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On-demand delivery in urban areas has been growing rapidly in recent years. Nevertheless, on-demand delivery networks lack an efficient, sustainable, and environmentally friendly operative strategy. An open trading system equipped with on-line auctions provides an opportunity for increasing the efficiency of on-demand delivery systems. Reinforcement learning techniques that automate decision-making can facilitate the implementation of such complex and dynamic systems. This paper presents an on-line auction-based request trading platform embedded within an open trading system as a new scheme for carriers and shippers to trade on-demand delivery requests. The system is developed based on a multi-agent model, composed of carriers, shippers, and the on-line platform as autonomous agents. Deep Q network enabled reinforcement learning is used in the decision-making processes for the agents to optimise their behaviour in a dynamic environment. Numerical experiments conducted on the Melbourne metropolitan network demonstrate the effectiveness of the open trading system, which can provide benefits for all stakeholders involved in the on-demand delivery market as well as the entire system. The reinforcement learning enabled platform can gain more profit when there are more learning carriers. The results indicate that the intelligent open trading system with on-line auctions is a promising city logistics solution. [ABSTRACT FROM AUTHOR]

The paper uses bids submitted by primary dealer banks at auctions of sovereign bonds to quantify the price elasticity of demand. The price elasticity of demand correlates strongly with the volatility of returns of the same bonds traded in the secondary market but only weakly with their bid-ask spread. It predicts same-bond post-auction returns in the secondary market, even after controlling for pre-auction volatility. The evidence suggests that the price elasticity of demand is associated with the magnitude of price pressure in the secondary market around auction days and proxies for primary dealer risk-bearing capacity. [ABSTRACT FROM AUTHOR]

Cloud Manufacturing (CMfg) is a promising approach that leverages the sharing economy to reduce costs and enhance supply chain flexibility. Particularly, when utilised alongside Additive Manufacturing (AM), CMfg is considered a key enabler for collaborative production (CP) systems. However, there is still a lack of planning models that reduce entry barriers for CP. Therefore, we propose a decentralised CP planning framework for AM. In our approach, machines autonomously select jobs from an existing production plan to forward them to other suppliers that can produce these parts more efficiently. A CMfg platform facilitates job forwarding and creates promising part bundles and manufacturing machines autonomously places bids on the packages via a combinatorial $ 2^{nd} $ 2 nd price reverse auction. Costs of the reallocated bundles are shared throughout a Shapley value-based approach without the need to disclose critical information. We benchmark our proposed framework against a centralised planning approach and find that it achieves comparable effectiveness as the benchmark solution. We also show that this mechanism promotes individual rationality and that agents particularly benefit when participating in both offering and acquiring production jobs through the auction. [ABSTRACT FROM AUTHOR]

This study uses an agent-based approach with a combinatorial auction mechanism to solve the joint order acceptance and assembly job shop scheduling problem. A set of jobs is offered. Each job has a revenue, ready time, due date, deadline, and consists of a set of operations with precedence relationships. Jobs that deviate from their due dates incur earliness/tardiness penalties. An operation may require several units of capacity per time unit and a resource could have multiple units of capacity. The manufacturer can reject any job to satisfy the capacity constraints and maximise the overall profit. We develop a mathematical model for the problem, then use an agent-based approach to solve it. First, the relaxed problem is decomposed into a set of job-level subproblems. Each job is optimised individually without considering the capacity constraints. Profitable jobs at the individual level submit their optimal schedules as combinatorial bids to an auctioneer to acquire combinations of resource capacity-time units. Then, the auctioneer records the profit upper bound, resolves capacity conflicts to reach a feasible solution, records the profit lower bound, and updates the dual variables. Experimental results show that the proposed methodology can solve large-sized problems in reasonable CPU times. [ABSTRACT FROM AUTHOR]

