Optimal Scoring for Dynamic Information Acquisition
CoRR(2023)
摘要
A principal seeks to learn about a binary state and can do so by enlisting an
agent to acquire information over time using a Poisson information arrival
technology. The agent learns about this state privately, and his effort choices
are unobserved by the principal. The principal can reward the agent with a
prize of fixed value as a function of the agent's sequence of reports and the
realized state. We identify conditions that each individually ensure that the
principal cannot do better than by eliciting a single report from the agent
after all information has been acquired. We also show that such a static
contract is suboptimal under sufficiently strong violations of these
conditions. We contrast our solution to the case where the agent acquires
information "all at once;" notably, the optimal contract in the dynamic
environment may provide strictly positive base rewards to the agent even if his
prediction about the state is incorrect.
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关键词
dynamic information acquisition,optimal scoring
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