Truthful Bidding Research Articles

Display Advertising Auctions with Arbitrage

Online display advertising exchanges connect Web publishers with advertisers seeking to place ads. In many cases, the advertiser obtains value from an ad impression (a viewing by a user) only if it is clicked, and frequently advertisers prefer to pay contingent on this occurring. But at the same time, many publishers demand payment independent of clicks. Arbitragers with good estimates of click-probabilities can resolve this conflict by absorbing the risk and acting as an intermediary, paying the publisher on allocation and being paid only if a click occurs. This article examines the incentives of advertisers and arbitragers and contributes an efficient mechanism with truthful bidding by the advertisers and truthful reporting of click predictions by arbitragers as dominant strategies while, given that a hazard rate condition is satisfied, yielding increased revenue to the publisher. We provide empirical evidence based on bid data from Yahoo's Right Media Exchange suggesting that the mechanism would increase revenue in practice.

Online Electricity Cost Saving Algorithms for Co-Location Data Centers

This work studies the online electricity cost minimization problem at a co-location data center. A co-location data center serves multiple tenants who rent the physical infrastructure within the data center to run their respective cloud computing services. Consequently, the co-location operator has no direct control over power consumption of its tenants, and an efficient mechanism is desired for eliciting desirable consumption patterns from the co-location tenants. Electricity billing faced by a data center is nowadays based on both the total volume consumed and the peak consumption rate. This leads to an interesting new combinatorial optimization structure on the electricity cost optimization problem, which also exhibits an online nature due to the definition of peak consumption. We model and solve the problem through two approaches: the pricing approach and the auction approach. For the former, we design an offline 2-approximation algorithm as well as an online algorithm with a small competitive ratio in most practical settings. For the latter, we design an efficient (2+ c )-competitive online algorithm, where c is a system dependent parameter close to 1.49, and then convert it into an efficient mechanism that executes in an online fashion, runs in polynomial time, and guarantees truthful bidding and (2+2 c )-competitive in social cost.

Distributed Queue-Aware Relay Node Selection for Cooperative Wireless Networks via Vickrey Auction Game

Queue-aware relay selection for the uplink of a user cooperation multiple access wireless network is investigated. To achieve better overall quality-of-service (QoS), the queue states of the relay buffers are considered in the relay selection process. A distributed relay selection mechanism based on a Vickrey auction game is proposed. It is shown that with a strictly positive reserve price, the unique Nash equilibrium of the auction game achieved by a truthful bidding strategy is the best relay response under the queue length constraint. Numerical results show that a significant improvement in QoS provisioning is achieved using the proposed auction-based queue-aware relay selection, compared to fixed relaying and channel-aware relay selection, especially when the relays have high average packet arrival rates. Given a target system packet blocking rate, the proposed scheme helps to better maintain relay buffer queue stability.

Truthful Mechanisms with Implicit Payment Computation

It is widely believed that computing payments needed to induce truthful bidding is somehow harder than simply computing the allocation. We show that the opposite is true: creating a randomized truthful mechanism is essentially as easy as a single call to a monotone allocation rule. Our main result is a general procedure to take a monotone allocation rule for a single-parameter domain and transform it (via a black-box reduction) into a randomized mechanism that is truthful in expectation and individually rational for every realization. The mechanism implements the same outcome as the original allocation rule with probability arbitrarily close to 1, and requires evaluating that allocation rule only once. We also provide an extension of this result to multiparameter domains and cycle-monotone allocation rules, under mild star-convexity and nonnegativity hypotheses on the type space and allocation rule, respectively. Because our reduction is simple, versatile, and general, it has many applications to mechanism design problems in which re-evaluating the allocation rule is either burdensome or informationally impossible. Applying our result to the multiarmed bandit problem, we obtain truthful randomized mechanisms whose regret matches the information-theoretic lower bound up to logarithmic factors, even though prior work showed this is impossible for truthful deterministic mechanisms. We also present applications to offline mechanism design, showing that randomization can circumvent a communication complexity lower bound for deterministic payments computation, and that it can also be used to create truthful shortest path auctions that approximate the welfare of the VCG allocation arbitrarily well, while having the same running time complexity as Dijkstra's algorithm.

