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A suite of chapters discusses symbolic artificial intelligence as an analytic method and its underlying methodology, covering probabilistic graphical models and evidence propagation, influence diagrams, time series modeling, and approximate algorithms such as Monte Carlo simulation, followed by cluster analysis and its relevance to machine learning.
Mapping interactions of trends and weak signals in influence diagrams creates the basis for the inductive scenarios.
In the following section, we will analyze the aforementioned strategic business issues associated with the retailer and computer assembly company global supply chains using closed-loop influence diagrams and system dynamics modeling.
Diagramming techniques: Diagramming techniques, such as system flow charts, cause-and-effect diagrams, and influence diagrams are used to uncover risks that aren't readily apparent in verbal descriptions.
Two of the assessment tools -- decision trees and influence diagrams -- are invaluable for this process.
Definitive Scenario users create influence diagrams to model a given situation.
Also, certain new material like influence diagrams or the analytic hierarchy (AHP) are described to some extent.
Two techniques for creating actual messages are described, influence diagrams and message maps, both of which rely on developing audience empathy.
The creation of influence diagrams helps assure that the key issues and their primary relationships are identified.
The structured approach they discuss involves modeling tools such as influence diagrams, spreadsheet engineering, parameterization, sensitivity and strategy analysis, and iterative modeling.
The system uses influence diagrams to view risks and allows risk owners to "drill down" to observe the causes of risk in any number of areas that impact an organization.
Decision Trees and Influence Diagrams can clearly model your decisions and identify the best decision to make as well as the risk involved.