We study mechanism design when agents have private preferences and private information about a common payoff-relevant state. We show that standard message-driven mechanisms cannot implement socially efficient allocations when agents have multidimensional types, even under favorable conditions.

We develop an economic framework to analyze the optimal pricing and product design of Large Language Models (LLM). Our framework captures several key features of LLMs: variable operational costs of processing input and output tokens; the ability to customize models through fine-tuning; and high-dimensional user heterogeneity in terms of task requirements and error sensitivity. In our model, a monopolistic seller offers multiple versions of LLMs through a menu of products. The optimal pricing structure depends on whether token allocation across tasks is contractible and whether users face scale constraints.

It has long been known that third degree price discrimination --- the practice of charging different prices to consumers in distinct market segments--- may increase or decrease consumer surplus. However, the potential benefits for consumers of third degree price discrimination when the seller can simultaneously engage in second degree price discrimination is more limited. We study the interplay between second and third degree price discrimination, with a particular focus on how and when market segmentation benefits consumers.

Digital advertising facilitates the matching of consumers and advertisers online. Large platforms leverage their extensive consumer data to offer access to qualified online shoppers, helping them find their preferred brands. In turn, advertisers join these platforms to target a wider range of potential consumers beyond their existing customer base.

The arrival of digital commerce has lead to an increasing use of personalization and differentiation strategies. With differentiated products along the quality dimension and/or the quantity dimension comes the need for nonlinear pricing policies or second degree price discrimination. The optimal pricing strategies for quality and quantity differentiated products were first investigated by Mussa and Rosen (1978) and Maskin and Riley (1984), respectively. The optimal pricing strategies were shown to depend heavily on the prior distribution of the private information regarding the types, and ultimately the willingness-to-pay of the buyers. Yet, frequently the sellers possess only weak and incomplete information about the distribution of demand. This paper aims to develop robust pricing policies that are independent of specific demand distributions and provide revenue guarantees across all possible distributions.

We study the problem of selling information to a data-buyer who faces a decision problem under uncertainty. We consider the classic Bayesian decision-theoretic model pioneered by Blackwell. Initially, the data buyer has only partial information about the payoff-relevant state of the world. A data seller offers additional information about the state of the world. The information is revealed through signaling schemes, also referred to as experiments. In the single-agent setting, any mechanism can be represented as a menu of experiments. A recent paper by Bergemann et al.[8] present a complete characterization of the revenue-optimal mechanism in a binary state and binary action environment. By contrast, no characterization is known for the case with more actions. In this paper, we consider more general environments and study arguably the simplest mechanism, which only sells the fully informative experiment. In the environment with binary state and m≥3 actions, we provide an $O(m)$-approximation to the optimal revenue by selling only the fully informative experiment and show that the approximation ratio is tight up to an absolute constant factor. An important corollary of our lower bound is that the size of the optimal menu must grow at least linearly in the number of available actions, so no universal upper bound exists for the size of the optimal menu in the general single-dimensional setting. We also provide a sufficient condition under which selling only the fully informative experiment achieves the optimal revenue.

We analyze the optimal information design in a click-through auction with stochastic click-through rates and known valuations per click. The auctioneer takes as given the auction rule of the click-through auction, namely the generalized second-price auction. Yet, the auctioneer can design the information flow regarding the click-through rates among the bidders. We require that the information structure to be calibrated in the learning sense. With this constraint, the auction needs to rank the ads by a product of the value and a calibrated prediction of the click-through rates. The task of designing an optimal information structure is thus reduced to the task of designing an optimal calibrated prediction.

This paper is focused not on the Internet architecture - as defined by layering, the narrow waist of IP, and other core design principles - but on the Internet infrastructure, as embodied in the technologies and organizations that provide Internet service. In this paper we discuss both the challenges and the opportunities that make this an auspicious time to revisit how we might best structure the Internet's infrastructure. Currently, the tasks of transit-between-domains and last-mile-delivery are jointly handled by a set of ISPs who interconnect through BGP. In this paper we propose cleanly separating these two tasks. For transit, we propose the creation of a "public option" for the Internet's core backbone. This public option core, which complements rather than replaces the backbones used by large-scale ISPs, would (i) run an open market for backbone bandwidth so it could leverage links offered by third-parties, and (ii) structure its terms-of-service to enforce network neutrality so as to encourage competition and reduce the advantage of large incumbents.

We study the classic sequential screening problem under ex-post participation constraints. Thus the seller is required to satisfy buyers' ex-post participation constraints. A leading example is the online display advertising market, in which publishers frequently cannot use up-front fees and instead use transaction-contingent fees.

We analyze a nonlinear pricing model with limited information. Each buyer can purchase a large variety, d, of goods. His preference for each good is represented by a scalar and his preference over d goods is represented by a d-dimensional vector. The type space of each buyer is given by a compact subset of Rd+ with a continuum of possible types. By contrast, the seller is limited to offer a finite number M of d-dimensional choices.

We study optimal pricing in the presence of recommender systems. A recommender system affects the market in two ways: (i) it creates value by reducing product uncertainty for the customers and hence (ii) its recommendations can be offered as add-ons which generate informational externalities. The quality of the recommendation add-on is endogenously determined by sales. We investigate the impact of these factors on the optimal pricing by a seller with a recommender system against a competitive fringe without such a system. If the recommender system is sufficiently effective in reducing uncertainty, then the seller prices otherwise symmetric products differently to have some products experienced more aggressively. Moreover, the seller segments the market so that customers with more in.exible tastes pay higher prices to get better recommendation.