Evolutionary Learning of the Optimal Pricing Strategy in an Artificial Payment Card Market

What is it about?

This chapter introduces an artificial payment card market in which we model the interactions between consumers, merchants and competing card issuers with the aim of determining the optimal pricing structure for card issuers. We allow card issuers to charge consumers and merchants fixed fees, provide net benefits from card usage and engage in market activities. The demand by consumers and merchants is only affected by the size of the fixed fees and the optimal pricing structure consists of a sizeable fixed fee to consumers, no fixed fee to merchants, negative net benefits to consumers and merchants as well as a high marketing effort.

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Photo by Markus Spiske on Unsplash

Photo by Markus Spiske on Unsplash

Why is it important?

In this paper we combine two powerfull tools: an agent-based model (ABM) and a learning algorithm. We used the ABM to simulate interactions at the point of sale among consumers and merchants; the learning algorithm is applied by card payment providers to design their business strategies based on the market indicators obtained from the simulation. We find that the demand by consumers and merchants is only affected by the size of the fixed fees and the optimal pricing structure consists of a sizeable fixed fee to consumers, no fixed fee to merchants, negative net benefits to consumers and merchants.

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