Happy to share our (updated) work on optimal decision making under strategic behavior https://arxiv.org/abs/1905.09239 , which lies in the emerging (super exciting) field of strategic machine learning. This work aims to find decision policies that lead individuals to self-improvement (1/n)
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Then, we first show that, in general, we cannot expect to find optimal decision policies in polynomial time under strategic behavior and there are cases in which deterministic policies are suboptimal (3/n)
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But, if the cost individuals pay to change their features satisfies a monotonicity assumption, we can narrow down the search for the optimal policy to a family of decision policies with desirable properties. This allows for a polynomial time heuristic search algorithm (4/n)
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Moreover, under no assumptions on the cost individuals pay to change their features, we develop an iterative search algorithm that is guaranteed to find locally optimal decision policies also in polynomial time (5/n)
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Finally, we experiment with synthetic and real lending data to both illustrate our theoretical findings and show that, under strategic behavior, the policies our algorithm find do much better than deterministic threshold rules (n/n)
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