The Interval Structure of Optimal Disclosure

Econometrica ◽  
2019 ◽  
Vol 87 (2) ◽  
pp. 653-675 ◽  
Author(s):  
Yingni Guo ◽  
Eran Shmaya
Keyword(s):  
Private Information ◽  
Simple Algorithm ◽  
Optimal Mechanism ◽  
Nested Intervals

A sender persuades a receiver to accept a project by disclosing information about a payoff‐relevant quality. The receiver has private information about the quality, referred to as his type. We show that the sender‐optimal mechanism takes the form of nested intervals: each type accepts on an interval of qualities and a more optimistic type's interval contains a less optimistic type's interval. This nested‐interval structure offers a simple algorithm to solve for the optimal disclosure and connects our problem to the monopoly screening problem. The mechanism is optimal even if the sender conditions the disclosure mechanism on the receiver's reported type.

Optimal Retirement Policies with Present-Biased Agents

2020 ◽  
Author(s):  
Pei Cheng Yu
Keyword(s):  
Private Information ◽  
Hyperbolic Discounting ◽  
Time Inconsistency ◽  
Retirement Savings ◽  
Present Bias ◽  
Optimal Mechanism ◽  
Savings Accounts ◽  
The Government ◽  
Social Security Benefits ◽  
Retirement Policies

Abstract This paper incorporates quasi–hyperbolic discounting into a Mirrlees taxation model to study the design of retirement policies for present-biased agents. I show that the government can improve the screening of productivity by exploiting time inconsistency. This is done by providing commitment to sophisticated agents and taking advantage of the incorrect beliefs of naïve agents. This can be achieved even if the degrees of present bias and sophistication are private information. I also demonstrate how the government can implement the optimal mechanism using retirement savings accounts and social security benefits.

scholarly journals Just Enough or All: Selling a Firm

2016 ◽  
Vol 8 (3) ◽  
pp. 223-256 ◽  
Author(s):  
Mehmet Ekmekci ◽  
Nenad Kos ◽  
Rakesh Vohra
Keyword(s):  
Private Information ◽  
Cash Flows ◽  
Private Benefits ◽  
One Dimensional ◽  
Optimal Mechanism

We consider the problem of selling a firm to a single buyer. The buyer privately knows post-sale cash flows and the benefits of control. Unlike the case where buyer's private information is one-dimensional, the optimal mechanism is a menu of tuples of cash-equity mixtures. When the seller wants to screen finely with respect to the private benefits, he makes an offer for the smallest fraction of the company that facilitates the transfer of control. When he wants to screen finely with respect to cash flows, he makes an offer for all the shares of the company. (JEL D21, D82, G32, G34)

scholarly journals Dynamic Delegation of Experimentation

2016 ◽  
Vol 106 (8) ◽  
pp. 1969-2008 ◽  
Author(s):  
Yingni Guo
Keyword(s):  
Poisson Process ◽  
Private Information ◽  
Prior Belief ◽  
Optimal Mechanism ◽  
Dynamic Relationship ◽  
Total Freedom

I study a dynamic relationship where a principal delegates experimentation to an agent. Experimentation is modeled as a one-armed bandit that yields successes following a Poisson process. Its unknown intensity is high or low. The agent has private information, his type being his prior belief that the intensity is high. The agent values successes more than the principal does, so prefers more experimentation. The optimal mechanism is a cutoff rule in the belief space: the cutoff gives pessimistic types total freedom but curtails optimistic types’ behavior. Pessimistic types overexperiment while the most optimistic ones underexperiment. This delegation rule is time consistent. (JEL D23, D82, D83, O30)

scholarly journals Optimal Project Selection Mechanisms

2014 ◽  
Vol 6 (3) ◽  
pp. 227-255 ◽  
Author(s):  
Talia Bar ◽  
Sidartha Gordon
Keyword(s):  
Private Information ◽  
Limited Liability ◽  
Project Selection ◽  
Project Managers ◽  
Outside Option ◽  
Optimal Mechanism ◽  
Optimal Value ◽  
Ex Ante ◽  
The Cost ◽  
Selection Mechanisms

We study mechanisms for selecting up to m out of n projects. Project managers' private information on quality is elicited through transfers. Under limited liability, the optimal mechanism selects projects that maximize some function of the project's observable and reported characteristics. When all reported qualities exceed their own project-specific thresholds, the selected set only depends on observable characteristics, not reported qualities. Each threshold is related to (i) the outside option level at which the cost and benefit of eliciting information on the project cancel out and (ii) the optimal value of selecting one among infinitely many ex ante identical projects. (JEL D21, D82, O32)

Optimal Contract Under Asymmetric Information About Fairness

2021 ◽  
Author(s):  
Valery Pavlov ◽  
Elena Katok ◽  
Wen Zhang
Keyword(s):  
Supply Chain ◽  
Information Asymmetry ◽  
Incomplete Information ◽  
Private Information ◽  
Problem Definition ◽  
Empirical Literature ◽  
Common Finding ◽  
Supply Chain Performance ◽  
Optimal Mechanism ◽  
Wide Range

