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Learning Revenue Maximizing Menus of Lotteries and Two-Part Tariffs
We advance a recently flourishing line of work at the intersection of learning theory and computational economics by studying the learnability of two classes of mechanisms prominent in economics, namely menus of lotteries and two-part tariffs.
Setting Fair Incentives to Maximize Improvement
A key technical challenge of this problem is the non-monotonicity of social welfare in the set of target levels, i. e., adding a new target level may decrease the total amount of improvement as it may get easier for some agents to improve.
On classification of strategic agents who can both game and improve
For the general discrete model, we give an efficient algorithm for the problem of maximizing the number of true positives subject to no false positives, and show how to extend this to a partial-information learning setting.
The Strategic Perceptron
The classical Perceptron algorithm provides a simple and elegant procedure for learning a linear classifier.
