Optimal control in linear stochastic advertising models with memory
This paper deals with a class of optimal control problems which arises in advertising models with Volterra Ornstein-Uhlenbeck process representing the product goodwill. Such choice of the model can be regarded as a stochastic modification of the classical Nerlove-Arrow model that allows to incorporate both presence of uncertainty and empirically observed memory effects such as carryover or distributed forgetting. We present an approach to solve such optimal control problems based on an infinite dimensional lift which allows us to recover Markov properties by formulating a optimization problem equivalent to the original one in a Hilbert space. Such technique, however, requires the Volterra kernel from the forward equation to have a representation of a particular form that may be challenging to obtain in practice. We overcome this issue for H\"older continuous kernels by approximating them with Bernstein polynomials (which turn out to enjoy a simple representation of the required type) and then solving the optimal control problem for the forward process with approximated kernel instead of the original one. The approach is illustrated with simulations.