Designing an incentive compatible auction that maximizes expected revenue is an intricate task. The single-item case was resolved in a seminal piece of work by Myerson in 1981, but more than 40 years later, a full analytical understanding of the optimal design still remains elusive for settings with two or more items. In this work, we initiate the exploration of the use of tools from deep learning for the automated design of optimal auctions. We model an auction as a multi-layer neural network, frame optimal auction design as a constrained learning problem, and show how it can be solved using standard machine learning pipelines. In addition to providing generalization bounds, we present extensive experimental results, recovering essentially all known solutions that come from the theoretical analysis of optimal auction design problems and obtaining novel mechanisms for settings in which the optimal mechanism is unknown. [ABSTRACT FROM AUTHOR]

This article provides a survey of China's initial public offering (IPO) market, focusing on IPO pricing, bids and allocation, and aftermarket trading. We show that strict regulations result in suppressed IPO offer prices and high initial returns, causing a high cost of going public. Investors treat IPOs as lotteries with extremely high short-term returns, with little attention to the long term. The auction selling method, however, works in the way it is supposed to. Mutual funds bid in a more informative way than other investors, and their advantages are unlikely to be due to underwriters' preferential treatment. We also discuss the latest registration-system reform. [ABSTRACT FROM AUTHOR]

In many situations, two rivals find themselves facing a common threat necessitating cooperation. Such alliances are uneasy as the parties anticipate eventually competing against each other in the future. Recent theoretical work shows that both standalone and joint contribution strategies, as well as a hybrid of the two, can emerge in equilibrium where the former strategy is characterized by one of the rivals contributing enough to eliminate the common threat, while the latter is characterized by rivals providing half the necessary effort to eliminate the common threat. Using a controlled laboratory experiment, we show that player behavior is best described by the hybrid strategy. However, none of these predictions closely describes the observed behavior, which is better described as following a proportionate rule. [ABSTRACT FROM AUTHOR]

Buyout options are available to bidders in certain online auctions. This study analyzes a bidder's buyout decision following the level-k auction theory proposed by Crawford and Iriberri (2007). Specifically, we derive the optimal buyout strategy of type Lk. We have three significant findings. First, the buyout decisions of Lk and L k - 1 are strategic substitutes. If L k - 1 's buyout incentive is stronger (weaker, respectively) than a rational bidder's, then Lk's buyout incentive is weaker (stronger, respectively) than a rational bidder's. Second, if L1's buyout incentive is identical to a rational bidder's, so does Lk for k > 1 . Third, if L1's buyout incentive differs from a rational bidder's, the auction revenue can be higher than the equilibrium level for some buy price. A certain bounded rationality improves auction revenues even if bidders are risk-neutral. Our findings consistently examine buyout decisions in auction experiments: accepting high buy prices and rejecting low buy prices. [ABSTRACT FROM AUTHOR]

This research deviates from usual studies in auction literature primarily focused on maximizing expected revenue. Instead, we concentrate on the strategic design of discrete Dutch auctions in the context of bidder emotional attachment, wherein valuations follow a lognormal distribution. Our objective is to attain an optimal balance between the auctioned object's selling price and the auction duration, ultimately maximizing the auctioneer's expected revenue per unit of time. Our proposed models exhibit significantly higher average revenues per unit of time than counterparts neglecting time considerations and emotional attachment of the bidders. This achievement results from strategically reducing auction durations, enabling more auctions within the allotted time. This intentional trade-off ensures the marginal revenue decrease in shorter auctions is surpassed by the substantial increase in overall revenues from heightened auction frequency. Numerical results emphasize the utility of our modified discrete Dutch auction design, particularly in scenarios with a large number of bidders. Furthermore, increasing skewness in valuation distributions correlates with higher revenue per unit of time. Complete knowledge of the number of participating bidders is crucial, leading to a noticeable elevation in the auctioneer's expected revenue per unit of time. However, the predictability of auction outcomes may be challenging, underscoring the nuanced nature of auction dynamics. [ABSTRACT FROM AUTHOR]

Auctions have become the most important policy instrument for promoting renewable energy (RE). This article offers a conceptual and methodological framework to grasp the variety of national RE auctions' governance arrangements. It presents indices to systematically measure and compare key dimensions of RE auctions and electricity governance: scope of regulation, private operators' influence, coordination, and the concentration of authority. The framework's usefulness is demonstrated via its application to three contrasted cases – the United Kingdom, Mexico, and Morocco, showing the disruption in electricity governance patterns induced by RE auctions and the relevance of the multidimensional approach to understand policy outcomes. [ABSTRACT FROM AUTHOR]

