Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/197176 
Year of Publication: 
2018
Citation: 
[Journal:] Theoretical Economics [ISSN:] 1555-7561 [Volume:] 13 [Issue:] 3 [Publisher:] The Econometric Society [Place:] New Haven, CT [Year:] 2018 [Pages:] 1233-1274
Publisher: 
The Econometric Society, New Haven, CT
Abstract: 
We introduce a learning framework in which a principal seeks to determine the ability of a strategic agent. The principal assigns a test consisting of a finite sequence of tasks. The test is adaptive: each task that is assigned can depend on the agent's past performance. The probability of success on a task is jointly determined by the agent's privately known ability and an unobserved effort level that he chooses to maximize the probability of passing the test. We identify a simple monotonicity condition under which the principal always employs the most (statistically) informative task in the optimal adaptive test. Conversely, whenever the condition is violated, we show that there are cases in which the principal strictly prefers to use less informative tasks.
Subjects: 
Adaptive testing
dynamic learning
ratcheting
testing experts
JEL: 
D82
D83
C44
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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