  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.

Econometrics