This paper focuses on joint dynamic pricing and demand learning in an oligopolistic market. Each firm seeks to learn the price-demand
relationship for itself and its competitors, and to set optimal prices, taking into account its competitors’ likely moves.
We follow a closed-loop approach to capture the transient aspect of the problem, that is, pricing decisions are updated dynamically
over time, using the data acquired thus far.
We formulate the problem faced at each time period by each firm as a Mathematical Program with Equilibrium Constraints (MPEC).
We utilize variational inequalities to capture the game-theoretic aspect of the problem. We present computational results
that provide insights on the model and illustrate the pricing policies this model gives rise to.