Online Auctions

The economic literature on online auctions is rapidly growing because of the enormous amount of freely available field data. Moreover, numerous innovations in auction-design features on platforms such as eBay have created excellent research opportunities. In this article, we survey the theoretical, empirical, and experimental research on bidder strategies (including the timing of bids and winner's-curse effects) and seller strategies (including reserve-price policies and the use of buy-now options) in online auctions, as well as some of the literature dealing with online-auction design (including stopping rules and multi-object pricing rules).

Buy-It-Now prices in eBay Auctions - The Field in the Lab

Electronic commerce has grown extraordinarily over the years, with online auctions being extremely successful forms of trade. Those auctions come in a variety of different formats, such as the Buy-It-Now auction format on eBay, that allows sellers to post prices at which buyers can purchase a good prior to the auction. Even though, buyer behavior is well studied in Buy-It-Now auctions, as to this point little is known about how sellers set Buy-It-Now prices. We investigate into this question by analyzing seller behavior in Buy-It-Now auctions. More precisely, we combine the use of a real online auction market (the eBay platform and eBay traders) with the techniques of lab experiments. We find a striking link between the information about agents provided by the eBay market institution and their behavior. Information about buyers is correlated with their deviation from true value bidding. Sellers respond strategically to this information when deciding on their Buy-It-Now prices. Thus, our results highlight potential economic consequences of information publicly available in (online) market institutions

How eBay Sellers set “Buy-it-now” prices - Bringing The Field Into the Lab

In this paper we introduce a new type of experiment that combines the advantages of lab and field experiments. The experiment is conducted in the lab but using an unchanged market environment from the real world. Moreover, a subset of the standard subject pool is used, containing those subjects who have experience in conducting transactions in that market environment. This guarantees the test of the theoretical predictions in a highly controlled environment and at the same time enables not to miss the specific features of economic behavior exhibited in the field. We apply the proposed type of experiment to study seller behavior in online auctions with a Buy-It-Now feature, where early potential bidders have the opportunity to accept a posted price offer from the seller before the start of the auction. Bringing the field into the lab, we invited eBay buyers and sellers into the lab to participate in a series of auctions on the eBay platform. We investigate how traders' experience in a real market environment influences their behavior in the lab and whether abstract lab experiments bias subjects' behavior

How eBay Sellers set “Buy-it-now†prices - Bringing The Field Into the Lab

In this paper we introduce a new type of experiment that combines the advantages of lab and field experiments. The experiment is conducted in the lab but using an unchanged market environment from the real world. Moreover, a subset of the standard subject pool is used, containing those subjects who have experience in conducting transactions in that market environment. This guarantees the test of the theoretical predictions in a highly controlled environment and at the same time enables not to miss the specific features of economic behavior exhibited in the field. We apply the proposed type of experiment to study seller behavior in online auctions with a Buy-It-Now feature, where early potential bidders have the opportunity to accept a posted price offer from the seller before the start of the auction. Bringing the field into the lab, we invited eBay buyers and sellers into the lab to participate in a series of auctions on the eBay platform. We investigate how traders' experience in a real market environment influences their behavior in the lab and whether abstract lab experiments bias subjects' behavior.online auctions; experiments; buyout prices

Optimal takeover contests with toeholds

This paper characterizes how a target firm should be sold when the possible buyers (bidders) have prior stakes in its ownership (toeholds). We find that the optimal mechanism needs to be implemented by a non-standard auction which imposes a bias against bidders with high toeholds. This discriminatory procedure is such that the target´s average sale price is increasing in both the size of the common toehold and the degree of asymmetry in these stakes. It is also shown that a simple mechanism of sequential negotiation replicates the main properties of the optimal procedure and yields a higher average selling price than the standard auctions commonly used in takeover battles

Buy-It-Now prices in eBay Auctions - The Field in the Lab

Electronic commerce has grown extraordinarily over the years, with online auctions being extremely successful forms of trade. Those auctions come in a variety of different formats, such as the Buy-It-Now auction format on eBay, that allows sellers to post prices at which buyers can purchase a good prior to the auction. Even though, buyer behavior is well studied in Buy-It-Now auctions, as to this point little is known about how sellers set Buy-It-Now prices. We investigate into this question by analyzing seller behavior in Buy-It-Now auctions. More precisely, we combine the use of a real online auction market (the eBay platform and eBay traders) with the techniques of lab experiments. We find a striking link between the information about agents provided by the eBay market institution and their behavior. Information about buyers is correlated with their deviation from true value bidding. Sellers respond strategically to this information when deciding on their Buy-It-Now prices. Thus, our results highlight potential economic consequences of information publicly available in (online) market institutions.electronic markets; experience; online auctions; BIN price; buyout

Buy-it-Now Prices in eBay Auctions-The Field in the Lab

This article is an experimental investigation on decision making in online auction markets. We focus on a widely used format, the Buy-It-Now auction on eBay, where sellers post prices at which buyers can purchase a good prior to an auction. Even though, buyer behavior is well studied in Buy-It-Now auctions, up to date little is known about the behavior of sellers. In this article, we study how sellers set Buy-It-Now prices by combining the use of a real online auction market (the eBay platform and eBay traders) with the techniques of lab experiments. We find a striking relation between information about agents provided by eBay and their behavior. Information about buyers is correlated with their deviation from true value bidding. Sellers respond strategically to this information when deciding on their Buy-It-Now prices. Our results highlight consequences of information publicly available in (online) markets and underline the crucial role of institutional details.Electronic markets, experience, online auctions, BIN price, buyout price, risk, single item auction, private value, experiment

A Grey-Box Approach to Automated Mechanism Design

Auctions play an important role in electronic commerce, and have been used to solve problems in distributed computing. Automated approaches to designing effective auction mechanisms are helpful in reducing the burden of traditional game theoretic, analytic approaches and in searching through the large space of possible auction mechanisms. This paper presents an approach to automated mechanism design (AMD) in the domain of double auctions. We describe a novel parametrized space of double auctions, and then introduce an evolutionary search method that searches this space of parameters. The approach evaluates auction mechanisms using the framework of the TAC Market Design Game and relates the performance of the markets in that game to their constituent parts using reinforcement learning. Experiments show that the strongest mechanisms we found using this approach not only win the Market Design Game against known, strong opponents, but also exhibit desirable economic properties when they run in isolation.Comment: 18 pages, 2 figures, 2 tables, and 1 algorithm. Extended abstract to appear in the proceedings of AAMAS'201