A classic result of Korte and Hausmann [1978] and Jenkyns [1976] bounds the quality of the greedy solution to the problem of finding a maximum value basis of an independence system (E,ℐ)$$ \left(E,\mathcal{I}\right) $$ in terms of the rank‐quotient. We extend this result in two ways. First, we apply the greedy algorithm to an inner independence system contained in ℐ$$ \mathcal{I} $$. Additionally, following an idea of Milgrom [2017], we incorporate exogenously given prior information about the set of likely candidates for an optimal basis in terms of a set 풪⊆ℐ. We provide a generalization of the rank‐quotient that yields a tight bound on the worst‐case performance of the greedy algorithm applied to the inner independence system relative to the optimal solution in 풪. Furthermore, we show that for a worst‐case objective, the inner independence system approximation may outperform not only the standard greedy algorithm but also the inner matroid approximation proposed by Milgrom [2017]. Second, we generalize the inner approximation framework of independence systems to inner approximations of packing instances in ℤ≥0n$$ {\mathbb{Z}}_{\ge 0}^n $$ by inner polymatroids and inner packing instances. We consider the problem of maximizing a separable discrete concave function and show that our inner approximation can be better than the greedy algorithm applied to the original packing instance. Our result provides a lower bound to the generalized rank‐quotient of a greedy algorithm to the optimal solution in this more general setting and subsumes Malinov and Kovalyov [1980]. We apply the inner approximation approach to packing instances induced by the FCC incentive auction and by two knapsack constraints. [ABSTRACT FROM AUTHOR]

Reverse auctions are often recognized as a tool that can cost-effectively allocate agri-environmental program funds to support environmentally-beneficial land management practices. However, transaction costs can limit participation in auctions which limits their cost-effectiveness. We use a laboratory experiment to examine how various levels of transaction costs influence participation and bidding behavior in discriminatory-price reverse auctions in low and high budget scenarios. Our experimental results show that transaction costs limit auction participation, increase bid amounts, and reduce cost-effectiveness. The negative effect of transaction costs on participation is particularly large when the budget level is low. Using the results of our experiment, we design a simulation to investigate whether reducing transaction costs via subsidies could increase program cost-effectiveness under various conditions. We find that transaction cost subsidies increase auction cost-effectiveness; however, our study raises new questions about how these subsidies are designed and the implications for the overall costs and benefits of efforts to reduce transaction costs in reverse auctions. [ABSTRACT FROM AUTHOR]

This paper tests whether the new trading mechanism reduces closing price manipulation in the Chinese stock market. The introduction of the closing call auction on the Shanghai Stock Exchange on 20 August 2018 can be considered a quasi-natural experiment. Using data from 2017 to 2018 for all companies listed on the main board of the Shanghai and Shenzhen Stock Exchanges, we provide robust evidence that the implementation of call auction on the Shanghai Stock Exchange can effectively reduce closing price manipulation in stock market. We further investigate the impact of various factors, including firm industry, ownership structure, secondary market trading, equity structure, and financial structure, on the effectiveness of call auction in mitigating closing price manipulation. Moreover, the mechanism study indicates that the introduction of closing call auction would improve the operation efficiency and information efficiency of stock market, thus reducing the closing price manipulation. Specifically, our results reveal that the closing call auction policy is effective in reducing manipulation and provides theoretical support for stock market institutional construction. [ABSTRACT FROM AUTHOR]

The two seats of the Paris Jesuits, the professed house and the Clermont college, each had a separate library, both of which owned books on coins as well as a numismatic collection. The discovery of their respective catalogs, annotated with auction results—prices and buyers' names—has enabled an analysis of their contents and fates. The auction catalogs describe around fifteen thousand and thirteen thousand "medals" over just a few pages, but the descriptions are too brief to identify individual items, save for a Roman weight now preserved in the Louvre Museum. The Paris Jesuits owned many more ancient coins than modern ones, and their commercial value was much higher than modern coins. However, in both cases, the value of the coin cabinets was only a fraction of the total value of the libraries themselves. [ABSTRACT FROM AUTHOR]