Designing Truthful Spectrum Auctions for Multi-hop Secondary Networks

Opportunistic wireless channel access granted to non-licensed users through auctions represents a promising approach for effectively distributing and utilizing the scarce wireless spectrum. A limitation of existing spectrum auction designs lies in the over-simplifying assumption that every non-licensed secondary user is a single node or single-hop network. For the first time in the literature, we propose to model non-licensed users as secondary networks (SNs), each of which comprises of a multihop network with end-to-end routing demands. We use simple examples to show that such auctions among SNs differ drastically from simple auctions among single-hop users, and previous solutions suffer from local, per-hop decision making. We first design a simple, heuristic auction that takes inter-SN interference into consideration and is truthful. We then design a randomized auction framework based on primal-dual linear optimization, which is automatically truthful and achieves a social welfare approximation ratio that matches one achieved by cooperative optimization assuming truthful bids for free. The framework relieves a spectrum auction designer from worrying about truthfulness of the auction, so that he or she can focus on social welfare maximization while assuming truthful bids for free.

Distributed Smart-Home Decision-Making in a Hierarchical Interactive Smart Grid Architecture

In this paper, we develop a comprehensive real-time interactive framework for the utility and customers in a smart grid while ensuring grid-stability and quality-of-service (QoS). First, we propose a hierarchical architecture for the utility-customer interaction consisting of sub-components of customer load prediction, renewable generation integration, power-load balancing and demand response (DR). Within this hierarchical architecture, we focus on the problem of real-time scheduling in an abstract grid model consisting of one controller and multiple customer units. A scalable solution to the real-time scheduling problem is proposed by combining solutions to two sub-problems: ( $1$ ) centralized sequential decision making at the controller to maximize an accumulated reward for the whole micro-grid and ( $2$ ) distributed auctioning among all customers based on the optimal load profile obtained by solving the first problem to coordinate their interactions. We formulate the centralized sequential decision making at the controller as a hidden mode Markov decision process (HM-MDP). Next, a Vikrey auctioning game is designed to coordinate the actions of the individual smart-homes to actually achieve the optimal solution derived by the controller under realistic gird interaction assumptions. We show that though truthful bidding is a weakly dominant strategy for all smart-homes in the auctioning game, collusive equilibria do exist and can jeopardize the effectiveness and efficiency of the trading opportunity allocation. Analysis on the structure of the Bayesian Nash equilibrium solution set shows that the Vickrey auctioning game can be made more robust against collusion by customers (anticipating distributed smart-homes) by introducing a positive reserve price. The corresponding auctioning game is then shown to converge to the unique incentive compatible truthful bidding Bayesian Nash equilibrium, without jeopardizing the auctioneer’s (microgrid controller’s) profit. The paper also explicitly discusses how this two-step solution approach can be scaled to be suitable for more complicated smart grid architectures beyond the assumed abstract model.

Reputation-Aware Continuous Double Auction

Truthful bidding is a desirable property for continuous double auctions (CDAs). Many incentive mechanisms have been proposed to elicit truthful bids. However, existing truthful CDA mechanisms often overlook the possibility that sellers may choose not to deliver the auctioned items to buyers as promised. In this situation, buyers may become unwilling to bid their true valuations in the future to compensate for their risks of being cheated, thereby rendering CDAs ineffective. In this paper, we propose a novel reputation-aware CDA (named RCDA) mechanism to consider the honesty of auction participants. It dynamically adjusts bids and asks according to the reputation of participants to reflect the risks involved in the transactions. Theoretical analysis proves that RCDA is effective in eliciting truthful bids from buyers and sellers in the presence of possible dishonest behavior from both buyers and sellers.

