Ordering competition: the interactional accomplishment of the sale of art and antiques at auction

Auctions provide an institutional solution to a social problem; they enable the legitimate pricing and exchange of goods where those goods are of uncertain value. In turn, auctions raise a number of social and organizational issues that are resolved within the interaction that arise in sales by auction. In this paper, we examine sales of fine art, antiques and objets d'art and explore the ways in which auctioneers mediate competition between buyers and establish a value for goods. In particular, we explore how bids are elicited, co-ordinated and revealed so as to rapidly escalate the price of goods in a transparent manner that enables the legitimate valuation and exchange of goods. In directing attention towards the significance of the social interaction, including talk, visual and material conduct, the paper contributes to the growing corpus of ethnographic studies of markets. It suggests that to understand the operation of markets and their outcomes, and to unpack issues of agency, trust and practice, we need to place the 'interaction order' at the heart of analytic agenda.     

Endogenous entry in auctions with negative externalities

In this paper, we study the auction to allocate an indivisible good when each potential buyer has a private and independent valuation for the item and suffers a negative externality if a competitor acquires it. In that case, the outside option of each buyer is mechanism-dependent, which implies that participation is endogenous. As several works in the literature have shown, the optimal auction entails strong threats to induce full entry and maximal expected revenue. This results from the full commitment assumption, which ensures that threats are credible. We show that absent credible threats, the entry process does not lead to full participation: the equilibrium entails screening of agents in the entry stage and a trade-off between reserve prices and entry fees. Besides, we discuss the conditions under which the impossibility to use threats does not prevent the seller from ensuring a minimal screening and reaching a high expected revenue.     

Viability of Auction‐Based Revenue Management in Sequential Markets

The Internet is providing an opportunity to revenue management practitioners to exploit the potential of auctions as a new price distribution channel. We develop a stochastic model for a high‐level abstraction of a revenue management system (RMS) that allows us to understand the potential of incorporating auctions in revenue management in the presence of forecast errors associated with key parameters. Our abstraction is for an environment where two market segments book in sequence and revenue management approaches consider auctions in none, one, or both segments. Key insights from our robust results are (i) limited auctions are best employed closest to the final sale date, (ii) counterbalancing forecast errors associated with overall traffic intensity and the proportion of customer arrivals in a segment is more important if an auction is adopted in that segment, and (iii) it is critically important not to err on the side of overestimating market willingness to pay.     

Interregional Price Difference in the New Orleans Auctions Market for Slaves

Our article investigates the variation of winning bids in slave auctions held in New Orleans from 1804 to 1862. Specifically, we measure the variation in the price of slaves conditional on their geographical origin. Previous work using a regression framework ignored the auction mechanism used to sell slaves. This introduced a bias in the conditional mean of the winning bid because it depended on the number of bidders participating in the auction. Unfortunately, the number of bidders is unobserved by the econometrician.We adopt the standard framework of a symmetric independent private value auction and propose an estimation strategy to attempt to deal with this omitted variable bias. Our estimate of the mean number of bidders doubled from 1804 to 1862. We find the number of bidders had a significant positive effect on the average winning bid. An increase from 20 to 30 bidders in an auction raised the average winning bid by around 10%%. The price variation according to the geographical origin of slaves found in earlier work continues to persist after accounting for the omitted variable. We also find a new result that a considerable premium is paid for slaves originating from New Orleans. However, this price variation disappears once we account for regional productivity differences.     

Design of Auction Parameters to Reduce the Effect of Collusion

Government departments are increasingly turning to auctions to procure goods and services. Collusion among bidders, however, reduces competition and raises winning bid prices. Since conventional collusion control measures based on the redesign of auction mechanisms are less effective in government procurement auctions, there is a need to devise control measures that decrease the effect of collusion. This article demonstrates how the principles of design of experiments can be applied in a system dynamics model to find the auction parameter values that substantially reduce the effect of collusion in government procurement auctions. This research makes a number of contributions. First, it develops a feedback‐based dynamic mechanism of collusion in government procurement auctions. The mechanism proposes the winning bid price as being determined not by the total number of bidders but by the number of independent bidders. It defines each cartel as one independent bidder regardless of the number of bidders in the cartel. Second, the mechanism is tested by developing a system dynamics model to government auctions for procuring contracts for roadwork projects in India. Third, the principles of experimental design are applied to find the auction parameter values that ensure high bid participation and low winning price‐to‐reserve price ratios.     

Notes and Comments the Amsterdam Auction

The Amsterdam auction has been used to sell real estate in the Dutch capital for centuries. By awarding a premium to the highest losing bidder, the Amsterdam auction favors weak bidders without having the implementation difficulties of Myerson's (1981) optimal auction. In a series of experiments, we compare the standard first‐price and English auctions, the optimal auction, and two variants of the Amsterdam auction. With strongly asymmetric bidders, the second‐price Amsterdam auction raises substantially more revenues than standard formats and only slightly less than the optimal auction.     

Mechanism Design with Interdependent Valuations: Efficiency

Agents' valuations are interdependent if they depend on the signals, or types, of all agents. Under the implicit assumption that agents cannot observe their outcome‐decision payoffs, previous literature has shown that with interdependent valuations and independent signals, efficient design is impossible. This paper shows that an efficient mechanism exists in an environment where first the final outcome (e.g., allocation of the goods) is determined, then the agents observe their own outcome‐decision payoffs, and then final transfers are made.     

Personality traits and bidding behavior in competing auctions

We study strategic behavior in an “alternating recognition” model of English auctions with competing sellers, which mimics a structure that is common in online marketplaces such as eBay. To relate decision making in our experimental setting to individual differences, we measure subjects’ personality with the Big-Five Trait Taxonomy. Our results suggest that personality has meaningful predictive power in explaining bidding behavior but only for female subjects. Further, females also earn more than males and the gender gap in earnings is large and significant. Finally, personality indirectly affects earnings through the choice of strategies but has no direct effect on earnings, controlling for strategies. This is an important result in that it demonstrates the mechanism through which personality matters in our setting.     

Modeling Reputation as a Time‐Series: Evaluating the Risk of Purchase Decisions on eBay*

Reputation systems based on buyer feedback play an important role in today's online markets. In this article, we provide a rigorous methodology to establish a relationship between a seller's feedback history and risk of default. We validate this method against eBay's reputation system, using a dataset of terminated users (Not‐A‐Registered‐User or NARU) and the feedback left for them by buyers. By treating feedback rating data as a function of time, we characterize the tendency of change in seller feedback ratings in order to predict the behavior of a seller. We find that NARU sellers have significantly more negative feedback in their final weeks. Applying functional principal component analysis and classification tree methods, we find that when projecting the feedback data to an appropriate space, NARU and non‐NARU sellers can be distinguished at better than 92% accuracy. We use this to provide a quantitative mechanism for evaluating the risk of trading with a seller who has less than perfect feedback, and offer advice on how much a buyer should offer to pay, given an asking price on a commodity item and a seller's feedback history.     

Identifying non-competitive bids in construction contract auctions

Construction contract auctions are characterised by (1) anticipated high outliers due to the presence of non-competitive bids, (2) very small samples and (3) uncertainty of the appropriate underlying density function model of the bids. This paper describes the simultaneous identification of high outliers and density function by systematically identifying and removing candidate (high) outliers and examining the composite goodness-of-fit of the resulting reduced samples with the normal and lognormal density functions. Six different identification strategies are tested empirically by application, both independently and in pooled form, to several sets of auction data gathered from around the world. The results indicate the normal density to be the most appropriate model and a multiple of the auction standard deviation to be the best identification strategy.