Personalised incentives with constrained regulator's budget

Lucas Javaudin, Andrea Araldo, André de Palma
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Abstract

We consider a regulator driving individual choices towards increasing social welfare by providing personal incentives. We formalise and solve this problem by maximising social welfare under a budget constraint. The personalised incentives depend on the alternatives available to each individual and on her preferences. A polynomial time approximation algorithm computes a policy within few seconds. We analytically prove that it is boundedly close to the optimum. We efficiently calculate the curve of social welfare achievable for each value of budget within a given range. This curve can be useful for the regulator to decide the appropriate amount of budget to invest. We extend our formulation to enforcement, taxation and non-personalised-incentive policies. We analytically show that our personalised-incentive policy is also optimal within this class of policies and construct close-to-optimal enforcement and proportional tax-subsidy policies. We then compare analytically and numerically our policy with other state-of-the-art policies. Finally, we simulate a large-scale application to mode choice to reduce CO2 emissions.
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约束监管机构预算的个性化激励
我们考虑监管者通过提供个人激励,推动个人选择增加社会福利。我们通过在预算限制下最大化社会福利来正式化和解决这个问题。个性化激励取决于每个人的选择和个人偏好。多项式时间近似算法在几秒钟内计算策略。我们分析证明了它是有界地接近最优的。我们有效地计算出给定范围内每个预算值所能实现的社会福利曲线。这条曲线可以帮助管理者决定适当的预算投入。我们将我们的制定扩展到执法、税收和非个人激励政策。分析表明,我国的个性化激励政策在这类政策中也是最优的,并构建了接近最优的执行和比例税收补贴政策。然后,我们将我们的政策与其他最先进的政策进行分析和数值比较。最后,我们模拟了一个大规模的应用模式选择,以减少二氧化碳的排放。
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