Efficient auctions for distributed transportation procurement

The purpose of this paper is to propose allocatively efficient auction mechanisms for the distributed transportation procurement problem (DTPP), which is generally the problem of matching demands and supplies over a transportation network. We first construct a one-sided Vickrey–Clarke–Groves (O-VCG) combinatorial auction for the DTPP where carriers are allowed to bid on bundles of lanes. The O-VCG auction minimizes the total transportation cost (i.e., allocative efficiency) and induces truthful bidding from carriers (i.e., incentive compatibility). To simplify the execution of auction, we next propose a primal–dual Vickrey (PDV) auction based on insights from the known Ausubel auctions and the primal–dual algorithm. The PDV auction is actually a multi-round descending auction that seems simple enough for bidders. The PDV auction realizes VCG payments and truthful bidding under the condition of seller-submodularity, which implies that the effect of each individual carrier is decreasing when the coalition increases. Such is the case for the DTPP in an oversupplied transportation market. The winner determination problem of O-VCG auction is solved by the proposed primal–dual algorithm when seller-submodularity holds. Finally, carriers may reveal less private information in the PDV auction due to its dynamic procedures.

Multi-attribute auctions with different types of attributes: Enacting properties in multi-attribute auctions

Multi-attribute auctions allow agents to sell and purchase goods and services taking into account more attributes than just price (e.g. service time, tolerances, qualities, etc.). In this paper we analyze attributes involved during the auction process and propose to classify them between verifiable attributes, unverifiable attributes and auctioneer provided attributes. According to this classification we present VMA2, a new Vickrey-based reverse multi-attribute auction mechanism, which takes into account the different types of attributes involved in the auction and allows the auction customization in order to suit the auctioneer needs. On the one hand, the use of auctioneer provided attributes enables the inclusion of different auction concepts, such as social welfare, trust or robustness whilst, on the other hand, the use of verifiable attributes guarantee truthful bidding. The paper exemplifies the behavior of VMA2 describing how an egalitarian allocation can be achieved. The mechanism is then tested in a simulated manufacturing environment and compared with other existing auction allocation methods.

Beyond the Last Touch: Attribution in Online Advertising

Online advertisers often utilize multiple publishers and bid in multiple auctions concurrently to deliver ads, a process which may result in externalities between publishers that impact advertising effectiveness. In conjunction with these externalities, uncertainty about consumer visit patterns may limit the advertiser's ability to optimize its campaigns.We define the attribution problem as the attempt to measure such uncertainties and correct for the externalities, and determine the characteristics of a good attribution process. Using an analytical model of an online campaign we show that publisher externalities will lead an advertiser to shift from truthful bidding in equilibrium, and that combined with uncertainty about consumer visits, its profits will be lowered. Our analysis of a common attribution method known as last-touch shows that it reduces advertiser profits compared to not using attribution at all, and that stronger advertisers suffer from a misallocation of consumer impressions due to overbidding for ads resulting from the attribution process. Our analysis of an attribution scheme based on the Shapley value shows that it will improve the profits of advertisers when conversion rates in the market are not too high.

Allocation of risk capital on an internal market

We propose an allocation process for economic risk capital using an internal sequential auction in which investment allowances are based on marginal risk contributions. Division managers have incentive to give truthful bids because of bonus payments, which are linear in the division’s profit and linked to the auction bids. With our model, the auction process reaches an equilibrium identical to the optimal allocation if division managers have no diverging interests. When division managers do have diverging preferences in terms of empire building, headquarters faces a trade-off between incurring opportunity costs for achieving a suboptimal allocation and bonus costs paid to division managers to overcome their diverging interests. However, bonus costs are partially offset by proceeds from the auction. Depending on the model parameters, total agency costs can become negative. We show that for large values of new risk capital to be allocated, headquarters can always choose a level of bonus payments so that total costs are negative.

A hybrid implementation mechanism of tradable network permits system which obviates path enumeration: An auction mechanism with day-to-day capacity control

Akamatsu (2007a,b) proposed a new dynamic traffic congestion control scheme called tradable network permits, and demonstrated its efficiency properties for general road networks. To implement tradable permit markets, this paper proposes a novel auction mechanism with capacity control. This mechanism employs an evolutionary approach to achieve a dynamic system optimal allocation of network permits in a computationally efficient manner. We prove that the proposed mechanism has the following desirable properties: (i) truthful bidding is a dominant strategy for each user on each day and (ii) the permit allocation pattern under the mechanism converges to a dynamic system optimal allocation pattern.

