Optimal Contracts for Experimentation

Halac, Marina; Kartik, Navin; Liu, Qingmin

doi:10.1093/restud/rdw013

Cited by 123 publications

(66 citation statements)

References 39 publications

(17 reference statements)

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“…And third, we allow for a continuum of types whereas there are only two types in Bouvard's model. Halac et al (2015) also study optimal experimentation in a principal-agent relationship with a two-arm bandit problem. In their model, there is moral hazard regarding the agent's effort choice and adverse selection regarding the probability that the risky arm yields a success conditional on being of good quality.…”

Section: Related Literaturementioning

confidence: 99%

“…8 By contrast, the agent in our model has private information about the prior probability that the risky arm is good. 9 Our mechanism is however different from that of Halac et al (2015), as the two papers assume different forms of asymmetric information about the payoff distribution of the risky arm. 10 Because Guo (2014) studies an environment without side transfers, she is able to use Lagrangian methods to determine the optimal ranges of delegation.…”

Section: Modelmentioning

confidence: 99%

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Experimentation and project selection: Screening and learning

Gomes

Gottlieb

Maestri³

2016

Games and Economic Behavior

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JEL classification:Firms must strike a delicate balance between the exploitation of well-known business models and the exploration of risky, untested approaches. In this paper, we study financial contracting between an investor and a firm with private information about its returns from exploration and exploitation. The investor-optimal mechanism offers contracts with different tolerance for failures to screen returns from exploitation, and with different exposure to the project's revenues to screen returns from exploration. We derive necessary and sufficient conditions for private information about returns from exploration to have zero value to the firm. When these conditions fail, private information about exploration may even decrease the firm's payoff.

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Section: Related Literaturementioning

confidence: 99%

Section: Modelmentioning

confidence: 99%

Experimentation and project selection: Screening and learning

Gomes

Gottlieb

Maestri³

2016

Games and Economic Behavior

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“…Pavan, Segal, and Toikka () provide a general treatment of optimal dynamic mechanism design with pure adverse selection . Other articles address dynamic contracting with mixed adverse selection and moral hazard, among which Krähmer and Strausz (), Gershkov and Perry (), Asseyer (), Eső and Szentes (), and Halac, Kartik, and Liu () are closely related to our article.…”

Section: Introductionmentioning

confidence: 98%

Pairing provision price and default remedy: optimal two‐stage procurement with private R&D efficiency

Liu

2018

The RAND J of Economics

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This article studies cost‐minimizing two‐stage procurement with Research and Development (R&D). The principal wishes to procure a product from an agent. At the first stage, the agent can conduct R&D to discover a more cost‐efficient production technology. First‐stage R&D efficiency and effort and the realized second‐stage production cost are the agent's private information. The optimal two‐stage mechanism is implemented by a menu of single‐stage contracts, each specifying a fixed provision price and remedy paid by a defaulting agent. A higher delivery price is paired with a higher default remedy, and a more efficient type opts for a higher price and higher remedy.

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“…Halac et al . [2016] analyze a model of long‐term contracting for experimentation with hidden information about the agent's ability and dynamic moral hazard and obtain an explicit characterization of optimal contracts. Aside from some technical differences, these previous works are primarily concerned with characterizing optimal contracts.…”

Section: Introductionmentioning

confidence: 99%

Dynamic performance evaluation with deadlines: The role of commitment

Chen

Ishida

2018

The J Industrial Economics

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We consider an environment in which a principal hires an agent and evaluates his productivity over time. The problem is embedded in a continuous‐time model with both hidden action and hidden information, where the principal must induce the agent to exert effort to facilitate her learning process. The value of committing to a deadline is examined in this environment, and factors which make the deadline more profitable are identified. Our framework generates a unique recursive equilibrium structure under no commitment which can be exploited to obtain a full characterization of the unique pure‐strategy equilibrium. The analysis allows us to evaluate the exact value of commitment for any given set of parameters and provides insight into when it is beneficial to commit to an evaluation deadline at the outset of a relationship.

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Optimal Contracts for Experimentation

Cited by 123 publications

References 39 publications

Experimentation and project selection: Screening and learning

Experimentation and project selection: Screening and learning

Pairing provision price and default remedy: optimal two‐stage procurement with private R&D efficiency

Dynamic performance evaluation with deadlines: The role of commitment

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