An overview of reconstructability analysis <s> IV. Software <s> The main intent of this paper is to demonstrate the use of systems methodology and systems theory in the light of its applicability. One way to accomplish this is by translating the ideas, concepts, theoretical algorithms and propositions into a useful tool. Such a tool can be implemented on a computer using software. The Systems Approach Problem Solver (SAPS) as a package is such an accomplished tool. We apply it here in the context of inductive modelling. We concern ourselves with the identification of data systems, behavioral systems of different forms, structural systems and metasysterns. For each, ample material from applications in recent consulting and research is provided. <s> BIB001 </s> An overview of reconstructability analysis <s> IV. Software <s> Abstract In this paper, we describe a reimplementation of the Systems Approach Problem Solver that was originally designed and implemented by H. J. J. Uyttenhove.1,2 In this reimplementation, emphasis was put on a clean and flexible user interface that allows the user to conveniently combine several of the SAPS basic operations for solving more complex problems. SAPS-II3 was implemented as a toolbox (function library) within the framework of the CTRL-C program,4 an interactive matrix manipulation language developed originally for computer-aided control system design, and enhanced later for other purposes such as signal analysis and statistical operations.5,6 From the point of view of the CTRL-C software, the SAPS-II library can be viewed as yet another enhancement for the purpose of systems analysis and synthesis. From the point of view of the SAPS-II software, CTRL-C can be viewed as a software shell for convenient embedding of the SAPS algorithms. <s> BIB002 </s> An overview of reconstructability analysis <s> IV. Software <s> Abstract Genetic algorithms are powerful generalized search techniques. This paper shows that genetic algorithms can solve a difficult class of problems in general systems theory quickly and efficiently. Genetic algorithms appear to be ideally suited to solving the combinatorially complex problem of behavior analysis. The search space of behavior analysis experiences exponential growth as a function of the number of variables. The genetic algorithm converges after considering a small percentage of these potential solutions. The number of solutions that need to be examined by the genetic algorithm seems to be a polynomial function of the number of variables and, in fact, the growth appears to be linear <s> BIB003 </s> An overview of reconstructability analysis <s> IV. Software <s> Reconstructability analysis (RA) is a method for detecting and analyzing the structure of multivariate categorical data. Jones and his colleagues extended the original variablebased formulation of RA to encompass models defined in terms of system states (Jones 1982; Jones 1985; Jones 1985; Jones 1986; Jones 1989). In this paper, we demonstrate that Jones’ previous work comprises two separable ideas: the “g to k” transformation and state-based modeling. We relate the concept of state-based modeling to established variable-based RA methods (Klir 1985; Krippendorff 1986), and demonstrate that statebased modeling, when applied to event and decision tree models, is a valuable adjunct to the variable-based sensitivity analyses commonly employed in risk and decision modeling. Examples are provided to illustrate the approach, and issues associated with the interpretation of state-based sensitivity analyses are explored. <s> BIB004 </s> An overview of reconstructability analysis <s> IV. Software <s> Software packages for Reconstructability Analysis (RA), as well as for related Log Linear modeling, generally provide a fixed set of functions. Such packages are suitable for end-users applying RA in various domains, but do not provide a platform for research into the RA methods themselves. A new software system, Occam3, is being developed which is intended to address three goals which often conflict with one another: to provide (1) a general and flexible infrastructure for experimentation with RA methods and algorithms; (2) an easily-configured system allowing methods to be combined in novel ways, without requiring deep software expertise; and (3) a system which can be easily utilized by domain researchers who are not computer specialists. Meeting these goals has led to an architecture which strictly separates functions into three layers: the Core, which provides representation of datasets, relations, and models; the Management Layer, which provides extensible objects for development of new algorithms; and the Script Layer, which allows the other facilities to be combined in novel ways to address a particular domain analysis problem. <s> BIB005 </s> An overview of reconstructability analysis <s> IV. Software <s> Extended dependency analysis (EDA) is a heuristic search technique for finding significant relationships between nominal variables in large data sets. The directed version of EDA searches for maximally predictive sets of independent variables with respect to a target dependent variable. The original implementation of EDA was an extension of reconstructability analysis. Our new implementation adds a variety of statistical significance tests at each decision point that allow the user to tailor the algorithm to a particular objective. It also utilizes data structures appropriate for the sparse data sets customary in contemporary data mining problems. Two examples that illustrate different approaches to assessing model quality tests are given in this paper. <s> BIB006
Computations were done using a software package being developed at Portland State University named OCCAM (for the principle of parsimony and as an acronym for "Organizational Complexity Computation And Modeling"). OCCAM is intended eventually to include all data, problem, and method types. Other RA software packages do exist, e.g., CONSTRUCT and SPECTRAL by  , SAPS by BIB001 and BIB002 , GSPS by Klir (1976) ,  , and coworkers, EDA by , Jones' k-systems analysis  ) and a recent program by BIB003    , and then by Doug Anderson who also wrote a program for multipredicting component modeling, and another program for inconsistency resolution for IRA identification  . Hui Shu wrote SRA reconstruction and structure lattice programs. For convenience, the whole set of these earliest RA programs will be called OCCAM0. In this period, Klaus Krippendorff generously provided to us his programs mentioned above and these assisted our research and informed our development efforts. We also utilized GSPS obtained from  . Marcus Daniels combined many functionalities of the Hosseini and Anderson reconstruction programs and Shu's lattice program by rewriting them and adding heuristic search in the multi-predicting component modeling, to produce OCCAM1. Stan Grygiel made innovations and improvements in the single-prediction-component calculations, in search heuristics, and general research usability; this produced OCCAM2. Calculations reported in this paper were done with OCCAM2 and occasionally with earlier separate programs mentioned above. A new program (OCCAM3) has now been written by Ken Willett, which is a more effective research and applications platform BIB005  ) and can be accessed over the web. Willett is also specifically exploring heuristic search and approximate computation approaches. Michael Johnson is programming SBM for future incorporation into the package, integrating it theoretically into the RA framework, and exploring its implications for decision analysis BIB004  . Bjorn Chambless has written a stand-alone single-predicting-component information-theoretic program which includes binning and aggregation preprocessing capacities. Binning for OCCAM is being developed by Michael Johnson and Steve Shervais. Tad Shannon has programmed an updated version of Conant's EDA BIB006 and also a time-series preprocessing utility.