An overview of reconstructability analysis
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paper_title: RECONSTRUCTABILITY ANALYSIS FOR GENERAL FUNCTIONS

paper_content:

Abstract The new discipline of reconstructability analysis has provided a powerful framework for the study of the relationships between parts and wholes. The concentration of effort has been on systems with probabilistic or possibilistic behavior functions. This paper extends aspects of reconstructability analysis to general functions which need not be behavior functions. We refer to a system with such a function as a g-system. First, a g-system is transformed to a dimensionless form (borrowing a term from partial differential equations). A mathematical structure is then induced via a type of isomorphism onto this system which renders it amenable to analysis by established techniques in reconstructability analysis. Absolutely no restrictions are placed on the units or mathematical structure of the g-system. We refer to the system induced from the g-system as a Klir system or k-system. These systems are named in honor of the reconstructability analysis founder. Finally, we explicate some uses of k-systems ...
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paper_title: K-SYSTEMS VERSUS CLASSICAL MULTIVARIATE SYSTEMS

paper_content:

K-systems exist in general systems theory for the study of the relationships between parts and wholes. We show here that K-systems analysis can be used to solve problems in classical multivariate analysis, and solve them more effectively than existing methods. To demonstrate this, we take on “the analysis of variance” in this paper. The analysis of variance is the most important and powerful technique in the field of statistical inference (quoted from Probability and Statistics by Hines and Montgomery). We apply K-systems analysis to the analysis of variance problem, and we compare the two techniques. We see that K-systems analysis can provide more information. But the major difference is that the analysis of variance guesses a model for the data whereas K-systems analysis uses “the” model that is true and correct for the data. Further, K-systems theory delivers a surprising and important insight: it is generally incorrect to use any model where effects and interactions are represented statically over sub...
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paper_title: RECONSTRUCTABILITY ANALYSIS FOR GENERAL FUNCTIONS

paper_content:

Abstract The new discipline of reconstructability analysis has provided a powerful framework for the study of the relationships between parts and wholes. The concentration of effort has been on systems with probabilistic or possibilistic behavior functions. This paper extends aspects of reconstructability analysis to general functions which need not be behavior functions. We refer to a system with such a function as a g-system. First, a g-system is transformed to a dimensionless form (borrowing a term from partial differential equations). A mathematical structure is then induced via a type of isomorphism onto this system which renders it amenable to analysis by established techniques in reconstructability analysis. Absolutely no restrictions are placed on the units or mathematical structure of the g-system. We refer to the system induced from the g-system as a Klir system or k-system. These systems are named in honor of the reconstructability analysis founder. Finally, we explicate some uses of k-systems ...
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paper_title: Architecture of Systems Problem Solving

paper_content:

1: Introduction.- 1.1. Systems Science.- 1.2. Systems Problem Solving.- 1.3. Hierarchy of Epistemological Levels of Systems.- 1.4. The Role of Mathematics.- 1.5. The Role of Computer Technology.- 1.6. Architecture of Systems Problem Solving.- Notes.- 2: Source and Data Systems.- 2.1. Objects and Object Systems.- 2.2. Variables and Supports.- 2.3. Methodological Distinctions.- 2.4. Discrete Versus Continuous.- 2.5. Image Systems and Source Systems.- 2.6. Data Systems.- Notes.- Exercises.- 3: Generative Systems.- 3.1. Empirical Investigation.- 3.2. Behavior Systems.- 3.3. Methodological Distinctions.- 3.4. From Data Systems to Behavior Systems.- 3.5. Measures of Uncertainty.- 3.6. Search for Admissible Behavior Systems.- 3.7. State-Transition Systems.- 3.8. Generative Systems.- 3.9. Simplification of Generative Systems.- 3.10. Systems Inquiry and Systems Design.- Notes.- Exercises.- 4: Structure Systems.- 4.1. Wholes and Parts.- 4.2. Systems, Subsystems, Supersystems.- 4.3. Structure Source Systems and Structure Data Systems.- 4.4. Structure Behavior Systems.- 4.5. Problems of Systems Design.- 4.6. Identification Problem.- 4.7. Reconstruction Problem.- 4.8. Reconstructability Analysis.- 4.9. Simulation Experiments.- 4.10. Inductive Reasoning.- 4.11. Inconsistent Structure Systems.- Notes.- Exercises.- 5: Metasystems.- 5.1. Change versus Invariance.- 5.2. Primary and Secondary Systems Traits.- 5.3. Metasystems.- 5.4. Metasystems versus Structure Systems.- 5.5. Multilevel Metasystems.- 5.6. Identification of Change.- Notes.- Exercises.- 6: Complexity.- 6.1. Complexity in Systems Problem Solving.- 6.2. Three Ranges of Complexity.- 6.3. Measures of Systems Complexity.- 6.4. Bremermann's Limit.- 6.5. Computational Complexity.- 6.6. Complexity Within GSPS.- Notes.- Exercises.- 7: Goal-Oriented Systems.- 7.1. Primitive, Basic, and Supplementary Concepts.- 7.2. Goal and Performance.- 7.3. Goal-Oriented Systems.- 7.4. Structure Systems as Paradigms of Goal-Oriented Behavior Systems.- 7.5. Design of Goal-Oriented Systems.- 7.6. Adaptive Systems.- 7.7. Autopoietic Systems.- Notes.- Exercises.- 8: Systems Similarity.- 8.1. Similarity.- 8.2. Similarity and Models of Systems.- 8.3. Models of Source Systems.- 8.4. Models of Data Systems.- 8.5. Models of Generative Systems.- 8.6. Models of Structure Systems.- 8.7. Models of Metasystems.- Notes.- Exercises.- 9: GSPS: Architecture, USE, Evolution.- 9.1. Epistemological Hierarchy of Systems: Formal Definition.- 9.2. Methodological Distinctions: A Summary.- 9.3. Problem Requirements.- 9.4. Systems Problems.- 9.5. GSPS Conceptual Framework: Formal Definition.- 9.6. Overview of GSPS Architecture.- 9.7. GSPS Use: Some Case Studies.- 9.8. GSPS Evolution.- Notes.- Exercises.- Appendices.- A: List of Symbols.- B: Glossary of Relevant Mathematical Terms.- C: Some Relevant Theorems.- D: Refinement Lattices.- E: Classes of Structures Relevant to Reconstructability Analysis.- References.- Author Index.
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paper_title: K-systems analysis for determining the factors influencing benthic microfloral productivity in a Louisiana estuary, USA

paper_content:

About 3000 sediment cores from Baratana Estuary, Louisiana, USA, were incubated between 26 Jul and 25 Aug 1983. For the month, benthic microfloral production measured at a subtidal site consisting of muddy sand was almost twice that measured at a sandy intertidal site (27 g C m-' vs 14 g C m-'). However, maximum productivity rates (230 mg C m-' h') were greatest at the sandy site; these rates occurred under periods of subaerial exposure which were limited to late afternoons on 11 d of the study, yet accounted for 43 % of the total monthly production at the sand site. Other variables monitored were chlorophyll a, light intensity, water temperature, salinity, benthic community respiration, productivity of the water column, biological activity (muddy sand site), wave height (sand site), meteorological tides (wind-induced water levels), tide height, and initial dissolved oxygen. Multichannel information analysis indicated that the information accounted for in productivity by these variables peaked at slightly less than 7 and 3 d periods for the sand site and weak peaks occurred at about 10 and 3 d periods for the muddy sand site. K-systems analysis indicated that the environmental variables acted in combination to influence productivity, and that the combinations changed through time. Physical processes (meteorologic, astronomic, and anthropogenic) largely controlled productivity at the sand site. Productivity at the muddy sand site was influenced by a combination of physical and biological activities.
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paper_title: State-Based Reconstructability Modeling for Decision Analysis

paper_content:

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.
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paper_title: EXTRACTING INFORMATION FROM ECOLOGICAL DATA CONTAINING HIGH SPATIAL AND TEMPORAL VARIABILITY: BENTHIC MICROFLORAL PRODUCTION

paper_content:

