Automated Pricing Rules in Electronic Posted Offer Markets

I. INTRODUCTION Electronic commerce affords the opportunity to automate the process by which buyers and sellers engage in an exchange at a mutually agreeable price. For example, buyers on the Internet can use software agents to automate the gathering of information on price and other product attributes in online retail markets that are traditionally organized as posted offer markets. (1) This technology obviously reduces the consumer transaction costs of comparing prices, which presumably would induce stronger competition among sellers. However, as Hal Varian remarked in the e-commerce magazine Wired, "Everybody thinks more information is better for consumers. That's not necessarily true" (Bayers, 2000, 215). The same technology that reduces consumer transaction costs can be employed by sellers to automate the monitoring of rivals' prices, which might mitigate or even overwhelm the procompetitive effect of reduced consumer search costs. In addition, electronic markets grant more than just ideal information on competitors' pr ices. With electronic commerce a seller can commit to implementing an automated algorithm that could possibly sustain tacitly collusive prices. In this article we study the supply-side effects of three automated pricing algorithms that use price information as an input that could be retrieved from consumer price-gathering technology in an electronic market. Two of the three algorithms that we study have the theoretical potential to facilitate collusion: low-price matching and a trigger strategy. The third and presumably more competitive algorithm, which we refer to as undercutting, is drawn from the common retail practice of beating competitors' prices. Using the experimental method, this article explores how sellers deploy these three automated pricing algorithms in electronic markets. More specifically, we ask: (1) Do sellers prefer to set their price manually or to adopt automated algorithms that adjust price more frequently? (2) Are markets with automated pricing algorithms more competitive or less competitive than markets with manually-posted prices? (3) Does increased commitment to an automated pricing mechanism facilitate tacit collusion? To study the impact of automated pricing on seller behavior, we base our work on the model of price dispersion in retail markets by Varian (1980). Thus, our article also explores how well a theory of mixed strategy pricing organizes the behavior of sellers in a market with a nearly continuous stream of differentially informed fully revealing customers. (2) As a preview of the results, we find that (1) sellers employ automated pricing algorithms more often than they manually set their own price, (2a) automated undercutting leads to prices similar to the game-theoretic prediction, (2b) automated low-price matching generates prices significantly higher than the game-theoretic prediction, (2c) automated trigger pricing results in market prices below the game-theoretic prediction, and (3) greater commitment to automated low-price matching shifts prices closer to the joint profit-maximizing outcome. The structure of the article is as follows. Section II presents the three automated pricing algorithms we consider. The experimental design and procedures are in section III. Section IV presents and discusses our results, and section V briefly concludes. II. AUTOMATED PRICING ALGORITHMS The market environment is motivated by the model of sales in Varian (1980). (3) In this model there are n sellers that supply buyers who each desire to purchase at most one unit of a homogeneous product. Each seller has a commonly known, constant marginal cost c of supplying a unit to a buyer and posts a price p, which the buyer can accept or reject. The private value v for each nonstrategic buyer is assumed to be a random variable, drawn independently from a known uniform distribution with a support [v, v]. Buyers differ based on the number of firms from which price quotes are received prior to making a purchasing decision. …