We are witnessing an increasing use of data-driven predictive models to
inform decisions. As decisions have implications for individuals and society,
there is increasing pressure on decision makers to be transparent about their
decision policies. At the same time, individuals may use knowledge, gained by
transparency, to invest effort strategically in order to maximize their chances
of receiving a beneficial decision. Our goal is to find decision policies that
are optimal in terms of utility in such a strategic setting. To this end, we
first characterize how strategic investment of effort by individuals leads to a
change in the feature distribution. Using this characterization, we first show
that, in general, we cannot expect to find optimal decision policies in
polynomial time and there are cases in which deterministic policies are
suboptimal. Then, we demonstrate that, if the cost individuals pay to change
their features satisfies a natural monotonicity assumption, we can narrow down
the search for the optimal policy to a particular family of decision policies
with a set of desirable properties, which allow for a highly effective
polynomial time heuristic search algorithm using dynamic programming. Finally,
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. Experiments on synthetic and
real credit card data illustrate our theoretical findings and show that the
decision policies found by our algorithms achieve higher utility than those
that do not account for strategic behavior.

研究表明，通过使用数据驱动的预测模型作出的决策越来越多，对于这些决策的影响，对于个人和社会，决策者透明公开的政策也就越来越重要。我们的研究目标是找到在具有策略性投资的情况下，对于实用性最优的决策策略，在我们的研究中，我们首先表征了个体策略性努力投资如何导致特征分布的变化，然后提出了一种适用于多种情况的最优决策策略的搜索方法，并根据合成和现实数据的实验结果，说明了我们算法找到的决策策略比没有考虑策略性的决策策略的实用性要高。