Learning Schemes in Evolutionary Game Theory: Application to a Model of Entry in a Regulated Market

In this paper we investigate different learning schemes in dynamic game theory and consider their relative importance when constructing strategic decision models for economic and business applications. The different models of learning dynamics we have formulated fall into three main categories: repeated games, discrete-time replicator games and discrete-time best-response games. Repeated games are a basic repetition of one-shot games, replicator games are inspired from applications of EGT to evolutionary biology, and best-response games combine to some extent many of the desirable properties of evolutionary learning with a more realistic economic interpretation of the decision making process. In all three approaches, the models are considered in their basic deterministic form as well as when stochastic behaviour and the effects of a variable memory of past moves and discounting are included. As a specific example, we run computer simulations of a simplified market-entry model in the context of the telecommunication industry. There are four players in the game: the incumbent, the market follower, the potential entrant, and the regulator. Each player has its own strategy set and characteristics, such as extent of adaptation and uncertainty about the environment. One important conclusion is that the choice of learning mechanism in the formulation of dynamic games has a drastic effect on the results obtained and is likely to be highly context dependent. Our results show that each approach has its merits and shortcomings. In particular, the use of best-response games with mixed strategy updating seems to offer a richer behavior range than its repeated game counterpart and a more realistic (short-term) evolution of the players' behaviors than the equivalent replicator game. We conclude by summarizing the main points and suggesting directions for future work.