Personalized Incentives with Constrained Regulator’s Budget

We consider a regulator driving individual choices towards increasing social welfare by providing personal incentives. We formalize and solve this problem by maximizing social welfare under a budget constraint. The personalized 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 Maximum Social Welfare Curve to achieve for a range of incentive budgets. This curve provides the right incentive budget to invest. We extend our formulation to enforcement, taxation and non-personalized-incentive policies. We analytically show that our personalized-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.