Auction Setting Research Articles

Information Disclosure by Informed Intermediary in Double Auction

Abstract In this paper, I study the role of an informed intermediary in improving market efficiency through communication with a buyer and a seller. Based on a double auction setting, I introduce an agent who is partially informed about the private information of a buyer and a seller. The agent can disclose information to either both parties, one party, or none of them. I compare the two most common incentives of the agent, maximizing either trade probability or expected price, in information disclosure. The analysis shows that the former incentive improves efficiency more than the latter; the agent with the former always discloses all relevant information, resulting in not only a higher trade probability but also a higher expected price. The buyer and the seller mostly prefer the agent disclosing information to both parties. When the agent discloses information to only one party, the buyer and the seller desire to be the one who receives the information exclusively. They sometimes prefer the agent disclosing information exclusively to the other party over no information for all. The findings of this paper have important implications for the design of compensation schemes for intermediaries who play a significant role as advisors to market participants.

Returns from herding as a function of the social network of agents: a simulation study

PurposeThis study combines different perspectives on herding, viewing it as a social network heuristic in comparison to other heuristics. The purpose is to use the heuristic view of herding as found in early literature and test it on grounds of efficiency and payoff, in essence, combining the heuristic and rational agent view of herding. The simulated double auction setting includes agents embedded in a social network, allowing for an examination of herding alongside rational behaviour and imperfect signals.Design/methodology/approachIn each round of the simulation, levels of homophily, density and fractions of types of agents is set and agents are allowed to follow their respective heuristics under those conditions. Characteristics of the social network, such as the size, levels of different homophilies, density and fractions of different types of agents are varied randomly to gauge their effect on the performance of herders vis-à-vis others and the overall market efficiency through simulation based approach. The data used for the study has been developed in Python and linear models are estimated using R.FindingsHerding decreases total surplus in private value double auctions, but herders are not worse off than other agents and perform equally in common value auctions. Further, herders and random offerers reduce payoffs of other agents as well, and herding effects the surplus per transaction and not the quantum.Research limitations/implicationsThis study explores herding as a strategic behaviour coexisting with rationality and other strategies in specific circumstances. It presents intriguing findings on the impact of herding on individual outcomes and market efficiency, raising new avenues for future research. Implication to research includes a dent on the “sieve” argument of markets rooting out irrationality and from it, a policy implication that follows is the need for corrective measures as markets cannot self-correct this, given herders do not perform worse than others.Originality/valueThe study links the phenomenon of herding to the dynamics of social networks and heuristic-based learning mechanisms that sets apart this research from the majority of existing literature, which predominantly conceptualizes herding as an outcome derived from a perfect Bayesian Equilibrium and a rational learning process.

Learning Coalition Structures with Games

Coalitions naturally exist in many real-world systems involving multiple decision makers such as ridesharing, security, and online ad auctions, but the coalition structure among the agents is often unknown. We propose and study an important yet previously overseen problem -- Coalition Structure Learning (CSL), where we aim to carefully design a series of games for the agents and infer the underlying coalition structure by observing their interactions in those games. We establish a lower bound on the sample complexity -- defined as the number of games needed to learn the structure -- of any algorithms for CSL and propose the Iterative Grouping (IG) algorithm for designing normal-form games to achieve the lower bound. We show that IG can be extended to other succinct games such as congestion games and graphical games. Moreover, we solve CSL in a more restrictive and practical setting: auctions. We show a variant of IG to solve CSL in the auction setting even if we cannot design the bidder valuations. Finally, we conduct experiments to evaluate IG in the auction setting and the results align with our theoretical analysis.