We investigate the stability of internet platform trading mechanisms using the notion of ex-ante incentive compatible core defined by Forges et al. (2002) in the context of an exchange economy. A mechanism can be blocked by a single buyer and seller pair if they can find an interim incentive-compatible trading mechanism that gives them higher ex-ante expected utilities. Standard double auction mechanisms like the trade reduction mechanism and McAfee double auction mechanism may not be single-buyer–single-seller (SBSS) ex-ante stable. We characterize interim incentive-compatible, interim individually-rational, symmetric and revenue-maximizing mechanisms that are SBSS ex-ante stable using methods in Myerson and Satterthwaite (1983). • We study the stability of double auction mechanisms of a platform. • Standard double auctions may not be single-buyer-single-seller (SBSS) ex-ante stable. • We characterize class of revenue-maximizing mechanisms that are SBSS ex-ante stable. [ABSTRACT FROM AUTHOR]

We investigate the consequences of different market designs and policies on market quality in a high‐frequency market. Based on an extensible theoretical framework, high‐frequency traders in our model can be either market makers or arbitragers, which leads to a Nash equilibrium between their utilities. We consider the optimal strategies of different market agents in various market conditions based on the equilibrium. We find frequent batch auctions benefit market liquidity but harm market volatility compared to continuous‐time auctions. Order cancellation ban harms market quality while the taker fee is beneficial. We also find that market design and policy changes are only effective in low‐latency markets. Finally, we address that the total effect of high‐frequency trading is positive, so it is vital to reap high‐frequency traders' benefits while minimising their harms in high‐frequency markets. [ABSTRACT FROM AUTHOR]

In "Optimal No-Regret Learning in Repeated First-Price Auctions," Y. Han, W. Tsachy, and Z. Zhou study online learning in repeated first-price auctions where a bidder, only observing the winning bid at the end of each auction, learns to adaptively bid to maximize her cumulative payoff. To achieve this goal, the bidder faces censored feedback: If she wins the bid, then she is not able to observe the highest bid of the other bidders, which we assume is i.i.d. drawn from an unknown distribution. In this paper, they develop the first learning algorithm that achieves a near-optimal regret bound, by exploiting two structural properties of first-price auctions, that is, the specific feedback structure and payoff function. We study online learning in repeated first-price auctions where a bidder, only observing the winning bid at the end of each auction, learns to adaptively bid to maximize the cumulative payoff. To achieve this goal, the bidder faces censored feedback: If the bidder wins the bid, then the bidder is not able to observe the highest bid of the other bidders, which we assume is i.i.d. drawn from an unknown distribution. In this paper, we develop the first learning algorithm that achieves a near-optimal O˜(T) regret bound, by exploiting two structural properties of first-price auctions, that is, the specific feedback structure and payoff function. We first formulate the feedback structure in first-price auctions as partially ordered contextual bandits, a combination of the graph feedback across actions (bids), the cross-learning across contexts (private values), and a partial order over the contexts. We establish both strengths and weaknesses of this framework by showing a curious separation that a regret nearly independent of the action/context sizes is possible under stochastic contexts but is impossible under adversarial contexts. In particular, this framework leads to an O(T log2.5T) regret for first-price auctions when the bidder's private values are independent and identically distributed. Despite the limitation of this framework, we further exploit the special payoff function of first-price auctions to develop a sample-efficient algorithm even in the presence of adversarially generated private values. We establish an O(T log3T) regret bound for this algorithm, hence providing a complete characterization of optimal learning guarantees for first-price auctions. Funding: This project was supported in part by the National Science Foundation [Awards CCF-2106467 and CCF-2106508]. Y. Han and T. Weissman were partially supported by the Yahoo Faculty Research and Engagement Program. [ABSTRACT FROM AUTHOR]