Problem definition: To improve the poor performance of supply chains caused by misaligned incentives under the wholesale price contract, theory proposes coordinating contracts. However, a common finding of experimental studies testing such contracts is that they tend to yield only a marginal, if any, performance improvement over wholesale pricing. These studies identify several behavioral factors that are at play but none accounted for by the theory proposing coordinating contracts. Among them, identified as the single most detrimental for the supply chain performance, is incomplete information about preferences for fairness causing contract rejections. Can the supply chain performance be improved with a contract designed allowing for this type of information asymmetry? What does this contract (mechanism) look like? Academic/practical relevance: The extant research characterized the optimal contracting mechanisms for such important practical cases as the suppliers’ private information about production cost or the retailers’ private information about the end customer demand. The present study addresses the gap in another important practical case: when the source of information asymmetry is the private information about preferences for fairness. Methodology: The underlying research method is mechanism design. Results: We prove that the optimal mechanism consists of a single contract positioned on the Pareto frontier and characterize the optimal profit split between the supplier and the retailer. We show that, under a wide range of preferences for fairness, the efficiency loss because of private information is strictly positive, but exceptions are possible. We also show that the optimal mechanism can be implemented with a variety of commonly used in practice and widely studied in academic literature contracts, including the minimum order quantity and the two-part tariff ones. Managerial implications: We establish a direct link between a large volume of theoretical and empirical literature on social preferences with the research on supply chain contracts. Because rejections that are because of incomplete information are an important cause of contract inefficiency observed in the laboratory, managers should avoid take it or leave it offers when they negotiate contracts. Instead, the bargaining process should be geared toward discovering the extent of the fairness preferences of the contracting parties.

On the Futility of Dynamics in Robust Mechanism Design

2021 ◽  
Author(s):  
Santiago R. Balseiro ◽  
Anthony Kim ◽  
Daniel Russo
Keyword(s):  
Private Information ◽  
Broad Class ◽  
Dynamic Mechanism ◽  
Minimax Regret ◽  
Worst Case ◽  
Optimal Mechanism ◽  
Optimal Dynamic ◽  
Aggregate Utility ◽  
Static Mechanism ◽  
Robust Mechanism Design

We consider a principal who repeatedly interacts with a strategic agent holding private information. In each round, the agent observes an idiosyncratic shock drawn independently and identically from a distribution known to the agent but not to the principal. The utilities of the principal and the agent are determined by the values of the shock and outcomes that are chosen by the principal based on reports made by the agent. When the principal commits to a dynamic mechanism, the agent best-responds to maximize his aggregate utility over the whole time horizon. The principal’s goal is to design a dynamic mechanism to minimize his worst-case regret, that is, the largest difference possible between the aggregate utility he could obtain if he knew the agent’s distribution and the actual aggregate utility he obtains. We identify a broad class of games in which the principal’s optimal mechanism is static without any meaningful dynamics. The optimal dynamic mechanism, if it exists, simply repeats an optimal mechanism for a single-round problem in each round. The minimax regret is the number of rounds times the minimax regret in the single-round problem. The class of games includes repeated selling of identical copies of a single good or multiple goods, repeated principal-agent relationships with hidden information, and repeated allocation of a resource without money. Outside this class of games, we construct examples in which a dynamic mechanism provably outperforms any static mechanism.

How to Sell a Data Set? Pricing Policies for Data Monetization

2021 ◽  
Author(s):  
Sameer Mehta ◽  
Milind Dawande ◽  
Ganesh Janakiraman ◽  
Vijay Mookerjee
Keyword(s):  
Private Information ◽  
Data Sets ◽  
Optimal Price ◽  
Worst Case ◽  
Data Set ◽  
Actual Performance ◽  
Optimal Mechanism ◽  
Pricing Policies ◽  
Endogenous Selection ◽  
Selection Of

The wide variety of pricing policies used in practice by data sellers suggests that there are significant challenges in pricing data sets. In this paper, we develop a utility framework that is appropriate for data buyers and the corresponding pricing of the data by the data seller. Buyers interested in purchasing a data set have private valuations in two aspects—their ideal record that they value the most, and the rate at which their valuation for the records in the data set decays as they differ from the buyers’ ideal record. The seller allows individual buyers to filter the data set and select the records that are of interest to them. The multidimensional private information of the buyers coupled with the endogenous selection of records makes the seller’s problem of optimally pricing the data set a challenging one. We formulate a tractable model and successfully exploit its special structure to obtain optimal and near-optimal data-selling mechanisms. Specifically, we provide insights into the conditions under which a commonly used mechanism—namely, a price-quantity schedule—is optimal for the data seller. When the conditions leading to the optimality of a price-quantity schedule do not hold, we show that the optimal price-quantity schedule offers an attractive worst-case guarantee relative to an optimal mechanism. Further, we numerically solve for the optimal mechanism and show that the actual performance of two simple and well-known price-quantity schedules—namely, two-part tariff and two-block tariff—is near optimal. We also quantify the value to the seller from allowing buyers to filter the data set.

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