Markov Models of Advertising and Pricing Decisions

This paper uses Markov decision analysis to study the properties of optimal advertising and pricing decisions in a dynamic, stochastic environment. We are primarily concerned with exhibiting and interpreting properties of the Markovian transition probabilities and one-period reward functions that imply that the optimal advertising levels and optimal prices always increase or always decrease as a function of the firm's market position. This is done in both non-competitive and competitive situations. In the former, total discounted reward for a single firm is the objective, whereas in the latter we use the concept of stochastic games to construct what is known as a discounted equilibrium point for two competing firms.