Effects of Minimum Bid Increment in Internet Auctions: Evidence from a Field Experiment

I study the role of minimum bid increments (MBI) in internet auctions using field experiment data. I sell identical gift cards while varying the MBI. Internet auctions have typically been viewed as second-price, implying truthful bidding. However, due to the presence of the MBI, equilibrium bidding behavior involves bid-shading. I test between truthful bidding and equilibrium bidding. Truthful bidding is rejected. Bidders conduct bid-shading in a pattern consistent with equilibrium bidding. I also report that the revenue maximizing level for the MBI is higher than zero and the eBay level is close to optimal. Moreover, a high MBI inefficiently limits entry.

A Hybrid Implementation Mechanism of Tradable Network Permits System Which Obviates Path Enumeration: An Auction Mechanism with Day-to-day Capacity Control

Akamatsu, Sato, and Nguyen (2006) and Akamatsu (2007a,b) proposed a new dynamic traffic congestion control scheme—the tradable network permit—and proved its efficiency properties for general road networks. To implement tradable permit markets successfully, this paper proposes a novel auction mechanism with capacity control. Assuming that each user makes a trip from an origin to a destination along a path in a specific time period, we design an auction mechanism that enables each user to purchase a bundle of network permits corresponding to a set of links in the user's preferred path. The proposed mechanism employs an evolutionary approach to achieve a dynamic system optimal allo- cation of network permits in a computationally efficient manner. Specifically, it is a hybrid mechanism that consistently combines an auction mechanism with a path capacity control, which are repeated on a day-to-day basis. The former phase involves selling bundles of permits, and the latter phase involves adjusting of the number of the bundles, which corresponds to the path capacities. We prove that the proposed mechanism has the following desirable properties: (i) truthful bidding is the dominant strategy for each user on each day; and (ii) the permit allocation pattern under the mechanism converges to an approximate dynamic system optimal allocation pattern in the sense that the achieved so- cial surplus reaches its maximum value when the number of users is large. Furthermore, we show that the proposed mechanism can be extended to obviate path enumeration by introducing a column generation procedure.

An Extension of Ausubel’s Auction for Heterogeneous Discrete Goods

Ausubel’s dynamic private-values auction for heterogeneous discrete goods, Ausubel (2006), yields an efficient equilibrium outcome but it is designed for a limited class of environments. If bidders’ values for bundles of goods are not integers, then Ausubel’s auction may end without allocating goods if no information on bidders’ values is available. In this paper, I extend Ausubel’s auction for heterogeneous discrete goods to real-valued quasilinear utility functions. The mechanism I propose reaches a Walrasian equilibrium price vector in finite “steps” without any additional information on bidders’ values. In the extension of Ausubel’s auction, truthful bidding constitutes an efficient equilibrium. JEL Category D44 ∗I would like to thank Ket Richter and Jan Werner for their continuous support, patience, and effort for my education. Their guidance has been enlightening. I would like to thank late Leo Hurwicz, and Andy McLennan for their contribution to my education. I benefited a lot from discussions with David Rahman and Itai Sher. I also would like to thank Pat Bajari, Tuba Inal, Ichiro Obara, Raj Singh, and the seminar participants at Central Michigan University, METU, the University of Minnesota, the Virginia Commonwealth University, Whitman College, the Games 2008, and the Society for Economic Design 2008 Conference for their valuable comments. All errors are mine. †http://hakaninal.weebly.com

Spectrum Auction Design

Spectrum auctions are used by governments to assign and price licenses for wireless communications. The standard approach is the simultaneous ascending auction, in which many related lots are auctioned simultaneously in a sequence of rounds. I analyze the strengths and weaknesses of the approach with examples from US spectrum auctions. I then present a variation-the combinatorial clock auction-which has been adopted by the UK and many other countries, which addresses many of the problems of the simultaneous ascending auction while building on its strengths. The combinatorial clock auction is a simple dynamic auction in which bidders bid on packages of lots. Most importantly, the auction allows alternative technologies that require the spectrum to be organized in different ways to compete in a technology-neutral auction. In addition, the pricing rule and information policy are carefully tailored to mitigate gaming behavior. An activity rule based on revealed preference promotes price and assignment discovery throughout the clock stage of the auction. Truthful bidding is encouraged, which simplifies bidding and improves efficiency. Experimental tests and early auctions confirm the advantages of the approach.

