Fast Core Pricing for Rich Advertising Auctions

Standard ad auction formats do not immediately extend to settings where multiple size configurations and layouts are available to advertisers. In these settings, the sale of web advertising space increasingly resembles a combinatorial auction with complementarities, where truthful auctions such as the Vickrey–Clarke–Groves (VCG) auction can yield unacceptably low revenue. In “Fast Core Pricing for Rich Advertising Auctions,” Niazadeh, Hartline, Immorlica, Khani, and Lucier study and suggest core-selecting auctions, which boost revenue by setting payments so that no group of agents, including the auctioneer, can jointly improve their utilities by switching to a different outcome. Their main result is a combinatorial algorithm that finds an approximate bidder-optimal core point with an almost linear number of calls to the welfare-maximization oracle. This algorithm is faster than previously proposed heuristics in the literature and has theoretical guarantees. By accompanying the theoretical study with an experimental study based on Microsoft Bing Ad Auction data, the authors conclude that core pricing is implementable even for very time-sensitive practical use cases such as real-time online advertising and can yield more revenue than the VCG or generalized second price auction.

Resource sharing of mobile edge computing networks based on auction game and blockchain

The edge clouds in mobile edge computing networks are isolate which may belong to different companies or organizations, and hence the communication, computation, and storage resources are not efficiently utilized. To solve this problem, we propose the resource-sharing model of edge clouds which is based on blockchain technology and auction game. In this model, the blockchain platform is regarded as the bridge of the resource sharing, composed of edge clouds, clouds, third-party spectrum and computation management, identity authentication institutions, etc. It is used to record the users’ transaction information and broadcast the intelligent terminals’ resource requirements to all edge clouds in the blockchain platform through smart contracts. Then, an optimization problem of the joint allocation of communication and computation resources is formulated to maximize the utility of intelligent terminals. And an efficient improved sealed second-price auction game is proposed to allocate communication and computation resources and determine the optimal price of resources under the intelligent terminals’ QoS constraints. Simulation results show that the model can effectively improve the system resources utilization and the successful transaction rate.

Obviousness around the clock

Li (Am Econ Rev 107(11):3257–3287, 2017) introduces a theoretical notion of obviousness of a dominant strategy, to be used as a refinement in mechanism design. This notion is supported by experimental evidence that bidding is closer to dominance in the dynamic ascending-clock auction than the static second-price auction (private values), noting that dominance is theoretically obvious in the former but not the latter. We replicate his experimental study and add three intermediate auction formats that decompose the designs’ differences to quantify the cumulative effects of (1) simply seeing an ascending-price clock (after bid submission), (2) bidding dynamically on the clock, and (3) getting (theoretically irrelevant) drop-out information about other bidders. The theory predicts dominance to become obvious through (2), dynamic bidding. We find no significant behavioral effect of (2), however, while the feedback effects (1) and (3) are highly significant. We conclude that behavioral differences between second-price and ascending-clock auctions offer rather limited support for the theory of obviousness and that framing has surprisingly large potential in mechanism design.

Auction theory and a note on game mechanisms

This paper will review important topics on the subject of auction theory and mechanism design, these include: efficiency first and foremost, also revenue comparison between different types of auctions and the issue of incentive compatibility, individual rationality with the general idea and proof that bilateral trade is inefficient. Mechanism design theory tells us that if buyers and sellers both have private information full efficiency is impossible, however Vickrey auction (single unit auction) will be efficient i.e. will put the goods in the hands of the buyers that value them most. However, the conclusion from this paper is that because of overvaluation of bidders the main result is inefficient, i.e. bids are too high. When weak and strong bidders are compared the main conclusion is that strong bidders’ expected payoff is higher in second price auction (SPA), while weak bidder prefers first price auction (FPA) bid.

Blockchain technology based decentralized energy management in multi-microgrids including electric vehicles

This paper proposes an energy scheduling mechanism among multiple microgrids (MGs) and also within the individual MGs. In this paper, electric vehicle (EV) energy scheduling is also considered and is integrated in the operation of the microgrid (MG). With the advancements in the battery technologies of EVs, the significance of Vehicle-to-Grid (V2G) is increasing tremendously. So, designing the strategies for energy management of electric vehicles (EVs) is of paramount importance. The battery degradation cost of an EV is also taken into account. Vickrey second price auction is used for truthful bidding. To enhance the security and trust, blockchain technology can be incorporated. The market is shifted to decentralized state by using blockchain. To encourage the MGs to generate more, contribution index is allotted to each prosumer of a MG and to the MGs as a whole, depending on which priority is given during auction. The system was simulated using IEEE 118 bus feeder which consists of 5 MGs, which in turn contain EVs and prosumers.