The Impact of Partial Production Capacity Sharing via Production as a Service

Cloud manufacturing, one of the trends subsumed under Industry 4.0, influences industrial production settings as it transforms resource utilization into a shared on-demand service. Central to cloud manufacturing is the Production as a Service (PaaS) paradigm, which allows intermediaries to coordinate supply and demand of idle production capacities and enables collaboration in production through outsourcing. Current practical implementations of PaaS use multiple steps to connect buyers and suppliers through an intermediary that treats each demand request individually and subsequently contacts a pool of suppliers. In contrast, we study a novel optimization-based PaaS approach and develop a combinatorial exchange model for production capacity sharing among multiple suppliers and buyers. To this end, we adapt the winner determination problem from procurement auction settings. We find that key performance indicators such as matched capacity, overall payment, and unfulfilled minimum demand depend on both the employed bidding behavior and the buyer and supplier ratio. We additionally develop a corresponding online counterpart that can be readily implemented in practice and show that it yields close to optimal solutions. Finally, we show that our solution approach increases the efficiency of matching idle production capacities on average by 39% and also increases the average supplier payoff by up to 74% compared to a practice-based benchmark heuristic.

A Cost-Aware Utility-Maximizing Bidding Strategy for Auction-Based Federated Learning.

Auction-based federated learning (AFL) has emerged as an efficient and fair approach to incentivize data owners (DOs) to contribute to federated model training, garnering extensive interest. However, the important problem of helping data consumers (DCs) bid for DOs in competitive AFL settings remains open. Existing work simply treats that the actual cost paid by a winning DC (i.e., the bid cost) is equal to the bid price offered by that DC itself. However, this assumption is inconsistent with the widely adopted generalized second-price (GSP) auction mechanism used in AFL, including in these existing works. Under a GSP auction, the winning DC does not pay its own proposed bid price. Instead, the bid cost for the winner is determined by the second-highest bid price among all participating DCs. To address this limitation, we propose a first-of-its-kind federated cost-aware bidding strategy () to help DCs maximize their utility under GSP auction-based federated learning (FL). It enables DCs to efficiently bid for DOs in competitive AFL markets, maximizing their utility and improving the resulting FL model accuracy. We first formulate the optimal bidding function under the GSP auction setting, and then demonstrate that it depends on utility estimation and market price modeling, which are interrelated. Based on this analysis, jointly optimizes in a novel end-to-end framework, and then executes the proposed return on investment (ROI)-based method to determine the optimal bid price for each piece of the data resource. Through extensive experiments on six commonly adopted benchmark datasets, we show that outperforms eight state-of-the-art methods, beating the best baseline by 4.39%, 4.56%, 1.33%, and 5.43% on average in terms of the total amount of data obtained, number of data samples per unit cost, total utility, and FL model accuracy, respectively.

Internalizing match-dependent externalities

External effects can be triggered through trade and depend on the locations of buyers and sellers who are matched. Inspired by electricity markets, we experimentally investigate markets in which net trades between two locations induce social costs. Based on a modified double auction setting, we compare the performance of market platforms that are location-blind with those where information on the location of (potential) trading partners or the level of the externality is given. We demonstrate that locational information is sufficient to reduce the externality. Imposing the full external costs on individual trades leads to maximal price differentiation between locations and further reduces net trades, while welfare improvements are limited. Reasons for not achieving the typically high efficiency of double auctions are discussed.

Social media and nonprofit fundraising: the influence of Facebook likes

PurposeThis paper aims to demonstrate that Facebook likes affect outcomes in nonprofit settings. Specifically, Facebook likes influence affinity to nonprofits, which, in turn, affects fundraising outcomes.Design/methodology/approachThe authors report three studies that establish that relationship. To examine social contagion, Study 1 – an auction field study – relies on selling artwork created by underprivileged youth. To isolate signaling, Study 2 manipulates the number of total Facebook likes on a page. To isolate commitment escalation, Study 3 manipulates whether a participant clicks a Facebook like.FindingsThe results show that Facebook likes increase willingness to contribute in nonprofit settings and that the process goes through affinity, as well as through Facebook impressions and bidding intensity. The total number of Facebook likes has a direct signaling effect and an indirect social contagion effect.Research limitations/implicationsThe effectiveness of the proposed mechanisms is limited to nonprofit settings and only applies to short-term effects.Practical implicationsFacebook likes serve as both a quality signal and a commitment mechanism. The magnitude of commitment escalation is larger, and the relationship is moderated by familiarity with the organization. Managers should target Facebook likes at those less familiar with the organization and should prioritize getting a potential donor to leave a like as a step leading to donation, in essence mapping a donor journey from prospective to active, where Facebook likes play an essential role in the journey. In a charity auction setting, the donor journey involves an additional step of bidder intensity.Social implicationsThe approach the authors study is shown effective in nonprofit settings but does not appear to extend to corporate social responsibility more broadly.Originality/valueTo the best of the authors’ knowledge, this study is the first investigation to map Facebook likes to a seller’s journey through signals and commitment, as well as the only investigation to map Facebook likes to charity auctions and show the effectiveness of this in the field.

