Competition among Payment Networks using Generalized Population Based Incremental Learning
AbstractWe 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.
Download InfoTo our knowledge, this item is not available for download. To find whether it is available, there are three options:
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2006 with number 311.
Date of creation: 04 Jul 2006
Date of revision:
agent-based computational economics; evolutionary computation;
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum).
If references are entirely missing, you can add them using this form.