Screening Knowledge with Verifiable Evidence
Research Seminars: Virtual Market Design SeminarA principal seeks to screen an agent based on his demonstrable knowledge of a subject matter, modeled as a binary state. The agent learns about the state through two kinds of opposing verifiable signals, each kind providing evidence in favor of one of the states. A good quality agent has an evidence structure which is more informative than a bad quality one. In a symmetric setting, the paper presented in this Virtual Market Design Seminar shows that under the optimal test, regardless of whether the agent can predict the state correctly, he is failed if the amount of evidence he is able to show is below a threshold. Conditional on providing evidence above this threshold, the agent is passed based on a simple True-False test – i.e., if and only if he gives the correct answer. The authors see this result as rationalizing a common test structure where test-takers are given credit for giving the correct answer only if they show a minimal amount of data, arguments, or steps, in support of their answer. The authors prove the results by identifying a connection to the optimal transport problem and leveraging it to show the existence of an appropriate virtual value function.
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