Large-scale allocation of personalized incentives

Lucas Javaudin (1) , Andre de Palma (1) , Andrea Araldo (2, 3, 4)

1 THEMA - Théorie économique, modélisation et applications
2 IP Paris - Institut Polytechnique de Paris
3 TSP - RST - Département Réseaux et Services de Télécommunications
4 METHODES-SAMOVAR - Méthodes et modèles pour les réseaux

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 f inding 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 personalizedincentive policy in reducing CO2 emissions.

Mots clés: Tax policy; CO2 emissions; Modal shift; H2; Q58; R41; Personalized; Knapsack problem; Tax policy

Domaines: Economies et finances

Lucas Javaudin, Andre de Palma, Andrea Araldo. Large-scale allocation of personalized incentives. 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), Sep 2022, Macau, China. pp.4151-4156, ⟨10.1109/ITSC55140.2022.9922143⟩. ⟨hal-03839571⟩