scholarly journals Optimal Monitoring Design

Econometrica ◽  
2020 ◽  
Vol 88 (5) ◽  
pp. 2075-2107 ◽  
Author(s):  
George Georgiadis ◽  
Balazs Szentes
Keyword(s):  
Information Acquisition ◽  
Minimal Cost ◽  
Threshold Policy ◽  
Agent Model ◽  
Monitoring Design ◽  
Effort Level ◽  
Optimal Monitoring ◽  
Principal Agent Model ◽  
Optimal Information ◽  
Acquisition Strategy

This paper considers a Principal–Agent model with hidden action in which the Principal can monitor the Agent by acquiring independent signals conditional on effort at a constant marginal cost. The Principal aims to implement a target effort level at minimal cost. The main result of the paper is that the optimal information‐acquisition strategy is a two‐threshold policy and, consequently, the equilibrium contract specifies two possible wages for the Agent. This result provides a rationale for the frequently observed single‐bonus wage contracts.

scholarly journals Information Acquisition and Its Incentives in an E-Commerce Supply Chain under the Offline Showroom Model

2021 ◽  
Vol 16 (5) ◽  
pp. 1791-1804
Author(s):  
Mengli Li ◽  
Xumei Zhang
Keyword(s):  
Supply Chain ◽  
Information Acquisition ◽  
Incentive Contracts ◽  
Demand Forecast ◽  
Competition Intensity ◽  
Demand Information ◽  
Optimal Information ◽  
Acquisition Strategy ◽  
The One ◽  
The Impact

Recently, the showroom model has developed fast for allowing consumers to evaluate a product offline and then buy it online. This paper aims at exploring the optimal information acquisition strategy and its incentive contracts in an e-commerce supply chain with two competing e-tailers and an offline showroom. Based on signaling game theory, we build a mathematical model by considering the impact of experience service and competition intensity on consumers’ demand. We find that, on the one hand, information acquisition promotes supply chain members to obtain demand information directly or indirectly, which leads to forecast revenue. On the other hand, information acquisition promotes supply chain members to distort optimal decisions, which results in signal cost. The optimal information acquisition strategy depends on the joint impact of forecast revenue, signal cost and demand forecast cost. Notably, in some conditions, the offline showroom will not acquire demand information even when its cost is equal to zero. We also design two different information acquisition incentive contracts to obtain Pareto improvement for all supply chain members.

scholarly journals Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems

2016 ◽  
Vol 55 ◽  
pp. 317-359 ◽  
Author(s):  
Chien-Ju Ho ◽  
Aleksandrs Slivkins ◽  
Jennifer Wortman Vaughan
Keyword(s):  
Dynamic Pricing ◽  
Principal Agent ◽  
Bandit Problem ◽  
Agent Model ◽  
Effort Level ◽  
Adaptive Discretization ◽  
Bonus Payments ◽  
Principal Agent Model ◽  
Pricing Problems

Crowdsourcing markets have emerged as a popular platform for matching available workers with tasks to complete. The payment for a particular task is typically set by the task's requester, and may be adjusted based on the quality of the completed work, for example, through the use of "bonus" payments. In this paper, we study the requester's problem of dynamically adjusting quality-contingent payments for tasks. We consider a multi-round version of the well-known principal-agent model, whereby in each round a worker makes a strategic choice of the effort level which is not directly observable by the requester. In particular, our formulation significantly generalizes the budget-free online task pricing problems studied in prior work. We treat this problem as a multi-armed bandit problem, with each "arm" representing a potential contract. To cope with the large (and in fact, infinite) number of arms, we propose a new algorithm, AgnosticZooming, which discretizes the contract space into a finite number of regions, effectively treating each region as a single arm. This discretization is adaptively refined, so that more promising regions of the contract space are eventually discretized more finely. We analyze this algorithm, showing that it achieves regret sublinear in the time horizon and substantially improves over non-adaptive discretization (which is the only competing approach in the literature). Our results advance the state of art on several different topics: the theory of crowdsourcing markets, principal-agent problems, multi-armed bandits, and dynamic pricing.

Why Were Workers Whipped? Pain in a Principal-Agent Model

1990 ◽  
Vol 100 (403) ◽  
pp. 1109 ◽  
Author(s):  
Michael Suk-Young Chwe
Keyword(s):  
Principal Agent ◽  
Agent Model ◽  
Principal Agent Model
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