This review summarizes a nascent body of theoretical research on design of incentives when the environment is not fully known to the designer and offers some general lessons from the work so far. These recent models based on uncertainty and robustness offer an additional set of tools in the toolkit, complementary to more traditional, fully Bayesian modeling approaches, and broaden the range of problems that can be studied. The kinds of insights that such models can offer, and the methodological and technical challenges that they confront, broadly parallel those of traditional approaches.
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