Information design in optimal auctions

We study the information design problem in a single-unit auction setting. The information designer controls independent private signals according to which the buyers infer their binary private values. Assuming that the seller adopts the optimal auction due to Myerson (1981) in response, we characterize both the buyer-optimal information structure, which maximizes the buyers' surplus, and the seller-worst information structure, which minimizes the seller's revenue. We translate both information design problems into finite-dimensional, constrained optimization problems in which one can explicitly solve for the optimal information structure. In contrast to the case with one buyer (Roesler and Szentes, 2017), we show that with two or more buyers, the symmetric buyer-optimal information structure is different from the symmetric seller-worst information structure. The good is always sold under the seller-worst information structure but not under the buyer-optimal information structure. Nevertheless, as the number of buyers goes to infinity, both symmetric information structures converge to no disclosure. We also show that in our ex ante symmetric setting, an asymmetric information structure is never seller-worst but can generate a strictly higher surplus for the buyers than the symmetric buyer-optimal information structure.

Influence of the At-Arrival Host Transcriptome on Bovine Respiratory Disease Incidence during Backgrounding

Bovine respiratory disease (BRD) remains the leading disease within the U.S. beef cattle industry. Marketing decisions made prior to backgrounding may shift BRD incidence into a different phase of production, and the importance of host gene expression on BRD incidence as it relates to marketing strategy is poorly understood. Our objective was to compare the influence of marketing on host transcriptomes measured on arrival at a backgrounding facility on the subsequent probability of being treated for BRD during a 45-day backgrounding phase. This study, through RNA-Seq analysis of blood samples collected on arrival, evaluated gene expression differences between cattle which experienced a commercial auction setting (AUCTION) versus cattle directly shipped to backgrounding from the cow-calf phase (DIRECT); further analyses were conducted to determine differentially expressed genes (DEGs) between cattle which remained clinically healthy during backgrounding (HEALTHY) versus those that required treatment for clinical BRD within 45 days of arrival (BRD). A profound difference in DEGs (n = 2961) was identified between AUCTION cattle compared to DIRECT cattle, regardless of BRD development; these DEGs encoded for proteins involved in antiviral defense (increased in AUCTION), cell growth regulation (decreased in AUCTION), and inflammatory mediation (decreased in AUCTION). Nine and four DEGs were identified between BRD and HEALTHY cohorts in the AUCTION and DIRECT groups, respectively; DEGs between disease cohorts in the AUCTION group encoded for proteins involved in collagen synthesis and platelet aggregation (increased in HEALTHY). Our work demonstrates the clear influence marketing has on host expression and identified genes and mechanisms which may predict BRD risk.

Authorship as a determinant of art prices and auction settings in eighteenth-century Paris

Abstract In the context of a booming art market in Paris, eighteenth-century art dealers began to exploit authorship as a value-enhancing strategy. Using Jean-Baptiste Pierre Lebrun’s business as a case study, we show that art dealers purposefully used a firm scale of authentication to create product differentiation and to boost auction dynamics and revenues by reordering the lots before the sale in leaflets known as feuilles de vacation. Our empirical findings support the hypothesis of the development of a market driven by the quest for the artist’s hand in pre-revolutionary Paris, with differential use of connoisseurial knowledge, depending on buyers’ profiles.