Problem definition: Motivated by carbon emissions trading schemes (ETSs), Treasury auctions, procurement auctions, and wholesale electricity markets, which all involve the auctioning of homogeneous multiple units, we consider the problem of learning how to bid in repeated multiunit pay-as-bid (PAB) auctions. In each of these auctions, a large number of (identical) items are to be allocated to the largest submitted bids, where the price of each of the winning bids is equal to the bid itself. In this work, we study the problem of optimizing bidding strategies from the perspective of a single bidder. Methodology/results: Effective bidding in PAB auctions is complex due to the combinatorial nature of the action space. We show that a utility decoupling trick enables a polynomial time algorithm to solve the offline problem where competing bids are known in advance. Leveraging this structure, we design efficient algorithms for the online problem under both full information and bandit feedback settings that achieve an upper bound on regret of O(MT log T) and O(MT23log T) , respectively, where M is the number of units demanded by the bidder, and T is the total number of auctions. We accompany these results with a regret lower bound of Ω(MT) for the full information setting and Ω(M2/3T2/3) for the bandit setting. We also present additional findings on the characterization of PAB equilibria. Managerial implications: Although the Nash equilibria of PAB auctions possess nice properties such as winning bid uniformity and high welfare and revenue, they are not guaranteed under no-regret learning dynamics. Nevertheless, our simulations suggest that these properties hold anyways, regardless of Nash equilibrium existence. Compared with its uniform price counterpart, the PAB dynamics converge faster and achieve higher revenue, making PAB appealing whenever revenue holds significant social value—for example, ETSs and Treasury auctions. Funding: R. Galgana and N. Golrezaei were supported in part by the Young Investigator Program Award from the Office of Naval Research [Grant N00014-21-1-2776] and the Massachusetts Institute of Technology Research Support Award. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0403. [ABSTRACT FROM AUTHOR]

This paper proposes an auction-based market trade mechanism for the electric vehicle battery recycling (EVBR) problem, which aims to realise the optimal resource allocation and pricing of EVBR. The main motivation of this paper is to attempt to explore an approach to achieving efficient battery recycling. We first consider an EVBR market with m buyers and n sellers, and develop the multi-unit trade reduction (MTR) mechanism in the EVBR market. According to the supply and demand relationship in the EVBR market, we consider three market scenarios of supply and demand balance, oversupply, and overdemand, and formulated corresponding auction allocation rules. Numerical study results show that the proposed MTR mechanism can achieve efficient resource allocation. We also observed that not all results increased with the number of sellers/buyers. Second, considering the distance between sellers and buyers, we developed a stochastic multiple MTR (SM-MTR) mechanism to enable sellers and buyers within the region to conduct transactions. Finally, we propose an integrated MTR, SM-MTR and one-sided Vickrey--Clarke--Groves auction mechanism that is feasible in both one-sided and bilateral environments. Furthermore, our work can provide novel managerial implications for EVBR market stakeholders in terms of practical application. [ABSTRACT FROM AUTHOR]

Although multichannel sales strategies have become common due to the use of advanced information technologies, how one trading mechanism can influence the outcome of another, especially in the B2B market, remains largely underexplored. This paper investigates the effect of price and quantity information from an online posted-price presales channel on the performance of the century-old sequential Dutch auction system. Sellers can control the price paid and make a proportion of their stock available in auction presales. Anything left after presales is sold via auctions. Our analysis of nearly 1.5 million flower lots reveals a positive effect with higher auction prices and total revenue for lots listed in presales than for lots that are not. The result holds even for lots with no actual sales in the presales, indicating that buyers pay close attention to the additional information from the posted-price presales channel. By teasing out the information effect of presales prices and presales quantity on auction prices, we evaluate a number of pricing strategies. The results suggest that selling at a high price in presales is still more beneficial than selling more by discounting prices. [ABSTRACT FROM AUTHOR]

This study revisits the question of predicting patent value with observed patent statistics, exploiting a rare public auction of patents in China in which the starting prices are used as approximates of patent private value. Family size and the number of assignees come out as the most consistent indicators, while notably, forward citations turn out to be not important. This discrepancy with past studies is investigated. We propose some of the institutional features in the Chinese patent system could lead to the forward citations not as informative as their counterpart in other jurisdictions. The findings bear important implications as Chinese patent data are increasingly being used in innovation studies. [ABSTRACT FROM AUTHOR]

