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The authors link the rapid and dramatic move from second-price to first-price auction format in the display advertising market, on the one hand, to the move from the waterfalling mechanism employed by publishers for soliciting bids in a preordered cascade over exchanges to an alternate header bidding strategy that broadcasts the request for bid to all exchanges simultaneously, on the other. First, they argue that the move from waterfalling to header bidding was a revenue-improving move for publishers in the old regime when exchanges employed second-price auctions. Given the publisher move to header bidding, the authors show that exchanges move from second-price to first-price auctions to increase their expected clearing prices. Interestingly, when all exchanges move to first-price auctions, each exchange faces stronger competition from other exchanges, and some exchanges may end up with lower revenue than when all exchanges use second-price auctions; yet all exchanges move to first-price auctions in the unique equilibrium of the game. The authors show that the new regime hinders the exchanges' ability to differentiate in equilibrium. Furthermore, it allows the publishers to achieve the revenue of the optimal mechanism despite not having direct access to the advertisers.

This report analyses competition in public procurement in Slovenia and the rationale behind the high prevalence of single-bidding and negotiated procedures without prior publication. It provides an empirical analysis of competition, using procurement data and several variables. It also provides recommendations for potential improvements to the institutional and regulatory frameworks to foster competition in Slovenia. Finally, it provides recommendations for improving the capacity of both contracting authorities and bidders regarding public procurement.

In sponsored search advertising, advertisers bid to be displayed in response to a keyword search. The operational activities associated with participating in an auction, i.e., submitting the bid and the ad copy, customizing bids and ad copies based on various factors (such as the geographical region from which the query originated, the time of day and the season, the characteristics of the searcher), and continuously measuring outcomes, involve considerable effort. We call the costs that arise from such activities keyword management costs . To reduce these costs and increase advertisers’ participation in keyword auctions, search engines offer an opt-in tool called broad match with automatic and flexible bidding , wherein the search engine automatically places bids on behalf of the advertisers and takes over the above activities as well. The bids are based on the search engine’s estimates of the advertisers’ valuations and, therefore, may be less accurate than the bids the advertisers would have turned in themselves. Using a game-theoretic model, we examine the strategic role of keyword management costs, and of broad match, in sponsored search advertising. We show that because these costs inhibit participation by advertisers in keyword auctions, the search engine has to reduce the reserve price, which reduces the search engine’s profits. This motivates the search engine to offer broad match as a tool to reduce keyword management costs. If the accuracy of broad match bids is sufficiently high, advertisers adopt broad match and benefit from the cost reduction, whereas if the accuracy is very low, advertisers do not use it. Interestingly, at moderate levels of bid accuracy, advertisers individually find it attractive to reduce costs by using broad match, but competing advertisers also adopt broad match and the increased competition hurts all advertisers’ profits, thus creating a “prisoner’s dilemma.” When advertisers adopt broad match, search engine profits increase. It therefore seems natural to expect that the search engine will be motivated to improve broad match accuracy. Our analysis shows that the search engine will increase broad match accuracy up to the point where advertisers choose broad match, but that increasing the accuracy any further reduces the search engine’s profits.

A strategy is obviously dominant if, for any deviation, at any information set where both strategies first diverge, the best outcome under the deviation is no better than the worst outcome under the dominant strategy. A mechanism is obviously strategy-proof (OSP) if it has an equilibrium in obviously dominant strategies. This has a behavioral interpretation: a strategy is obviously dominant if and only if a cognitively limited agent can recognize it as weakly dominant. It also has a classical interpretation: a choice rule is OSP-implementable if and only if it can be carried out by a social planner under a particular regime of partial commitment.

This article introduces a near-optimal bidding algorithm for use in real-time display advertising auctions. These auctions constitute a dominant distribution channel for internet display advertising and a potential funding model for addressable media. The proposed efficient, implementable learning algorithm is proven to rapidly converge to the optimal strategy while achieving zero regret and constituting a competitive equilibrium. This is the first algorithmic solution to the online knapsack problem to offer such theoretical guarantees without assuming a priori knowledge of object values or costs. Furthermore, it meets advertiser requirements by accommodating any valuation metric while satisfying budget constraints. Across a series of 100 simulated and 10 real-world campaigns, the algorithm delivers 98% of the value achievable with perfect foresight and outperforms the best available alternative by 11%. Finally, we show how the algorithm can be augmented to simultaneously estimate impression values and learn the bidding policy. Across a series of simulations, we show that the total regret delivered under this dual objective is less than that from any competing algorithm required only to learn the bidding policy.

