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

Learning optimization models in the presence of unknown relations

In a sequential auction with multiple bidding agents, it is highly challenging to determine the ordering of the items to sell in order to maximize the revenue due to the fact that the autonomy and private information of the agents heavily influence the outcome of the auction. The main contribution of this paper is two-fold. First, we demonstrate how to apply machine learning techniques to solve the optimal ordering problem in sequential auctions. We learn regression models from historical auctions, which are subsequently used to predict the expected value of orderings for new auctions. Given the learned models, we propose two types of optimization methods: a black-box best-first search approach, and a novel white-box approach that maps learned models to integer linear programs (ILP) which can then be solved by any ILP-solver. Although the studied auction design problem is hard, our proposed optimization methods obtain good orderings with high revenues. Our second main contribution is the insight that the internal structure of regression models can be efficiently evaluated inside an ILP solver for optimization purposes. To this end, we provide efficient encodings of regression trees and linear regression models as ILP constraints. This new way of using learned models for optimization is promising. As the experimental results show, it significantly outperforms the black-box best-first search in nearly all settings.Comment: 37 pages. Working pape

Trust and Experience in Online Auctions

This paper aims to shed light on the complexities and difficulties in predicting the effects of trust and the experience of online auction participants on bid levels in online auctions. To provide some insights into learning by bidders, a field study was conducted first to examine auction and bidder characteristics from eBay auctions of rare coins. We proposed that such learning is partly because of institutional-based trust. Data were then gathered from 453 participants in an online experiment and survey, and a structural equation model was used to analyze the results. This paper reveals that experience has a nonmonotonic effect on the levels of online auction bids. Contrary to previous research on traditional auctions, as online auction bidders gain more experience, their level of institutional-based trust increases and leads to higher bid levels. Data also show that both a bidder’s selling and bidding experiences increase bid levels, with the selling experience having a somewhat stronger effect. This paper offers an in-depth study that examines the effects of experience and learning and bid levels in online auctions. We postulate this learning is because of institutional-based trust. Although personal trust in sellers has received a significant amount of research attention, this paper addresses an important gap in the literature by focusing on institutional-based trust

An investigation of the trading agent competition : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Computer Science at Massey University, Albany, New Zealand

The Internet has swept over the whole world. It is influencing almost every aspect of society. The blooming of electronic commerce on the back of the Internet further increases globalisation and free trade. However, the Internet will never reach its full potential as a new electronic media or marketplace unless agents are developed. The trading Agent Competition (TAC), which simulates online auctions, was designed to create a standard problem in the complex domain of electronic marketplaces and to inspire researchers from all over the world to develop distinctive software agents to a common exercise. In this thesis, a detailed study of intelligent software agents and a comprehensive investigation of the Trading Agent Competition will be presented. The design of the Risker Wise agent and a fuzzy logic system predicting the bid increase of the hotel auction in the TAC game will be discussed in detail

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

An Experimental Assessment of Confederate Reserve Price Bids in Online Auction

internet auctions, bid shilling, reserve price, internet fraud, market design

Tractors on eBay: Differences between Internet and In-Person Auctions

Internet auction platforms are changing the face of transactions in many business sectors, including agriculture. We provide one of the first systematic examinations of the differences between internet and in-person auctions in agricultural input markets. A hedonic model estimated with used tractor transactions from Midwestern sellers pooled between eBay and in-person auctions reveals statistically distinct price surfaces for the two auction venues and predicts significantly lower prices for comparable equipment sold on eBay, though this difference is attenuated for tractors fully covered by eBay's buyer protection program and is fully absent for the most frequently traded tractor. An endogenous venue-selection model reveals that larger, more-valuable tractors are less likely to be offered on eBay, a choice that should enhance seller revenues. Furthermore, sellers in states with more valuable stocks of machinery, more frequent tractor sales, and a lower propensity to use the internet for agricultural marketing are more likely to offer tractors for sale via in-person auctions than on eBay.auctions, electronic commerce, eBay, farm equipment, hedonic models, Marketing, D44, Q13,