Consider a variation on the classic Weitzman search problem, in which competing firms can choose how much information about their product to reveal to a consumer. If there are no search frictions, there is a unique symmetric equilibrium in pure strategies; and for any market of finite size, the firms are not fully informative. With search frictions, if the expected value of the prize is sufficiently high, there is a unique symmetric equilibrium in which firms are fully informative. Remarkably, a small search cost leads to the perfect competition level of information provision: consumers gain when firms are forced to compete on information.
More recently, Gamp and Kr "ahmer (2017) @cite_24 examine a scenario in which sellers can dupe naive consumers into buying products that are lemons. As in this model (though through a different mechanism), search frictions can be beneficial to consumers. Choi, Dai and Kim (2016) @cite_15 incorporate price competition into a model of Weitzman search and show, among other things, that a reduction in search cost can lead to an increase in price. Lastly, Perez-Richet (2012) @cite_23 considers a model in which candidates can choose whether to disclose information in order to convince a decision maker to adopt their product.
@cite_24 @cite_15 @cite_23
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We consider an oligopoly model in which consumers engage in sequential search based on partial product information and advertised prices. By applying Weitzman's (1979) optimal sequential search solution, we derive a simple static condition that fully summarizes consumers' shopping outcomes and translates the pricing game among the sellers into a familiar discrete‐choice problem. Exploiting the discrete‐choice reformulation, we provide sufficient conditions that guarantee the existence and uniqueness of market equilibrium and analyze the effects of preference diversity and search frictions on market prices. Among other things, we show that a reduction in search costs raises market prices. We study the interplay between deception and consumer search in a search market where firms may deceive some naive consumers with inferior products that display hidden (bad) attributes. We derive an equilibrium in which both superior and inferior quality is offered and show that as search frictions vanish, superior goods are entirely driven out of the market. Deception harms sophisticated consumers, as it forces them to search longer to find a superior product. We argue that policy interventions that reduce search frictions such as the standardization of price and package formats may harm welfare. In contrast, reducing the number of naive consumers through transparency policies and education campaigns as well as a minimum quality standard can improve welfare. This paper develops search-theoretic models in which it is individually rational for firms to engage in obfuscation. It considers oligopoly competition between firms selling a homogeneous good to a population of rational consumers who incur search costs to learn each firm's price. Search costs are endogenized: obfuscation is equated with unobservable actions that make it more time-consuming to inspect a product and learn its price. We note two mechanisms by which obfuscation can affect consumer beliefs about future search costs: a direct effect that applies when search costs are convex in time spent searching and a signal-jamming effect that applies when an informational link is present. As long as obfuscation is costless for firms, the presence of either of these mechanisms guarantees that obfuscation must occur in equilibrium, unless consumer search costs are already so high that consumers are willing to purchase at the highest equilibrium price in the absence of obfuscation. Changes in consumer search costs are at least partially offset by changes in the equilibrium level of obfuscation, raising doubts about whether reductions in consumer search costs must make markets more competitive. We also examine patterns of obfuscation and show that higher markups are usually associated with more obfuscation. (This abstract was borrowed from another version of this item.) We investigate nonparametric multiproduct pricing problems, in which we want to find revenue maximizing prices for products @math based on a set of customer samples @math . We mostly focus on the unit-demand case, in which products constitute strict substitutes and each customer aims to purchase a single product. In this setting a customer sample consists of a number of nonzero values for different products and possibly an additional product ranking. Once prices are fixed, each customer chooses to buy one of the products she can afford based on some predefined selection rule. We distinguish between the min-buying, max-buying, and rank-buying models. Some of our results also extend to single-minded pricing, in which case products are strict complements and every customer seeks to buy a single set of products, which she purchases if the sum of prices is below her valuation for that set. For the min-buying model we show that the revenue maximization problem is not approximable within factor @math for some constant @math , unless @math , thereby almost closing the gap between the known algorithmic results and previous lower bounds. We also prove inapproximability within @math , @math being an upper bound on the number of nonzero values per customer, and @math under slightly stronger assumptions and provide matching upper bounds. Surprisingly, these hardness results hold even if a price ladder constraint, i.e., a predefined order on the prices of all products, is given. Without the price ladder constraint we obtain similar hardness results for the special case of uniform valuations, i.e., the case that every customer has identical values for all the products she is interested in, assuming specific hardness of the balanced bipartite independent set problem in constant degree graphs or hardness of refuting random 3CNF formulas. Introducing a slightly more general problem definition in which customers are given as an explicit probability distribution, we obtain inapproximability within @math assuming @math . These results apply to single-minded pricing as well. For the max-buying model a polynomial-time approximation scheme exists if a price ladder is given. We give a matching lower bound by proving strong NP-hardness. Assuming limited product supply, we analyze a generic local search algorithm and prove that it is 2-approximate. Finally, we discuss implications for the rank-buying model.