Coordination of Purchasing and Bidding Activities Across Markets

In both consumer purchasing and industrial procurement, combinatorial interdependencies among the items to be purchased are commonplace. E-commerce compounds the problem by providing more opportunities for switching suppliers at low costs, but also potentially eases the problem by enabling automated market decision-making systems, commonly referred to as trading agents, to make purchasing decisions in an integrated manner across markets. Most of the existing research related to trading agents assumes that there exists a combinatorial market mechanism in which buyers (or sellers) can bid (or sell) service or merchant bundles. Todayâ??s prevailing e-commerce practice, however, does not support this assumption in general and thus limits the practical applicability of these approaches. We are investigating a new approach to deal with the combinatorial interdependency challenges for online markets. This approach relies on existing commercial online market institutions such as posted-price markets and various online auctions that sell single items. It uses trading agents to coordinate a buyerâ??s purchasing and bidding activities across multiple online markets simultaneously to achieve the best overall procurement effectiveness. This paper presents two sets of models related to this approach. The first set of models formalizes optimal purchasing decisions across posted-price markets with fixed transaction costs. Flat shipping costs, a common e-tailing practice, are captured in these models. We observe that making optimal purchasing decisions in this context is NP-hard in the strong sense and suggest several efficient computational methods based on discrete location theory. The second set of models is concerned with the coordination of bidding activities across multiple online auctions. We study the underlying coordination problem for a collection of first or second-price sealed-bid auctions and derive the optimal coordination and bidding policies.

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: A Hybrid Internet Market Institution

This paper analyzes seller choices and outcomes in approximately 700 recent Internet auctions on eBay. The ‘Buy it Now’ option that is available in these auctions allows the seller to supplement or replace the auction with a posted price offer. We use a structural model to control for the endogenous conduct of the auction (e.g., number of bids and bidders) as well as product and seller characteristics. Among other results, we find that the ‘Buy it Now’ option was used more often by sellers with higher ratings (awarded by previous buyers) and sellers offering fewer units; and that posted prices were more prevalent for used items. Sellers obtained higher prices for unused and undamaged items overall, and especially when selling at the ‘Buy it Now’ price.

Pricing and Market Segmentation Using Opaque Selling Mechanisms

In opaque pricing certain characteristics of the product or service are hidden from the consumer until after purchase, transforming a differentiated good into somewhat of a commodity. Opaque pricing has become popular in service pricing as it allows firms to sell their differentiated products at higher prices to regular brand loyal customers while simultaneously selling to non loyal customers at discounted prices. We develop a stylized model of consumer model a monopolist selling a product via three selling channels: a regular full information channel, an opaque posted price channel and an opaque bidding channel where consumers specify the price they are willing to pay. We illustrate the segmentation created by opaque pricing as well as compare optimal revenues and prices for sellers using regular full information channels with those using opaque selling mechanisms in conjunction with regular channels. We also study the segmentation and policy changes induced by capacity constraints

Last Minute Feedback

Feedback mechanisms that allow partners to rate each other after a transaction are considered crucial for the success of anonymous internet trading platforms. We document an asymmetry in the feedback behavior on eBay, propose an explanation based on the micro structure of the feedback mechanism and the time when feedbacks are given, and support this explanation by findings from a large data set. Our analysis implies that the informational content of feedback records is likely to be low. The reason for this is that agents appear to leave feedbacks strategically. Negative feedbacks are given late, in the "last minute," or not given at all, most likely because of the fear of retaliative negative feedback. Conversely, positive feedbacks are given early in order to encourage reciprocation. Towards refining our insights into the observed pattern, we look separately at buyers and sellers, and relate the magnitude of the effects to the trading partners' experience

