Using bayesian belief networks for modeling of communication service provider businesses
Research output: Contribution to journal › Conference article › Scientific › peer-review
This paper analyses the usage of Bayesian Belief Networks (BBNs) for Communication Service Provider (CSP) business modeling and simulation. Large and complex BBNs have been created to describe the causal relationships in CSP business domains. As a part of the study, a novel method to collect knowledge from a large number of independent experts living in different countries has been introduced. A BBN from each expert result was created (referred to here as a sub-BBN). Business model ontology was utilized to combine sub-BBNs together into a comprehensive model. The resulting BBN represents typical business circumstances in the European telecommunications domain. The experts participating in the study represented expertise in different business related categories such as technology, processes, customer experience, regulation, organization and products. Experts were asked to list causality triplets for business categories including causal connection strengths, in order to assess the belief part as well. The triplets were manually converted to a graphical causal map and conditional probability tables constructed. The benefit of the method is the capability to introduce rapidly a high number of variables and causal relationships. A challenge is that experts use different terms with the same underlying meaning.
|Number of pages||9|
|Journal||CEUR Workshop Proceedings|
|Publication status||Published - 2011|
|MoE publication type||A4 Article in a conference publication|