Optimal Mechanism Design with Flexible Consumers and Costly Supply

The problem of designing a profit-maximizing, Bayesian incentive compatible and individually rational mechanism with flexible consumers and costly heterogeneous supply is considered. In our setup, each consumer is associated with a flexibility set that describes the subset of goods the consumer is equally interested in. Each consumer wants to consume one good from its flexibility set. The flexibility set of a consumer and the utility it gets from consuming a good from its flexibility set are its private information. We adopt the flexibility model of [1] and focus on the case of nested flexibility sets -- each consumer's flexibility set can be one of k nested sets. Examples of settings with this inherent nested structure are provided. On the supply side, we assume that the seller has an initial stock of free supply but it can purchase more goods for each of the nested sets at fixed exogenous prices. We characterize the allocation and purchase rules for a profit-maximizing, Bayesian incentive compatible and individually rational mechanism as the solution to an integer program. The optimal payment function is pinned down by the optimal allocation rule in the form of an integral equation. We show that the nestedness of flexibility sets can be exploited to obtain a simple description of the optimal allocations, purchases and payments in terms of thresholds that can be computed through a straightforward iterative procedure.Comment: 8 pages. arXiv admin note: text overlap with arXiv:1607.0252

Robust Dynamic Pricing with Strategic Customers

We consider the canonical revenue management (RM) problem wherein a seller must sell an inventory of some product over a finite horizon via an anonymous, posted price mechanism. Unlike typical models in RM, we assume that customers are forward looking. In particular, customers arrive randomly over time and strategize about their times of purchases. The private valuations of these customers decay over time and the customers incur monitoring costs; both the rates of decay and these monitoring costs are private information. This setting has resisted the design of optimal dynamic mechanisms heretofore. Optimal pricing schemes-an almost necessary mechanism format for practical RM considerations-have been similarly elusive. The present paper proposes a mechanism we dub robust pricing. Robust pricing is guaranteed to achieve expected revenues that are at least within 29% of those under an optimal (not necessarily posted price) dynamic mechanism. We thus provide the first approximation algorithm for this problem. The robust pricing mechanism is practical, since it is an anonymous posted price mechanism and since the seller can compute the robust pricing policy for a problem without any knowledge of the distribution of customer discount factors and monitoring costs. The robust pricing mechanism also enjoys the simple interpretation of solving a dynamic pricing problem for myopic customers with the additional requirement of a novel “restricted sub-martingale constraint” on prices that discourages rapid discounting. We believe this interpretation is attractive to practitioners. Finally, numerical experiments suggest that the robust pricing mechanism is, for all intents, near optimal

Dynamic Matching Market Design

We introduce a simple benchmark model of dynamic matching in networked markets, where agents arrive and depart stochastically and the network of acceptable transactions among agents forms a random graph. We analyze our model from three perspectives: waiting, optimization, and information. The main insight of our analysis is that waiting to thicken the market can be substantially more important than increasing the speed of transactions, and this is quite robust to the presence of waiting costs. From an optimization perspective, naive local algorithms, that choose the right time to match agents but do not exploit global network structure, can perform very close to optimal algorithms. From an information perspective, algorithms that employ even partial information on agents' departure times perform substantially better than those that lack such information. To elicit agents' departure times, we design an incentive-compatible continuous-time dynamic mechanism without transfers

Progressive Screening: Long-Term Contracting with a Privately Known Stochastic Process

We examine a model of long-term contracting in which the buyer is privately informed about the stochastic process by which her value for a good evolves. In addition, her realized values are also her private information. We characterize the profit-maximizing long-term contract offered by a monopolist in this setting. This optimal contract consists of a menu of deterministic sequences of static contracts. Within each sequence, higher real- ized values lead to greater quantity provision; however, an increasing proportion of buyer types are excluded over time (eventually leading to inefficient early termination of the re- lationship). Moreover, the menu choices differ by future generosity, with more costly (up- front) plans guaranteeing greater quantity provision in the future. Thus, the seller screens buyers in the initial period, and then progressively screens additional buyers so as to re- duce the information rents paid in future periods.Asymmetric information, Dynamic mechanism design, Long-term contracts, Term life insurance, Sequential screening.

Nonlinear pricing for stochastic container leasing system

With the substantial upsurge of container traffic, the container leasing company thrives on the financial benefits and operational flexibility of leasing containers requested by shippers. In practice, container lease pricing problem is different from the consumer product pricing in consideration of the fair value of container, limited customer types and monopolistic supply market. In view of the durability of container and the diversified lease time and quantity, the pricing is a challenging task for the leasing company. This paper examines the monopolist’s nonlinear pricing problems in static and dynamic envi- ronments. In particular, the leasing company designs and commits a menu of price and hire quantity/time pairs to maximize the expected profit and in turn customers choose hire quantities/time to maximize their surpluses according to their hire preferences. In a static environment, closed-form solutions are obtained for different groups of customers with multiple types subject to capacity constraint. In a dynamic environment, we address two customer types and derive closed-form solutions for the problem of customers with hire time preference. Further, we show that the effect of the capacity constraint increases with time of the planning horizon when customers have the same hire time preference; while in the case with different hire time preferences, the capacity constraint has opposite effects on the low and high type customers. Last, the case of customers with hire quantity preference is discussed. We focus on the lease with alternative given sets of hire time and use dynamic programming to derive the numerical optimal hire time sequence

Dynamic Mechanism Design

We study the optimal mechanism in a dynamic sales relationship where the buyerís arrival date is uncertain, and where his value changes stochastically over time. The buyerís arrival date is the Örst date at which contracting is feasible and is his private information. To induce immediate participation, the buyer is granted positive expected rents even if his value at arrival is the lowest possible. The buyer is punished for arriving late; i.e., he expects to earn less of the surplus. Optimal allocations for a late arriver are also further distorted below Örst-best levels. Conditions are provided under which allocations converge to the e¢ cient ones long enough after contracting, and this convergence occurs irrespective of the time the contract is initially agreed (put di§erently, the so-called "principle of vanishing distortions" introduced by Battaglini (2005) continues to apply irrespective of the buyerís arrival date)

Dynamic Mechanism Design

We study the optimal mechanism in a dynamic sales relationship where the buyerís arrival date is uncertain, and where his value changes stochastically over time. The buyerís arrival date is the Örst date at which contracting is feasible and is his private information. To induce immediate participation, the buyer is granted positive expected rents even if his value at arrival is the lowest possible. The buyer is punished for arriving late; i.e., he expects to earn less of the surplus. Optimal allocations for a late arriver are also further distorted below Örst-best levels. Conditions are provided under which allocations converge to the e¢ cient ones long enough after contracting, and this convergence occurs irrespective of the time the contract is initially agreed (put di§erently, the so-called "principle of vanishing distortions" introduced by Battaglini (2005) continues to apply irrespective of the buyerís arrival date)