Optimal adaptive testing: Informativeness and incentives
研究委托人如何设计自适应测试来评估战略型代理人的能力，发现当单调性条件成立时，最优测试总是使用统计上信息量最大的任务；否则可能故意选择信息量较小的任务。
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.