Coordination of Purchasing and Bidding Activities Across Markets

In both consumer purchasing and industrial procurement, combinatorial interdependencies among the items to be purchased are commonplace. E-commerce compounds the problem by providing more opportunities for switching suppliers at low costs, but also potentially eases the problem by enabling automated market decision-making systems, commonly referred to as trading agents, to make purchasing decisions in an integrated manner across markets. Most of the existing research related to trading agents assumes that there exists a combinatorial market mechanism in which buyers (or sellers) can bid (or sell) service or merchant bundles. Today’s prevailing e-commerce practice, however, does not support this assumption in general and thus limits the practical applicability of these approaches. We are investigating a new approach to deal with the combinatorial interdependency challenges for online markets. This approach relies on existing commercial online market institutions such as posted-price markets and various online auctions that sell single items. It uses trading agents to coordinate a buyer’s purchasing and bidding activities across multiple online markets simultaneously to achieve the best overall procurement effectiveness. This paper presents two sets of models related to this approach. The first set of models formalizes optimal purchasing decisions across posted-price markets with fixed transaction costs. Flat shipping costs, a common e-tailing practice, are captured in these models. We observe that making optimal purchasing decisions in this context is N P-hard in the strong sense and suggest several efficient computational methods based on discrete location theory. The second set of models is concerned with the coordination of bidding activities across multiple online auctions. We study the underlying coordination problem for a collection of firstor second-price sealed-bid auctions and derive the optimal coordination and bidding policies

Why Every Economist Should Learn Some Auction Theory

This is an Invited paper for the World Congress of the Econometric Society held in Seattle in August 2000. We discuss the strong connections between auction theory and "standard" economic theory, and argue that auction-theoretic tools and intuitions can provide useful arguments and insights in a broad range of mainstream economic settings that do not, at first sight, look like auctions. We also discuss some more obvious applications, especially to industrial organization.Auctions, Bidding, Auction Theory, Private Values, Common Values, Mechanism Design, Litigation, Stock Markets, Queues, Financial Crashes, Brand Loyalty, War of Attrition, Bertrand, Perfect Competition, E-Commerce, Spectrum Auctions, Treasury Auctions, Electricity

Simple, Credible, and Approximately-Optimal Auctions

We identify the first static credible mechanism for multi-item additive auctions that achieves a constant factor of the optimal revenue. This is one instance of a more general framework for designing two-part tariff auctions, adapting the duality framework of Cai et al [CDW16]. Given a (not necessarily incentive compatible) auction format $A$ satisfying certain technical conditions, our framework augments the auction with a personalized entry fee for each bidder, which must be paid before the auction can be accessed. These entry fees depend only on the prior distribution of bidder types, and in particular are independent of realized bids. Our framework can be used with many common auction formats, such as simultaneous first-price, simultaneous second-price, and simultaneous all-pay auctions. If all-pay auctions are used, we prove that the resulting mechanism is credible in the sense that the auctioneer cannot benefit by deviating from the stated mechanism after observing agent bids. If second-price auctions are used, we obtain a truthful $O(1)$-approximate mechanism with fixed entry fees that are amenable to tuning via online learning techniques. Our results for first price and all-pay are the first revenue guarantees of non-truthful mechanisms in multi-dimensional environments; an open question in the literature [RST17]

Last Minute Feedback

Feedback mechanisms that allow partners to rate each other after a transaction are considered crucial for the success of anonymous internet trading platforms. We document an asymmetry in the feedback behavior on eBay, propose an explanation based on the micro structure of the feedback mechanism and the time when feedbacks are given, and support this explanation by findings from a large data set. Our analysis implies that the informational content of feedback records is likely to be low. The reason for this is that agents appear to leave feedbacks strategically. Negative feedbacks are given late, in the "last minute," or not given at all, most likely because of the fear of retaliative negative feedback. Conversely, positive feedbacks are given early in order to encourage reciprocation. Towards refining our insights into the observed pattern, we look separately at buyers and sellers, and relate the magnitude of the effects to the trading partners' experience.eBay; reputation mechanism; strategic feedback behavior; informational content; reciprocity; fear of retaliation