Revenue-Optimal Deterministic Auctions for Multiple Buyers with Ordinal Preferences over Fixed-Price Items

In this article, we introduce a Bayesian revenue-maximizing mechanism design model where the items have fixed, exogenously given prices. Buyers are unit-demand and have an ordinal ranking over purchasing either one of these items at its given price or purchasing nothing. This model arises naturally from the assortment optimization problem, in that the single-buyer optimization problem over deterministic mechanisms reduces to deciding on an assortment of items to “show.” We study its multi-buyer generalization in the simplest setting of single-winner auctions or, more broadly, any service-constrained environment. Our main result is that if the buyer rankings are drawn independently from Markov chain choice models, then the optimal mechanism is computationally tractable, and structurally a virtual welfare maximizer. We also show that for ranking distributions not induced by Markov chains, the optimal mechanism may not be a virtual welfare maximizer. Finally, we apply our virtual valuation notion for Markov chains, in conjunction with existing prophet inequalities, to improve algorithmic guarantees for online assortment problems.

Evolution of a dealer trading network and its effects on art auction prices

We use a unique historical data set (1741-1913) of all London-based art auctions to capture the role of information acquisition in the development of a trading network for dealers and its effect on artwork prices. We explore how the network size, depth of interactions, and similarities in art specialization between trading partners influence the decision to form new links. We find that, a larger and deeper trading network exacerbates informational asymmetries among buyers, leading to a price advantage. We show that information transmission through a network of experienced dealers is important in determining the price they pay - a factor that has been overlooked so far in the literature.

The price of actor diversity: Measuring project developers’ willingness to accept risks in renewable energy auctions

Renewable energy (RE) auctions were partly introduced to lower the cost of policy support. However, policymakers are increasingly aware that effective policies require a broader set of objectives that include securing actor diversity and social acceptance. While several countries have introduced measures to incentivize citizen participation in RE projects to meet those objectives, little is known about their effect on the risk perceptions of developers in auction settings. Based on insights from 559 experimental choices made by 61 European project developers, this study investigates risk-return preferences for different onshore wind energy auction designs. We show that developers are willing to accept the requirement to submit bid bonds up to a certain threshold, and that a 36-month realisation period is preferred overall. While developers may be open to citizen co-investment, the risk premiums they require increase with the share of capital offered to local residents in auction settings, highlighting an apparent trade-off between promoting actor diversity and low-cost RE deployment. Policymakers unwilling to compensate developers for the price of increasing actor diversity may have to find other ways to promote citizen participation in order to achieve RE objectives, as this factor may have important implications for the acceptance of RE deployment.

In the basic auction model, the optimal reserve price may depend on the number of bidders

A widespread claim in the auction literature about the most well known auction setting with i.i.d. private values is that the optimal reserve price for a second price auction is independent of the number n of bidders. This is indeed the case if the virtual valuation function is increasing, but this result fails to hold if the virtual valuation is non-monotone. In such case the optimal reserve price is weakly increasing in n and, as n tends to infinity, it tends to the highest regular valuation with zero virtual value.

Excessive Competition and Supplier Non‐Performance Risk: Trade‐offs in Reverse Auctions

Supplier non‐performance is an ever present problem for an original equipment manufacturer (OEM) buyer and past survey‐based research has shown that increasing supplier competition adversely affects their performance. Using a reverse auction setting in which supplier competition is determined by number ( n) of participating suppliers (bidders), we show how buyer's exposure to supplier non‐performance risk is influenced by n. In particular, we characterize conditions under which supplier non‐performance risk is increasing in n. We then formulate a buyer's decision on the optimal n for uniformly distributed costs that allows it to resolve its cost vs. risk trade‐off. We further investigate other strategies like dual sourcing, better screening, and lower threshold on acceptable bids, that the buyer can use, together with limiting n, to limit non‐performance risk and control its overall procurement cost. Managerially, our findings indicate that an OEM buyer procuring non‐strategic (non‐critical) components could increase its exposure to supplier non‐performance risk by leveraging too much competition, especially among small entrant suppliers. In such situations a buyer can manage its input cost and risk by limiting supplier competition.