We prove that the PoA of First Price Auctions is 1 -- 1/e² ≈ 0.8647, closing the gap between the best known bounds [0.7430, 0.8689]. [ABSTRACT FROM AUTHOR]

The article examines the impact of fish products export by fishermen of the Murmansk Oblast on the realization of two main indicators prescribed in the Food Security Doctrine: the volume of products supplied to the domestic market and their economic accessibility to the population. As a result of the study, it was found that the main indicator of the Doctrine implementation — the threshold value of fish products supply to the domestic market — is not met due to its excessive export. The level of under-supply, compared to the recommendations of the Doctrine, amounted to 53.4% in 2021. Fish is supplied to the Murmansk Oblast and other regions in volumes that ensure its sale at high wholesale prices set by fishermen and with little competition. The bulk of the Arctic fish catch — up to 75%, and over 90% of cod and haddock — is exported, which is facilitated by high world prices and the low rate of the Russian ruble. Even when Russian fish is sold abroad at a discount, fish producers prefer to export it. In the absence of incentives for the supply of fishery products to the domestic market, it is advisable to increase the legal status of the Doctrine and make the implementation of its recommendations mandatory, or change the rules for allocating fishing resources to economic entities. The economic availability of cut fish for the population of the producing region — the Murmansk Oblast — is lower than the Russian average. There is a declining level of consumption. This is a consequence of the use of high prices set in the domestic market in a non-market way. There is a need to establish prices for fish products on the domestic market using auctions or exchange trading.

The non-transferability of a designated confirmer signature scheme allows a signer to control the verification ability of a signature, hence protecting the signer's privacy. However, a designated confirmer signature is insufficient when the secret keys are damaged and incapable of collaborative signature generation. In this paper, we circumvent these limitations by introducing the notion of designated confirmer threshold signature. First, we present a formal security model, then give a generic construction, which utilizes threshold signature schemes, encryption schemes and Σ -protocols. Instantiating this generic construction, we have two specific schemes, based on threshold Schnorr and threshold ECDSA, respectively. We further design two efficient Σ -protocols for efficient proofs. We also implement these schemes, and the experiment results show that our schemes are practical with rich functionalities. Finally, we demonstrate interesting applications for blockchains, such as verifiable asset auctions in blockchain and traditional electronic bidding. [ABSTRACT FROM AUTHOR]

In the assignment problem where multiple heterogeneous indivisible items are assigned to unit-demand bidders, we introduce a novel ascending auction called the Flexible Reporting Ascending Auction to balance the privacy preservation and the speed of the ascending auction. Assuming bidders behave truthfully, it always results in a Walrasian equilibrium, along with the minimum Walrasian equilibrium price vector. If each bidder reports without a contradiction, bidders' truthful behavior forms a Nash equilibrium. Our auction generalizes some well-known ascending auctions in literature, and we also provide simulation results to compare these auctions. • A novel ascending auction provides flexible reporting requirements for bidders. • Balance between privacy preservation and speed of the ascending auction. • Simulations compare our auction with other ascending auctions. [ABSTRACT FROM AUTHOR]

This article introduces the St. Dunstan Illuminated Editions, a set of editions de luxe produced by New York-based publisher George D. Sproul between approximately 1901 and 1903, and attempts to place them within the history of other "deluxe" editions published around the turn of the century. It provides a brief history of the books' publication plan, illuminators, and intended subscribers, as well as a short overview of legal actions during their initial publication phase. It finishes by looking at their shifting financial value since publication as seen through auction outcomes and booksellers' pricing. [ABSTRACT FROM AUTHOR]