Do core-selecting Combinatorial Clock Auctions always lead to high efficiency? An experimental analysis of spectrum auction designs

For many years the Simultaneous Multi-Round Auction (SMRA) has been the primary auction design for spectrum sales worldwide. Recently, the core-selecting Combinatorial Clock Auction (CCA) has been used as an alternative to the SMRA in a number of countries promising strong incentives for truthful bidding and high efficiency as a result. We analyze the efficiency and auctioneer revenue of the CCA in comparison to SMRA and examine bidding behavior in both formats. The experiments are based on two value models, which resemble single- and multiband spectrum sales in the field. Such applications often allow for thousands of possible bundles. Bidders in the CCA submitted bids for only a fraction of all bundles with a positive valuation. Bundles were selected based on synergies and payoff after the primary bid rounds. As a consequence, we found efficiency of the CCA to be significantly lower than that of SMRA in the multi-band value model and auctioneer revenue of the CCA to be lower in both value models. In addition, we characterize several properties of the auction format, which result from the two-stage design and the payment and activity rules.

Strategic ignorance in a second-price auction

Suppose that bidders may publicly choose not to learn their values prior to a second-price auction with costly bidding. All equilibria with truthful bidding exhibit bidder ignorance when the number of bidders is sufficiently small. Ignorance considerations also affect the optimal reserve price.

Design of online auctions: Proxy versus non-proxy settings

Recent years have witnessed the rapid development of online auctions. Currently, some online auctions, such as eBay, introduce a proxy bidding policy, under which bidders submit their maximum bids and delegate to a proxy agent to automatically outbid other competitors for the top bidder, whereas other online auctions do not. This paper compares these two widely used auction mechanisms (proxy setting and non-proxy setting) and characterizes the equilibrium bidding behavior and the seller's expected revenue. We find the proxy auction outperforms the non-proxy auction in terms of the seller's expected revenue. This dominance result is not prone to the specific bid announcement policy, the bidder's knowledge regarding the number of bidders, the impact of traffic congestion along the bidding process, the number of items sold through the auction, and the existence of a reserve price. We further find that the proxy setting usually fails to sustain the truthful bidding as a dominant strategy equilibrium even if no minimum bid increments are adopted, and the possibility of a low-valuation-bidder dilemma where the low-valuation bidders could be better off if all bidders collude to bid at the last minute. We also discuss the dramatically different equilibrium bidding behaviors under the two auction mechanisms.

A Unified Framework for Dynamic Prediction Market Design

Recently, coinciding with and perhaps driving the increased popularity of prediction markets, several novel pari-mutuel mechanisms have been developed such as the logarithmic market-scoring rule (LMSR), the cost-function formulation of market makers, utility-based markets, and the sequential convex pari-mutuel mechanism (SCPM). In this work, we present a convex optimization framework that unifies these seemingly unrelated models for centrally organizing contingent claims markets. The existing mechanisms can be expressed in our unified framework by varying the choice of a concave value function. We show that this framework is equivalent to a convex risk minimization model for the market maker. This facilitates a better understanding of the risk attitudes adopted by various mechanisms. The unified framework also leads to easy implementation because we can now find the cost function of a market maker in polynomial time by solving a simple convex optimization problem. In addition to unifying and explaining the existing mechanisms, we use the generalized framework to derive necessary and sufficient conditions for many desirable properties of a prediction market mechanism such as proper scoring, truthful bidding (in a myopic sense), efficient computation, controllable risk measure, and guarantees on the worst-case loss. As a result, we develop the first proper, truthful, risk-controlled, loss-bounded (independent of the number of states) mechanism; none of the previously proposed mechanisms possessed all these properties simultaneously. Thus, our work provides an effective tool for designing new prediction market mechanisms. We also discuss possible applications of our framework to dynamic resource pricing and allocation in general trading markets.