The Principal-Agent Approach to Testing Experts

2011 ◽  
Vol 3 (2) ◽  
pp. 89-113 ◽  
Author(s):  
Wojciech Olszewski ◽  
Marcin Pęski
Keyword(s):  
Stochastic Process ◽  
Decision Maker ◽  
Recent Literature ◽  
Infinite Horizon ◽  
Discount Factor ◽  
Principal Agent ◽  
Surplus Extraction ◽  
Full Surplus Extraction

Recent literature on testing experts shows that it is often impossible to determine whether an expert knows the stochastic process that generates data. Despite this negative result, we show that there often exist contracts that allow a decision maker to attain the first-best payoff without learning the expert's type. This kind of full-surplus extraction is always possible in infinite-horizon models in which future payoffs are not discounted. If future payoffs are discounted (but the discount factor tends to 1), the possibility of full-surplus extraction depends on a constraint involving the forecasting technology. (JEL D82)

scholarly journals Optimal dynamic contracting: The first‐order approach and beyond

2019 ◽  
Vol 14 (4) ◽  
pp. 1435-1482 ◽  
Author(s):  
Marco Battaglini ◽  
Rohit Lamba
Keyword(s):  
Stochastic Process ◽  
Time Horizon ◽  
Incentive Compatibility ◽  
Discount Factor ◽  
Principal Agent ◽  
Optimal Contract ◽  
Dynamic Contracting ◽  
First Order ◽  
Incentive Compatible ◽  
Optimal Dynamic

We explore the conditions under which the “first‐order approach” (FO approach) can be used to characterize profit maximizing contracts in dynamic principal–agent models. The FO approach works when the resulting FO‐optimal contract satisfies a particularly strong form of monotonicity in types, a condition that is satisfied in most of the solved examples studied in the literature. The main result of our paper is to show that except for nongeneric choices of the stochastic process governing the types' evolution, monotonicity and, more generally, incentive compatibility are necessarily violated by the FO‐optimal contract if the frequency of interactions is sufficiently high (or, equivalently, if the discount factor, time horizon, and persistence in types are sufficiently large). This suggests that the applicability of the FO approach is problematic in environments in which expected continuation values are important relative to per period payoffs. We also present conditions under which a class of incentive compatible contracts that can be easily characterized is approximately optimal.

The Stackelberg Games of Water Extraction with Myopic Agents

2021 ◽  
Author(s):  
Alain Jean-Marie ◽  
Mabel Tidball ◽  
Víctor Bucarey López
Keyword(s):  
Stackelberg Game ◽  
Infinite Horizon ◽  
Dynamic Game ◽  
Water Extraction ◽  
Discount Factor ◽  
Stackelberg Games ◽  
Groundwater Extraction ◽  
Strategic Interactions ◽  
Water Users ◽  
Threshold Policies

We consider a discrete-time, infinite-horizon dynamic game of groundwater extraction. A Water Agency charges an extraction cost to water users and controls the marginal extraction cost so that it depends not only on the level of groundwater but also on total water extraction (through a parameter [Formula: see text] that represents the degree of strategic interactions between water users) and on rainfall (through parameter [Formula: see text]). The water users are selfish and myopic, and the goal of the agency is to give them incentives so as to improve their total discounted welfare. We look at this problem in several situations. In the first situation, the parameters [Formula: see text] and [Formula: see text] are considered to be fixed over time. The first result shows that when the Water Agency is patient (the discount factor tends to 1), the optimal marginal extraction cost asks for strategic interactions between agents. The contrary holds for a discount factor near 0. In a second situation, we look at the dynamic Stackelberg game where the Agency decides at each time what cost parameter they must announce. We study theoretically and numerically the solution to this problem. Simulations illustrate the possibility that threshold policies are good candidates for optimal policies.

Protecting Privacy on Mobile Apps: A Principal–Agent Perspective

2022 ◽  
Vol 29 (1) ◽  
pp. 1-32
Author(s):  
Zilong Liu ◽  
Xuequn Wang ◽  
Xiaohan Li ◽  
Jun Liu
Keyword(s):  
Mobile Applications ◽  
Recent Literature ◽  
Mobile Apps ◽  
Theory And Practice ◽  
Principal Agent ◽  
Daily Lives ◽  
Privacy Issue ◽  
Informed Decisions ◽  
The Relationship

Although individuals increasingly use mobile applications (apps) in their daily lives, uncertainty exists regarding how the apps will use the information they request, and it is necessary to protect users from privacy-invasive apps. Recent literature has begun to pay much attention to the privacy issue in the context of mobile apps. However, little attention has been given to designing the permission request interface to reduce individuals’ perceived uncertainty and to support their informed decisions. Drawing on the principal–agent perspective, our study aims to understand the effects of permission justification, certification, and permission relevance on users’ perceived uncertainty, which in turn influences their permission authorization. Two studies were conducted with vignettes. Our results show that certification and permission relevance indeed reduce users’ perceived uncertainty. Moreover, permission relevance moderates the relationship between permission justification and perceived uncertainty. Implications for theory and practice are discussed.

scholarly journals Ex-post full surplus extraction, straightforwardly

2007 ◽  
Vol 32 (2) ◽  
pp. 399-410 ◽  
Author(s):  
Vlad Mares ◽  
Ronald M. Harstad
Keyword(s):  
Ex Post ◽  
Surplus Extraction ◽  
Full Surplus Extraction

THE SEQUENTIAL STOCHASTIC ASSIGNMENT PROBLEM WITH POSTPONEMENT OPTIONS

2012 ◽  
Vol 27 (1) ◽  
pp. 25-51 ◽  
Author(s):  
Tianke Feng ◽  
Joseph C. Hartman
Keyword(s):  
Stochastic Process ◽  
Decision Maker ◽  
Assignment Problem ◽  
Lower Risk ◽  
Care Management ◽  
Optimal Assignment ◽  
Threshold Policies ◽  
Stochastic Assignment ◽  
Sequential Stochastic Assignment ◽  
Heterogeneous Resources

The sequential and stochastic assignment problem (SSAP) has wide applications in logistics, finance, and health care management, and has been well studied in the literature. It assumes that jobs with unknown values arrive according to a stochastic process. Upon arrival, a job's value is made known and the decision-maker must immediately decide whether to accept or reject the job and, if accepted, to assign it to a resource for a reward. The objective is to maximize the expected reward from the available resources. The optimal assignment policy has a threshold structure and can be computed in polynomial time. In reality, there exist situations in which the decision-maker may postpone the accept/reject decision. In this research, we study the value of postponing decisions by allowing a decision-maker to hold a number of jobs which may be accepted or rejected later. While maintaining this queue of arrivals significantly complicates the analysis, optimal threshold policies exist under mild assumptions when the resources are homogeneous. We illustrate the benefits of delaying decisions through higher profits and lower risk in both cases of homogeneous and heterogeneous resources.

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