A major challenge faced by marketers attempting to optimize their advertising campaigns is to deal with budget constraints. The problem is even harder in the face of multidimensional budget constraints, particularly in the presence of many decision variables involved and the interplay among the decision variables through such constraints. Concise bidding strategies help advertisers deal with this challenge by introducing fewer variables upon which to act. In this paper, we study the problem of finding optimal concise bidding strategies for advertising campaigns with multiple budget constraints. Given bid landscapes—that is, the predicted value (e.g., number of clicks) and the cost per click for any bid—that are typically provided by ad-serving systems, we optimize the value of an advertising campaign given its budget constraints. In particular, we consider bidding strategies that consist of no more than k different bids for all keywords. For constant k , we provide a polynomial-time approximation scheme to optimize the profit, whereas for arbitrary k we show how a constant-factor approximation algorithm can be obtained via a combination of solution enumeration and dependent LP rounding techniques, which can be of independent interest. In addition to being able to deal with multidimensional budget constraints, our results do not assume any specific payment scheme and can be applied on pay-per-click, pay-per-impression, or pay-per-conversion models. Also, no assumption about the concavity of value or cost functions is made. Finally, we evaluate the performance of our algorithms on real data sets in regimes with up to six-dimensional budget constraints. In the case of a single budget constraint, in which uniform bidding (currently used in practice) has a provable performance guarantee, our algorithm beats the state of the art by an increase of 1%–6% in the expected number of clicks. This is achieved by only two or three clusters in contrast with the single cluster permitted in uniform bidding. With multiple budget constraints, the gap between the performance of our algorithm and an enhanced version of uniform bidding grows to an average of 5%–6% (and as high as 35% in higher dimensions). This paper was accepted by Yinyu Ye, optimization.

The smart grid is an unprecedented opportunity to shift the current energy industry into a new era of a modernized network where the power generation, transmission, and distribution are intelligently, responsively, and cooperatively managed through a bi-directional automation system. Although the domains of smart grid applications and technologies vary in functions and forms, they generally share common potentials such as intelligent energy curtailment, efficient integration of Demand Response, Distributed Renewable Generation, and Energy Storage. This paper presents a comprehensive review categorically on the recent advances and previous research developments of the smart grid paradigm over the last two decades. The main intent of the study is to provide an application-focused survey where every category and sub-category herein are thoroughly and independently investigated. The preamble of the paper highlights the concept and the structure of the smart grids. The work presented intensively and extensively reviews the recent advances on the energy data management in smart grids, pricing modalities in a modernized power grid, and the predominant components of the smart grid. The paper thoroughly enumerates the recent advances in the area of network reliability. On the other hand, the reliance on smart cities on advanced communication infrastructure promotes more concerns regarding data integrity. Therefore, the paper dedicates a sub-section to highlight the challenges and the state-of-the-art of cybersecurity. Furthermore, highlighting the emerging developments in the pricing mechanisms concludes the review.

One of the central issues in auction design is how much information should be disclosed to bidders. In this paper, we examine bidder's identity disclosure in sequential business-to-business (B2B) auctions. Specifically, we compare two information disclosure policies, one that publicly discloses winners’ identities (the status quo) and an alternative policy that conceals winners’ identities. Using a large-scale field experiment in the Dutch flower auction market, we find that concealing winners’ identities can significantly increase the average winning price and thereby raise the seller’s revenue. We further explore the underlying mechanism that drives the observed effect. The empirical analysis of bidding behavior in these auctions suggests that bidders tend to imitate some of their competitors who have won in previous rounds of auctions and shade their bids accordingly. Concealing winners’ identities can disrupt such imitation heuristic, which in turn mitigates the price-declining trend in sequential rounds. Our findings have important implications for the design of information disclosure policies in B2B auction markets. This paper was accepted by Chris Forman, information systems.

This paper examines bidding in over 1,700 knockout auctions used by a bidding cartel (or ring) of stamp dealers in the 1990s. The knockout was conducted using a variant of the model studied by Daniel Graham, Robert Marshall, and Jean-Francois Richard (1990). Following a reduced form examination of these data, damages, induced inefficiency, and the ring's benefit from colluding are estimated using a structural model in the spirit of Emmanuel Guerre, Isabelle Perrigne, and Quang Vuong (2000). A notable finding is that nonring bidders suffered damages that were of the same order of magnitude as those of the sellers.

Internet auction companies have developed innovative tools that enable sellers to reveal more information about their credibility and product quality to avoid the \"lemons\" problem. On the basis of signaling and auction theories, the authors propose a typology of Internet auction quality and credibility indicators, adopt and modify Park and Bradlow's (2005) model, and use eBay as an example to examine empirically how different types of indicators help alleviate uncertainty. This empirical evidence demonstrates how Internet auction features affect consumer participation and bidding decisions, what modifies the credibility of quality indicators, and how different buyers react to indicators. The signaling-based hypotheses provide coherent explanations of consumers' bidding behavior. As the first empirical study to evaluate the signaling role of comprehensive Internet auction institutional features in mitigating the adverse selection problem, this research provides evidence to clarify the economic foundation behind innovative Internet auction designs.