This article describes the use of acrlong DLTs as a means to create personalized social nudges and to influence the behavior of agents in a smart city environment. Specifically, we present a scheme to price personalized risk in sharing economy applications. We provide proofs for the convergence of the proposed stochastic system and we validate our approach through the use of extensive Monte Carlo simulations.