defy much of the traditional game theoretical analysis in this field. Computational methods can be of great help in exploring potential auction designs and analyzing the virtues of various design options. In our simulations, we found that ICA designs with linear prices performed very well for different valuation models even in cases of high synergies among the valuations. There were, however, significant differences in efficiency and in the revenue distributions of the three ICA formats. Heuristic bidding strategies using only a few of the best bundles also led to high levels of efficiency. We have also identified a number of auction rules for ask price calculation and auction termination that have shown to perform very well in the simulations.

Combinatorial procurement auctions are increasingly being employed in the private and public sector as an alternative to simultaneous single contract auctions. This mechanism has the advantage that it enables suppliers to express synergies across bundles of public contracts. This mitigates the exposure problem and also has the potential to both lower the price paid by the procuring authority and to enhance efficiency. This paper provides stylized facts of recently performed combinatorial public procurements in various markets in Sweden.

Combinatorial auctions are a common abstraction of many complex resource allocation problems: A large number of items (goods, resources) should be assigned to a number of agents (bidders) with different valuations on bundles of items. They are the central representative problem for the field of algorithmic mechanism design. In this field, algorithmic problems are studied in a game theoretic setting in which the input of the algorithm is not publicly known but distributed among a set of selfish agents which would possibly lie about their private information if this would give an advantage to them. A mechanism is called incentive compatible or truthful if it allocates the goods and sets payments for the bidders in such a way that it is a dominant strategy for each bidder to report his/her valuations for different bundles of items in a truthful manner.

Many auctions involve the sale of a variety of distinct assets. Examples are airport time slots, delivery routes, network routing, and furniture. Because of complementarities or substitution effects between the different assets, bidders have preferences not just for particular items but for sets of items. For this reason, economic efficiency is enhanced if bidders are allowed to bid on bundles or combinations of different assets. This paper surveys the state of knowledge about the design of combinatorial auctions and presents some new insights. Periodic updates of portions of this survey will be posted to this journal's Online Supplements web page at http://joc.pubs.informs.org/OnlineSupplements.html

In combinatorial auctions, prospective buyers bid on bundles of items for sale, including but not limited to singleton bundles. The bid price given by a buyer on a particular bundle reflects his/her perceived utility of the bundle of items as a whole. After collecting all the bids, the auctioneer determines the revenue-maximizing assignment of winning bidders to bundles subject to nonoverlapping of bundles. To accomplish this, the auctioneer needs to solve a winner determination problem (WDP). The exactly same way of thinking can be taken to the context of min-sum scheduling, where jobs can be viewed as bidders who bid on bundles of discrete time periods on machines. Particular problems often permit only a subset of bundles. By putting appropriate restrictions on the collection of permissible bundles, we can derive from the WDP, various integer programming (IP) formulations for nonpreemptive as well as preemptive min-sum scheduling problems. We thus obtain the well-known time-indexed IP formulation in the nonpreemptive case, and further, a new strong IP formulation in the preemptive case.

Combinatorial auctions (CAs) where bidders can bid on bundles of items can be very desirable market mechanisms when the items sold exhibit complementarity and/or substitutability, so the bidder's valuations for bundles are not additive. However, in a basic CA, the bidders may need to bid on exponentially many bundles, leading to difficulties in determining those valuations, undesirable information revelation, and unnecessary communication. In this paper we present a design of an auctioneer agent that uses topological structure inherent in the problem to reduce the amount of information that it needs from the bidders. An analysis tool is presented as well as data structures for storing and optimally assimilating the information received from the bidders. Using this information, the agent then narrows down the set of desirable (welfare-maximizing or Pareto-efficient) allocations, and decides which questions to ask next. Several algorithms are presented that ask the bidders for value, order, and rank information. A method is presented for making the elicitor incentive compatible.

Several auctions have been proposed and applied to perform contract negotiation and resource allocation in reservation-based networks. The methods proposed by these works perceive resources as single items with multiple units and place importance on a limited efficiency inside each node. However, as a user evaluates resources not individually, but rather as a whole set of required resources, the economical efficiency of the overall network cannot be achieved by these methods. To solve this problem, we propose a bandwidth allocation system using GVA (Generalised Vickrey Auction). Network resources, which are composed of many links at various bandwidths, are regarded by the proposed method as multiple items with multiple units. We describe how to apply GVA protocol to bandwidth allocations among multiple users. We investigate algorithmic and accounting problems inside multiple nodes using an end-to-end approach. We evaluate the proposed method's performance from various viewpoints: the utilisation of resources, profits of the telecommunications carriers, users' utility and users' satisfaction. We show that, by adopting GVA, the total utility of users can be maximised and the revenues of networks can also be improved.