个性化激励的大规模配置
Large-Scale Allocation of Personalized Incentives
摘要：
我们认为，监管机构愿意通过向大量个人提供激励来推动个人选择增加社会福利。为此，我们形式化和解决的问题，找到一个最佳的个性化的激励政策：最佳的意义上，它最大化社会福利的激励预算约束下，个性化的意义上，提出的激励措施取决于每个人的替代品，以及她的喜好。我们提出了一个多项式时间近似算法，计算在几秒钟内的政策，我们分析证明，它是有界接近最优。然后，我们扩展的问题，有效地计算最大社会福利曲线，它给出了最大的社会福利可实现的激励预算范围（而不仅仅是一个值）。这条曲线是监管机构确定正确的投资激励预算的一个有价值的实用工具。最后，我们模拟了一个大规模的应用程序，在法国的一个部门（约20万人）的模式选择，并说明了建议的个性化激励政策在减少二氧化碳排放量的有效性。
英文摘要：
We consider a regulator willing to drive individual choices towards increasing social welfare by providing incentives to a large population of individuals. For that purpose, we formalize and solve the problem of finding an optimal personalized-incentive policy: optimal in the sense that it maximizes social welfare under an incentive budget constraint, personalized in the sense that the incentives proposed depend on the alternatives available to each individual, as well as her preferences. We propose a polynomial time approximation algorithm that computes a policy within few seconds and we analytically prove that it is boundedly close to the optimum. We then extend the problem to efficiently calculate the Maximum Social Welfare Curve, which gives the maximum social welfare achievable for a range of incentive budgets (not just one value). This curve is a valuable practical tool for the regulator to determine the right incentive budget to invest. Finally, we simulate a large-scale application to mode choice in a French department (about 200 thousands individuals) and illustrate the effectiveness of the proposed personalized-incentive policy in reducing CO2 emissions.
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