Optimal Adaptive Testing: Informativeness and Incentives

Rahul Deb and Colin Stewart

Working Papers from University of Toronto, Department of Economics

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 questions or tasks. The test is adaptive: each question that is assigned can depend on the agent's past performance. The probability of success on a question is jointly determined by the agent's privately known ability and an unobserved action 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 question 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 questions.

Keywords: testing; learning; sequential choice of experiments (search for similar items in EconPapers)
JEL-codes: C70 C72 (search for similar items in EconPapers)
Pages: Unknown pages
Date: 2015-10-30
New Economics Papers: this item is included in nep-mic
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Journal Article: Optimal adaptive testing: informativeness and incentives (2018)
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