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Article Dans Une Revue Transportmetrica A: Transport Science Année : 2023

Personalised incentives with constrained regulator's budget

Résumé

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 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-personalizedincentive 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.
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Dates et versions

hal-04304703 , version 1 (24-11-2023)

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Lucas Javaudin, Andrea Araldo, André de Palma. Personalised incentives with constrained regulator's budget. Transportmetrica A: Transport Science, 2023, pp.1 - 43. ⟨10.1080/23249935.2023.2284353⟩. ⟨hal-04304703⟩
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