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.
The paper closest in content to this paper is Competitive Information Disclosure in Search Markets" (Board and Lu (2017)) @cite_19 . There, the authors examine the case of a collection of sellers competing to persuade a prospective buyer to purchase one of their products. Each seller has the same set of products to sell and designs an experiment in order to sway the buyer's opinion on the product. In contrast to our paper, buyers pick a seller at random; and moreover, the state of the world upon which the searchers' utility depends is common. Here, the information provided by each box provides the searcher with no information about the contents of any of the other boxes.
@cite_19
2557199769 2022427910 1987352480 2088733959
Buyers often search across sellers to learn which product best fits their needs. We study how sellers manage these search incentives through their disclosure strategies (e.g., product trials, reviews, and recommendations) and ask how competition affects information provision. If sellers can observe the beliefs of buyers or can coordinate their strategies, then there is an equilibrium in which sellers provide the "monopoly level" of information. In contrast, if buyers' beliefs are private, then there is an equilibrium in which sellers provide full information as search costs vanish. Anonymity and coordination thus play important roles in understanding how advice markets work. In this article, we study the problem of online market clearing where there is one commodity in the market being bought and sold by multiple buyers and sellers whose bids arrive and expire at different times. The auctioneer is faced with an online clearing problem of deciding which buy and sell bids to match without knowing what bids will arrive in the future. For maximizing profit, we present a (randomized) online algorithm with a competitive ratio of ln(pmax − pmin) p 1, when bids are in a range [pmin, pmax], which we show is the best possible. A simpler algorithm has a ratio twice this, and can be used even if expiration times are not known. For maximizing the number of trades, we present a simple greedy algorithm that achieves a factor of 2 competitive ratio if no money-losing trades are allowed. We also show that if the online algorithm is allowed to subsidize matches---match money-losing pairs if it has already collected enough money from previous pairs to pay for them---then it can actually be 1-competitive with respect to the optimal offline algorithm that is not allowed subsidy. That is, for maximizing the number of trades, the ability to subsidize is at least as valuable as knowing the future. We also consider objectives of maximizing buy or sell volume and social welfare. We present all of these results as corollaries of theorems on online matching in an incomplete interval graph.We also consider the issue of incentive compatibility, and develop a nearly optimal incentive-compatible algorithm for maximizing social welfare. For maximizing profit, we show that no incentive-compatible algorithm can achieve a sublinear competitive ratio, even if only one buy bid and one sell bid are alive at a time. However, we provide an algorithm that, under certain mild assumptions on the bids, performs nearly as well as the best fixed pair of buy and sell prices, a weaker but still natural performance measure. This latter result uses online learning methods, and we also show how such methods can be used to improve our “optimal” algorithms to a broader notion of optimality. Finally, we show how some of our results can be generalized to settings in which the buyers and sellers themselves have online bidding strategies, rather than just each having individual bids. In this paper we consider a mechanism design problem in the context of large-scale crowdsourcing markets such as Amazon's Mechanical Turk mturk, ClickWorker clickworker, CrowdFlower crowdflower. In these markets, there is a requester who wants to hire workers to accomplish some tasks. Each worker is assumed to give some utility to the requester on getting hired. Moreover each worker has a minimum cost that he wants to get paid for getting hired. This minimum cost is assumed to be private information of the workers. The question then is -- if the requester has a limited budget, how to design a direct revelation mechanism that picks the right set of workers to hire in order to maximize the requester's utility? We note that although the previous work (Singer (2010) (2011)) has studied this problem, a crucial difference in which we deviate from earlier work is the notion of large-scale markets that we introduce in our model. Without the large market assumption, it is known that no mechanism can achieve a competitive ratio better than 0.414 and 0.5 for deterministic and randomized mechanisms respectively (while the best known deterministic and randomized mechanisms achieve an approximation ratio of 0.292 and 0.33 respectively). In this paper, we design a budget-feasible mechanism for large markets that achieves a competitive ratio of 1 -- 1 e = 0.63. Our mechanism can be seen as a generalization of an alternate way to look at the proportional share mechanism, which is used in all the previous works so far on this problem. Interestingly, we can also show that our mechanism is optimal by showing that no truthful mechanism can achieve a factor better than 1 -- 1 e, thus, fully resolving this setting. Finally we consider the more general case of submodular utility functions and give new and improved mechanisms for the case when the market is large. E-commerce web-sites such as Ebay as well as advertising exchanges (AdX) such as DoubleClick's, RightMedia, or AdECN work as intermediaries who sell items (e.g. page-views) on behalf of a seller (e.g. a publisher) to buyers on the opposite side of the market (e.g., advertisers). These platforms often use fixed-percentage sharing schemes, according to which (i) the platform runs an auction amongst buyers, and (ii) gives the seller a constant-fraction (e.g., 80 ) of the auction proceeds. In these settings, the platform faces asymmetric information regarding both the valuations of buyers for the item (as in a standard auction environment) as well as about the seller's opportunity cost of selling the item. Moreover, platforms often face intense competition from similar market places, and such competition is likely to favor auction rules that secure high payoffs to sellers. In such an environment, what selling mechanism should platforms employ? Our goal in this paper is to study optimal mechanism design in settings plagued by competition and two-sided asymmetric information, and identify conditions under which the current practice of employing constant cuts is indeed optimal. In particular, we first show that for a large class of competition games, platforms behave in equilibrium as if they maximize a a convex combination of seller's payoffs and platform's revenue, with weight α on the seller's payoffs (which is proxy for the intensity of competition in the market). We generalize the analysis of Myerson and Satterthwaite (1983), and derive the optimal direct-revelation mechanism for each α. As expected, the optimal mechanism applies a reserve price which is decreasing in α. Next, we present an indirect implementation based on sharing schemes". We show that constant cuts are optimal if and only if the opportunity cost of the seller has a power-form distribution, and derive a simple formula for computing the optimal constant cut as a function of the sellers' distribution of opportunity costs, and the market competition proxy α. Finally, for completeness, we study the case of a seller's optimal auction with a fixed profit for the platform, and derive the optimal direct and indirect implementations in this setting.