Presented at the International Institute for General Systems Studies, Second Workshop, Jan. 9-11, 1997, Southwest Texas State University, San Marcos, Texas.
This paper reports an algorithm for the resolution of local inconsistency in information-theoretic identification. This problem was first pointed out by Klir as an important research area in reconstructability analysis. Local inconsistency commonly arises when an attempt is made to integrate multiple data sources, i.e., contingency tables, which have differing common margins. For example, if one has an AB table and a BC table, the B margins obtained from the two tables may disagree. If the disagreement can be assigned to sampling error, then one can arrive at a compromise B margin, adjust the original AB and BC tables to this new B margin, and then obtain the integrated ABC table by the conventional maximum uncertainty solution.
The problem becomes more complicated when the common margins themselves have common margins. The algorithm is an iterative procedure which handles this complexity by sequentially resolving increasingly higher dimensional inconsistencies. The algorithm is justified theoretically by maximum likelihood arguments. It opens up the possibility of many new applications in information theoretic modeling and forecasting. One such application, involving transportation studies in the Portland area, will be briefly discussed.