@article{THESIS,
      recid = {12945},
      author = {Dasgupta, Sulagna},
      title = {Essays on Screening Knowledge},
      publisher = {University of Chicago},
      school = {Ph.D.},
      address = {2024-08},
      number = {THESIS},
      abstract = {&lt;p&gt;Individuals are evaluated on their knowledge or  expertise in a myriad of settings. Students take exams, job  candidates are interviewed on their domain knowledge,  consultants help firms make decisions and are often  rewarded based on the ex-post accuracy of their advice, and  so on. In this dissertation I consider the problem of  designing these tests “optimally”, so as to maximize the  examiner’s learning of the quality of the test-taker. I  take a mechanism design view of this problem, modeling  knowledge as beliefs over an unknown state. &lt;/p&gt; &lt;p&gt;I  consider three environments that differ in terms of the  features of this state and the nature of knowledge. In  Chapter 2, Dasgupta (2024a), I consider the most basic form  of this problem where the state is binary and knowledge is  the test-taker’s single-dimensional belief over it. I show  that optimal tests are simple: They take the form of  True-False, weighted True-False or True-False-Unsure,  regardless of the principal’s preferences, the distribution  of the agent’s beliefs, its correlation with his quality or  his knowledge thereof. The need to elicit knowledge forces  the principal to trade-off the efficacy of the test in  terms of whom it rewards, against how much it rewards them.  The optimal resolution of this trade-off may lead to a  partial penalty for an “obvious” answer even if it is  correct, a partial reward for a “counterintuitive” answer  even if it is incorrect, or a reward for admitting  ignorance. When the principal can pick the subject matter,  she picks one that admits no such obvious answers. In this  case, the highly prevalent True-False test is always  optimal, regardless of principal’s preferences, agent’s  learning, or the specific optimal choice of the subject  matter. &lt;/p&gt; &lt;p&gt;In Chapter 3, Dasgupta (2024b), I consider  the same problem and largely the same setting, but now  knowledge is demonstrable, modeled as verifiable evidence.  The test-taker learns about the state through two kinds of  opposing verifiable signals, each kind providing evidence  in favor of one of the states. A high quality agent is more  likely to posses evidence which is greater in both quantity  and accuracy, than a low quality agent. In a symmetric  setting, I show that the under the optimal test, regardless  of whether the agent can predict the state correctly, he is  passed if his total amount of evidence provided is  sufficiently high and failed if it is sufficiently low.  Conditional on providing intermediate levels of evidence,  the agent is passed based on a simple True-False test –  i.e., if and only if he gives the correct answer.  Consequently, for intermediate levels of quality  sensitivity of the principal, the optimal test is the  simple True-False, which makes no use of verifiable  evidence, even though it is available. &lt;/p&gt; &lt;p&gt;In Chapter  4, Dasgupta (2024c), I consider a natural extension of the  model of Chapter 2, where I allow the state to take  multiple values. This captures both the cases where there  are multiple questions in the test and the one where the  test is about a complex subject matter instead of a simple,  binary one. I show that in a symmetric setting, the  standard multiple choice question – where the test-taker is  awarded full credit if and only if he selects the correct  answer – is optimal, if and only if the principal is  sufficiently quality-sensitive. &lt;/p&gt;},
      url = {http://knowledge.uchicago.edu/record/12945},
      doi = {https://doi.org/10.6082/uchicago.12945},
}