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Welcome to New Users
The ANP software implements Thomas Saaty's Analytic Network Process as described in his book, The Analytic Network Process: Decision Making with Dependence and Feedback, and it is ready to make its first public appearance. The development of this version of the software was supported by the Creative Decisions Foundation, and it is now available for beta testers to try it out.
Getting the Software
To obtain the software you need only register, click download, and indicate your agreement to abide by the beta tester's license. Download into a temp directory on your hard drive. You can choose to download a single file of about 2.5 meg or two smaller files each of which fits on a disk. Currently we are releasing only an alpha version. So please select the Alpha version on the download page. We would appreciate it if you would get back to us with any questions or bugs you encounter. You may use the Help, Submit Bug command on the ANP software main menu to submit a bug - you must be connected to the Internet to do this. This automatically gives us information about the version you have and the machine and system you are running. Alternately, you may go to the Bug Submission area on the ANP software page and submit a bug or change request that way.
Install the ANP softwear by running the program file with the .exe extension. If you downloaded the single file, it has a .exe extension. If you downloaded the two files, the first has the .exe extension - so run it; the installation program will pick up the second file when it needs it. Some browsers currently have a bug that can add an extra dot to the file name when downloading the second file of the 2-disk version. So if you download the 2-disk version, please make sure the second file is named fullanp.WO2, and not fullanp..WO2. Re-name the second file before installing using Windows Explorer, if necessary. Remember to re-boot your machine after the installation.
The Tutorial - Suggestions for Getting Started with the Software
There is a tutorial available as a word document. You can download it by clicking on the Tutorials and Demos button on the ANP page. Although you can get help directly in the software by clicking on the Help button, we think that a quick reading of the tutorial will give you a better overall perspective and get you up and running sooner. Please read the tutorial. You can get all three models discussed there, Bridge.mod, Hamburger.mod, and Car_BCR.mod, from the sample models directory. At the end of the tutorial there is a walkthrough that will guide you step-by-step through constructing a model.
Bridge.mod is a straightforward decision problem of picking the best bridge. It is a simple network of one level that contains two clusters, the nodes within the clusters, and their (directed)links. Clusters, nodes and links comprise the structure of any network. We use this model to show how feedback pairwise comparison questions are formulated and how judgments are made. The clusters in this model are outer dependent, that is, nodes in any cluster are compared only with respect to nodes in another cluster.
The Hamburger.mod model, one of the earliest applications of the ANP that predicted the market share of three fast-food hamburger joints, is richer than the bridge model. Some of its clusters are inner dependent with nodes in them being compared with respect to other nodes in the same cluster. Also, it has cluster comparisons as well as node comparisons. We use this model to explain cluster comparisons. It is still a simple network model as it has only one level.
The Car_BCR.mod model is a complex model consisting of a top-level control model with subnetworks attached to nodes in the control model. This example has a very simple control model containing a Benefits node, a Costs node and a Risks node, each of which has a subnetwork. Each subnetwork is attached to a node in the control network. We usually call the control nodes control criteria.The top-level control model may contain hierarchies, or even networks - we will tell you more about that in later news releases. We are still working on the best way to present the control model concept. In the present beta version, the user has a free hand to construct any kind of control model.
Background: Why use Decision-making Software that can do Feedback?
In decision-making it has long been observed that it is not strictly a top-down process carried out by judging how well the alternatives of choice perform on the criteria. The criteria themselves are often dependent on the available alternatives. This calls for some kind of iteration or feedback process. The top down approach used in a typical AHP model has a goal at the top, criteria beneath it that are evaluated for their importance to the goal by doing pairwise comparisons, perhaps subcriteria evaluated pairwise in terms of the criteria, and finally alternatives at the bottom of the model evaluated in terms of the bottom level of (sub)criteria. Such a top down approach assumes one can say how important the criteria are - without even considering what the alternatives are. But that is not true in many decision problems. Consider this: when cars with power steering first started coming on the market, power steering was an important criterion in ranking cars. Some had it, some did not. But later when all the cars had power steering, it was no longer an important distinguishing criterion. To sum up, criteria can depend on alternatives, too, and this calls for feedback.
So one formulates two kinds of questions. The first is: for criterion one, is alternative A or alternative B more preferred, and by how much? The second is: for alternative A, is criterion one or criterion two the more preferable thing about it? Answering such simple pairwise comparison questions on all the elements in the problem that influence each other and synthesizing the results throughout the structure leads to preference rankings. In feedback networks, it is essential to keep the questions formulated so that the influence or preference direction is the same.