Prior-Independent Optimal Auctions

Auctions are widely used in practice. Although auctions are also extensively studied in the literature, most of the developments rely on the significant common prior assumption. We study the design of optimal prior-independent selling mechanisms: buyers do not have any information about their competitors, and the seller does not know the distribution of values but only knows a general class to which it belongs. Anchored on the canonical model of buyers with independent and identically distributed values, we analyze a competitive ratio objective in which the seller attempts to optimize the worst-case fraction of revenues garnered compared with those of an oracle with knowledge of the distribution. We characterize properties of optimal mechanisms and in turn establish fundamental impossibility results through upper bounds on the maximin ratio. By also deriving lower bounds on the maximin ratio, we are able to crisply characterize the optimal performance for a spectrum of families of distributions. In particular, our results imply that a second price auction is an optimal mechanism when the seller only knows that the distribution of buyers has a monotone nondecreasing hazard rate, and it guarantees at least 71.53% of oracle revenues against any distribution within this class. Furthermore, a second price auction is near optimal when the class of admissible distributions is that of those with nondecreasing virtual value function (a.k.a. regular). Under this class, it guarantees a fraction of 50% of oracle revenues, and no mechanism can guarantee more than 55.6%. This paper was accepted by Kalyan Talluri, revenue management and market analytics.

Marketing Agencies and Collusive Bidding in Online Ad Auctions

The transition of the advertising market from traditional media to the internet has induced a proliferation of marketing agencies specialized in bidding in the auctions that are used to sell ad space on the web. We analyze how collusive bidding can emerge from bid delegation to a common marketing agency and how this can undermine the revenues and allocative efficiency of both the generalized second-price auction (GSP, used by Google, Microsoft Bing, and Yahoo!) and the Vickrey–Clarke–Groves (VCG) mechanism (used by Facebook). We find that despite its well-known susceptibility to collusion, the VCG mechanism outperforms the GSP auction in terms of both revenues and efficiency. This paper was accepted by Gabriel Weintraub, revenue management and market analytics.

Lindahl vs. Lindahl: Optimal siting and sizing of a noxious facility

The provision of projects generating net benefits for several communities except for the host community poses two problems: where to locate the unpleasant facility, and how large this facility should be. We propose a mechanism that combines some market-like properties with a modified second-price auction. We elicit prices per unit as a host and as a contributor to the facility, the desired quantity (i.e., facility size), and an auction's bid defining the hosting community. Regardless of whom is selected as the host, any equilibrium outcome of this mechanism is a Lindahl allocation. If, in addition, every community truthfully reveals its gain from becoming the host (with respect to being a contributor), the selected Lindahl allocation is globally optimal.

Online Second Price Auction with Semi-Bandit Feedback under the Non-Stationary Setting

In this paper, we study the non-stationary online second price auction problem. We assume that the seller is selling the same type of items in T rounds by the second price auction, and she can set the reserve price in each round. In each round, the bidders draw their private values from a joint distribution unknown to the seller. Then, the seller announced the reserve price in this round. Next, bidders with private values higher than the announced reserve price in that round will report their values to the seller as their bids. The bidder with the highest bid larger than the reserved price would win the item and she will pay to the seller the price equal to the second-highest bid or the reserve price, whichever is larger. The seller wants to maximize her total revenue during the time horizon T while learning the distribution of private values over time. The problem is more challenging than the standard online learning scenario since the private value distribution is non-stationary, meaning that the distribution of bidders' private values may change over time, and we need to use the non-stationary regret to measure the performance of our algorithm. To our knowledge, this paper is the first to study the repeated auction in the non-stationary setting theoretically. Our algorithm achieves the non-stationary regret upper bound Õ(min{√S T, V¯⅓T⅔), where S is the number of switches in the distribution, and V¯ is the sum of total variation, and S and V¯ are not needed to be known by the algorithm. We also prove regret lower bounds Ω(√S T) in the switching case and Ω(V¯⅓T⅔) in the dynamic case, showing that our algorithm has nearly optimal non-stationary regret.