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A monotonicity condition guarantees the principal selects the most statistically informative task; when it fails, the principal may rationally prefer less informative tasks to improve incentives and outcomes.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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Informativeness and incentives  \nRahul Deb  \nDepartment of Economics, University of Toronto  \nColin Stewart  \nDepartment of Economics, University of Toronto  \nWe 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.  \nKeywords. Adaptive testing, dynamic learning, ratcheting, testing experts.  \nJEL classification. C44, D82, D83 .  \n1. Introduction  \nIn this paper, we introduce a learning framework in which a principal seeks to determine the privately known ability of a strategic agent. The principal can assign various tasks to the agent, with different types of the agent varying in their ability to complete a given task.1 The agent, who is privately informed about his ability, can, through unobservable actions, affect the outcome on each assigned task and thereby influence the information the principal receives. How does strategic behavior by the agent affect the optimal choice of tasks by the principal? When is learning optimized by assigning more informative tasks?  \n[Rahul Deb:](Rahul Deb: rahul.deb@utoronto.ca)[ rahul.deb@utoronto.ca](Rahul Deb: rahul.deb@utoronto.ca)  \nColin Stewart: [colinbstewart@gmail.com](colinbstewart@gmail.com)  \nThis paper was previously circulated with the shorter title “Optimal adaptive testing.” We would like to thank Heski Bar-Isaac, Dirk Bergemann, Rohan Dutta, Amanda Friedenberg, Sean Horan, Johannes Hörner, George Mailath, Marcin Pski, John Quah, Phil Reny, Larry Samuelson, Ed Schlee, Balázs Szentes, several anonymous referees, and various seminar and conference participants for helpful comments and suggestions. Rya Sciban and Young Wu provided excellent research assistance. We are very grateful for the financial support provided by the Social Sciences and Humanities Research Council of Canada.  \n1 Learning a worker’s ability by observing their performance on differentially informative tasks is a problem that goes back to at least Prescott and Visscher (1980) .  \n© 2018 The Authors. Licensed under the Creative Commons Attribution-NonCommercial License 4 .0. Available at [http://econtheory.org](http://econtheory.org). [https://doi.org/10.3982/TE2914](https://doi.org/10.3982/TE2914)  \n1234 Deb and Stewart Theoretical Economics 13 (2018)  \nOur model can be applied to a number of settings. For example, a manager may choose which tasks to assign to determine whether an employee is suitable for promotion, an employer chooses which questions to ask in a job interview to determine whether a candidate should be hired, and computerized standardized testing assigns questions with the aim of uncovering a student’s ability. In each of these scenarios, information is obtained by observing the agent’s performance over a sequence of tasks, and the principal’s choice of which task to assign may depend on the agent’s past performance. Consequently, the agent can, to some extent, control the path of assigned tasks through his performance.  \nAt a more abstract level, our exercise builds on the classic “sequential choice of experiments” problem in statistics (see, for instance, Chapter 14 of DeGroot 2005) . In this problem, a researcher who wants to learn about an unknown parameter has at her disposal a collection of “experimen","cbCaimPQpk9gVdqD","https://ap.wps.com/l/cbCaimPQpk9gVdqD","pdf",507756,42,"English","# Introduction\n## Adaptive testing framework\n## Strategic effort and task informativeness\n## Connection to sequential experiments in statistics","[{\"question\":\"What does the principal try to determine in the adaptive testing framework?\",\"answer\":\"The principal seeks to determine the strategic agent’s privately known ability by assigning a finite, adaptive sequence of tasks.\"},{\"question\":\"How do effort and ability affect the probability of success on a task?\",\"answer\":\"Success on each task is jointly determined by the agent’s privately known ability and an unobserved effort level that the agent chooses to maximize the probability of passing.\"},{\"question\":\"When does the principal choose the most informative task in the optimal adaptive test?\",\"answer\":\"When a specified monotonicity condition holds, the principal always employs the most statistically informative task.\"}]","Optimal adaptive testing - Informativeness and incentives | PDF",1789422272,106]