Public–private partnerships are an important tool for infrastructure development, but variability in bids can undermine value for money. This study investigated bid variability in India's initial two auction road assets under the Toll-Operate-Transfer (TOT) model. A comparative case study research design was utilized involving an in-depth analysis of the TOT I and TOT II auctions. Data was gathered from project reports, guidelines, news articles, stakeholder interviews, and then thematically analyzed. The analysis revealed substantial bid variability within and between the auctions. The findings showed bid variability within the auctions depended on the information gaps owing to the timing and quality of the information released by the auctioneer, the tender conditions and tender process, potential value enhancement and securitization of assets by the bidders, heterogeneous bidder objectives across asset types, and strategic bidding assumptions. The bid variability between the TOT I and TOT II auctions depended on asset quality, declared reserve price, sociopolitical factors, and the prevailing business conditions. While TOT I road quality and growth prospects encouraged strong bidding, overpricing, shifting business scenarios, and political uncertainty in TOT II states depressed bids below expectations. Results indicated that transparency around asset characteristics and traffic forecasts enables realistic bid assessments to control variability. It can inform bid rationalization and pricing mechanisms for future auctions of public infrastructure. Setting reserve prices reflecting project specifics also helped anchor bid ranges. Attracting larger bidder pools countered the influence of overly optimistic/pessimistic valuations. Gradual expansion into new geographies allowed adaptive policy learning. [ABSTRACT FROM AUTHOR]

This study examines how the strategic selection and design of the first auction item using the "Motion in Mind" and "Potential Reinforcement Energy" frameworks influence auction dynamics, affecting participation and competitiveness. The initial item's selection, based on its starting and expected prices, triggers bidder motivations such as personal expression, artistic appreciation, and status signaling, driven by gameplay incentives, which dictate the auction's competitive pace. Data from major auction houses, including Christie's and Sotheby's, were analyzed using correlation analysis and multivariate regression modeling. The findings indicate that the strategic placement of the initial item significantly impacts auction velocity and the deviation of final auction prices from pre-auction estimates, highlighting the auction's multidimensional nature influenced by economic, psychological, and social factors. The research suggests that effective auction design requires accommodating diverse bidder preferences across financial, personal, and social dimensions. A deep understanding of bidder motivations, precise pricing strategies, and nuanced market analysis is crucial for maintaining participant engagement and optimizing auction success. This paper underscores the transcendence of auctions beyond economic transactions, involving a complex interplay of strategic, self-actualization, and engagement motives, as well as non-economic utilities, reflecting broader human behavior and social interactions. [ABSTRACT FROM AUTHOR]

We examine the effect of expected competition on markups in a random utility model where the number of competing firms may differ across consumers. Firms observe consumers' utility shocks and set prices using personalized pricing. We derive a precise condition under which the expected markup across consumers can be represented by a simple expression involving consumers' expected utility and the expected demand. This delivers a general condition under which greater expected competition is price‐increasing. Whether this condition holds depends on the distribution of utility shocks, consumers' outside option, the expected number of competing firms, and the distribution of the number of firms competing for each consumer. [ABSTRACT FROM AUTHOR]

We investigate the impact of public procurement on business survival. Using Italy as a laboratory, we construct a large‐scale dataset of firms—covering balance‐sheet, income‐statement, and administrative records—and match it with public contract data. Employing a regression discontinuity design for close‐call auctions, we find that winners are more likely to stay in the market than marginal losers after the award and that the boost in survival chances lasts longer than the contract duration. We document that this effect is associated with earnings substitution rather than increased business scale and that survivors experience no productivity premium. Securing contracts relaxes credit constraints and acts as a mechanism for survival. [ABSTRACT FROM AUTHOR]

In this article I analyze the effect of the sensitivity of firm value on the information available to potential acquirers in common‐value takeover auctions with toeholds. I show that the quality of information does not affect equilibrium when bidders have equal toeholds but has a significant effect when toeholds are different. My article demonstrates that increasing the relative information quality of the bidder with a smaller toehold makes both bidders bid more aggressively and leads to a higher price. I also analyze the combined effect of toeholds and information quality on equilibrium bidding strategies and discuss ways target shareholders can increase the expected final price. [ABSTRACT FROM AUTHOR]

