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Competition among Payment Networks using Generalized Population Based Incremental Learning

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Author Info

  • Biliana Alexandrova Kabadjova

    (University of Essex)

  • Andreas Krause

    (University of Bath)

  • Edward Tsang

    (University of Essex)

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    Abstract

    We have developed an agent-based model for competition among payment cards networks. In our model the competitors in the payment card market learn by observing model-based interactions between costumers and merchants at the point of sale (POS). The interactions are represented on lattice with three different connections: local, small world and random. We are studying how the payment card providers improve their strategies in a competitive market. We are using Generalized Population Based Incremental Learning (GPBIL) algorithm as our machine learning technique to evolve strategies for card purveyors. In our Computational Agent-based model of Competition in the Payment Card Market, we are simulating the interactions among consumers and merchants in a way, which to our knowledge has not been explored previously in the literature. The simulation allows us explicitly represent the network externalities in the use/acceptance of payment cards and model its impact on the consumers/merchants decisions to adopt or drop a particular electronic payment. Additionally the decisions of consumers and merchants regarding the subscription and use of electronic cards are guided by the cost of the payment instruments, which is determinate by the payment card providers.

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    Bibliographic Info

    Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2006 with number 311.

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    Date of creation: 04 Jul 2006
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    Handle: RePEc:sce:scecfa:311

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    Related research

    Keywords: agent-based computational economics; evolutionary computation;

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