Detecting bid-rigging coalitions in different countries and auction formats

We propose an original application of screening methods using machine learning to detect collusive groups of firms in procurement auctions. As a methodical innovation, we calculate coalition-based screens by forming coalitions of bidders in tenders to flag bid-rigging cartels. Using Swiss, Japanese and Italian procurement data, we investigate the effectiveness of our method in different countries and auction settings, in our cases first-price sealed-bid and mean-price sealed-bid auctions. We correctly classify 90% of the collusive and competitive coalitions when applying four machine learning algorithms: lasso, support vector machine, random forest, and super learner ensemble method. Finally, we find that coalition-based screens for the variance and the uniformity of bids are in all the cases the most important predictors according to the random forest.

Influencing Factor Analysis of the Hammer Price of Yellow Diamond Online Auction: A Case Study of Ali Auction Hammer Price

In this paper, the factors affecting the hammer price of yellow diamonds were explored and analyzed through the auction data of yellow diamonds from July to September 2019 of Ali Auction. After constructing a five-factor model including auction setting, item description, commodity attribution, buyer competition and bidding strategy, the relevant hypothesis was put forward, and the empirical test was carried out by using correlation analysis and multiple linear regression analysis. It was found that the mark-up range in auction setting, the weight and colour grade of the diamonds in commodity attribute, the bidding times in buyer competition and the jumping bid in bidding strategy all have significant effects on the hammer price. Other factors such as margin, the number of pictures, the status of the end of the auction, and the number of applicants etc. have no significant impact on the hammer price.

Strongly budget balanced auctions for multi-sided markets

In two-sided markets, Myerson and Satterthwaite's impossibility theorem states that one can not maximize the gain-from-trade while also satisfying truthfulness, individual-rationality and no deficit. Attempts have been made to circumvent Myerson and Satterthwaite's result by attaining approximately-maximum gain-from-trade: the double-sided auction of McAfee (1992) [35] is truthful and has no deficit — it is weakly-budget-balanced, and the one by Segal-Halevi et al.'s (2016) [38] additionally has no surplus — it is strongly-budget-balanced. They consider two categories of agents — buyers and sellers, where each trade set is composed of a single buyer and a single seller.The practical complexity of applications in areas such as supply chain requires one to look beyond two-sided markets. Common requirements are for: buyers trading with multiple sellers of different or identical items, buyers trading with sellers through transporters and mediators, and sellers trading with multiple buyers. We attempt to address these settings.We generalize Segal-Halevi et al. (2016)'s [38] strongly-budget-balanced double-sided auction setting to a multilateral market where each trade set is composed of any number of agent categories. Our generalization refines the notion of competition in multi-sided auctions by introducing the concepts of external competition and trade reduction. We also show an obviously-truthful implementation of our auction using multiple ascending prices.

Auctioneers sometimes prefer entry fees to extra bidders

We investigate a market thickness–market power tradeoff in an auction setting with endogenous entry. We find that charging admission fees can sometimes dominate the benefit of recruiting additional bidders, even though the fees themselves implicitly reduce competition at the auction stage. We also highlight that admission fees and reserve prices are different instruments in a setting with uncertainty over entry costs, and that optimal mechanisms in such settings may be more complex than simply setting a reserve price. Our results provide a counterpoint to the broad intuition of Bulow and Klemperer (1996) that market thickness often takes precedence over market power in auction design.

Linear Program-Based Approximation for Personalized Reserve Prices

We study the problem of computing data-driven personalized reserve prices in eager second price auctions without having any assumption on valuation distributions. Here, the input is a data set that contains the submitted bids of n buyers in a set of auctions, and the problem is to return personalized reserve prices r that maximize the revenue earned on these auctions by running eager second price auctions with reserve r. For this problem, which is known to be NP complete, we present a novel linear program (LP) formulation and a rounding procedure, which achieves a 0.684 approximation. This improves over the [Formula: see text]-approximation algorithm from Roughgarden and Wang. We show that our analysis is tight for this rounding procedure. We also bound the integrality gap of the LP, which shows that it is impossible to design an algorithm that yields an approximation factor larger than 0.828 with respect to this LP. This paper was accepted by Chung Piaw Teo, Management Science Special Section on Data-Driven Prescriptive Analytics.