The paper describes an auction for selling vaccines in a pandemic. The environment borrows from the problem of allocating positions for sponsored links on web pages with search results but recognizes the externalities that one man's vaccination imposes on another. The auction is the pivot Vickrey–Clarke–Groves mechanism and, so, inherits its properties: efficiency and strategy-proofness. Crucially, the auction is designed to let each bidder bid not only on his own behalf but also on behalf of others. The auction requires neither the bidders nor the auctioneer to forecast the efficacy of the vaccine or the evolution of the pandemic. [ABSTRACT FROM AUTHOR]

In this paper, we investigate whether greedy algorithms, traditionally used for pedestrian-based crowdsensing, remain effective in the context of vehicular crowdsensing (VCS). Vehicular crowdsensing leverages vehicles equipped with sensors to gather and transmit data to address several urban challenges. Despite its potential, VCS faces issues with user engagement due to inadequate incentives and privacy concerns. In this paper, we use a dynamic incentive mechanism based on a recurrent reverse auction model, incorporating vehicular mobility patterns and realistic urban scenarios using the Simulation of Urban Mobility (SUMO) traffic simulator and OpenStreetMap (OSM). By selecting a representative subset of vehicles based on their locations within a fixed budget, our mechanism aims to improve coverage and reduce data redundancy. We evaluate the applicability of successful participatory sensing approaches designed for pedestrian data and demonstrate their limitations when applied to VCS. This research provides insights into adapting greedy algorithms for the particular dynamics of vehicular crowdsensing. [ABSTRACT FROM AUTHOR]

Adversarial Risk Analysis (ARA) can be a more realistic and practical alternative to traditional game theoretic or decision theoretic approaches for modeling strategic decision‐making in the presence of an adversary. ARA relies on quantifying the decision‐maker's (DM's) uncertainties about the adversary's strategic thinking, choices and utilities via probability distributions to identify the optimal solution for the DM. ARA solution will be sensitive to the choices of prior distributions used for modelling the expert beliefs. Yet, to date, no mathematical results to characterize the robustness of the ARA solution to the misspecification of one or more prior distributions exist. Prior elicitation is known to be challenging. We present the very first mathematical results on the global robustness of the ARA solution. We use the distorted band class of priors and establish the conditions under which an ordering on the ARA solution can be established when modelling the first‐price sealed‐bid auctions using the nonstrategic play and level‐k thinking solution concepts. We illustrate these results using numerical examples and discuss further areas of research. [ABSTRACT FROM AUTHOR]

A frontrunning attack is one of the most common attacks on smart contracts. Its essence is in manipulating the order of transaction inclusion in a block to gain an advantage by altering the transaction processing sequence. This attack poses a particular threat to the conduct of p2p auctions for "green" energy trading. In this article, different types of such attacks are considered, analyzed, and formalized in step-by-step execution algorithms. Next, a model is proposed to estimate the probability of success of such an attack. An explicit formula is derived for the probability of success of a displacement attack and an insertion attack, which are particular cases of a frontrunning attack. The probability of success is shown to depend on network parameters and the ratio between the transaction fees created by an honest and a malicious users. Numerical examples of practical applications of the derived formula are provided, further confirming the correctness of the analytical results. [ABSTRACT FROM AUTHOR]

Our primary aim is to examine the relationship between the implementation of policies aimed at the privatization of public land and real estate and the manner in which real estate financialization processes unfold. To this end, we focus on the case of Argentina between 2015 and 2019, when the national government promoted the sale of substantial amounts of land located in the City of Buenos Aires. Our central argument posits that the auctions acted as catalysts for the real estate financialization process in two distinct ways. First, the government succeeded in transferring portions of the land to financial agents or under increasingly financialized logics. Second, in the dynamics involved in executing the auctions, the agencies and officials responsible for this task adopted discourses, valuation methods, and practices typical of the financial sector, integrating into networks where these agents operate and designing real estate products that would be attractive to them. Methodologically, we employ a mixed-methods approach, combining quantitative and qualitative techniques. This includes an analysis of regulations governing the auctions, records of trips and meetings held by public officials, and sectoral analyses of the companies that purchased land. Additionally, we conducted in-depth interviews with high-ranking political officials. [ABSTRACT FROM AUTHOR]