About 1,000 sediment cores from a salt water lagoon in Ventura County, CA were incubated between September 6 and October 6, 1981. The data were analyzed using stepwise regression, time series, multichannel information, and entropy data analysis. Stepwise regression analyses were not informative in explaining changes in productivity because the correlations between productivity and individual environmental variables (chlorophyll a, incident radiation, water temperature, mean tidal range, hours of subaerial exposure, benthic community respiration, pheophytin a/chlorophyll a, and initial dissolved oxygen concentration) varied through time. Time series and multichannel information analysis were used to uncover dynamic behavior in the data and to optimize sampling strategy. The information analysis indicated that, for a long range study, considerably more information could be obtained by concentrating samples into a 14-day interval within each month, rather than sampling throughout the month. Entropy data anal...
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paper_title: Discovering Noncritical Organization: Statistical Mechanical, Information Theoretical, and Computational Views of Patterns in One-Dimensional Spin Systems

paper_content:

We compare and contrast three different, but complementary views of "structure" and "pattern" in spatial processes. For definiteness and analytical clarity we apply all three approaches to the simplest class of spatial processes: one-dimensional Ising spin systems with finite-range interactions. These noncritical systems are well suited for this study since the change in structure as a function of system parameters is more subtle than that found in critical systems where, at a phase transition, many observables diverge thereby making the detection of change in structure obvious. This survey demonstrates that the measures of pattern from information theory and computational mechanics differ from known thermodynamic and statistical mechanical functions. Moreover, they capture important structural features that are otherwise missed. In particular, a type of mutual information called the excess entropy---an information theoretic measure of memory---serves to detect ordered, low entropy density patterns. It is superior in many respects to other functions used to probe the structure of a configuration distribution, such as magnetization and structure factors. $\epsilon$-Machines---the main objects of computational mechanics---are seen to be the most direct approach to revealing the (group and semigroup) symmetries possessed by the spatial patterns and to estimating the minimum amount of memory required to reproduce the configuration ensemble, a quantity known as the statistical complexity. Finally, we argue that the information theoretic and computational mechanical analyses of spatial patterns capture the intrinsic computational capabilities in spin systems---how they store, transmit, and manipulate configurational information to produce spatial structure.
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paper_title: Control uniqueness in reconstructability analysis

paper_content:

Abstract When the reconstructability analysis of a directed system yields a structure in which a generated variable appears in more than one subsystem, information from all of the subsystems can be used in modeling the relationship between generating and generated variables. The conceptualization and procedure proposed here is discussed in relation to Klir's concept of control uniqueness
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paper_title: Architecture of Systems Problem Solving

paper_content:

1: Introduction.- 1.1. Systems Science.- 1.2. Systems Problem Solving.- 1.3. Hierarchy of Epistemological Levels of Systems.- 1.4. The Role of Mathematics.- 1.5. The Role of Computer Technology.- 1.6. Architecture of Systems Problem Solving.- Notes.- 2: Source and Data Systems.- 2.1. Objects and Object Systems.- 2.2. Variables and Supports.- 2.3. Methodological Distinctions.- 2.4. Discrete Versus Continuous.- 2.5. Image Systems and Source Systems.- 2.6. Data Systems.- Notes.- Exercises.- 3: Generative Systems.- 3.1. Empirical Investigation.- 3.2. Behavior Systems.- 3.3. Methodological Distinctions.- 3.4. From Data Systems to Behavior Systems.- 3.5. Measures of Uncertainty.- 3.6. Search for Admissible Behavior Systems.- 3.7. State-Transition Systems.- 3.8. Generative Systems.- 3.9. Simplification of Generative Systems.- 3.10. Systems Inquiry and Systems Design.- Notes.- Exercises.- 4: Structure Systems.- 4.1. Wholes and Parts.- 4.2. Systems, Subsystems, Supersystems.- 4.3. Structure Source Systems and Structure Data Systems.- 4.4. Structure Behavior Systems.- 4.5. Problems of Systems Design.- 4.6. Identification Problem.- 4.7. Reconstruction Problem.- 4.8. Reconstructability Analysis.- 4.9. Simulation Experiments.- 4.10. Inductive Reasoning.- 4.11. Inconsistent Structure Systems.- Notes.- Exercises.- 5: Metasystems.- 5.1. Change versus Invariance.- 5.2. Primary and Secondary Systems Traits.- 5.3. Metasystems.- 5.4. Metasystems versus Structure Systems.- 5.5. Multilevel Metasystems.- 5.6. Identification of Change.- Notes.- Exercises.- 6: Complexity.- 6.1. Complexity in Systems Problem Solving.- 6.2. Three Ranges of Complexity.- 6.3. Measures of Systems Complexity.- 6.4. Bremermann's Limit.- 6.5. Computational Complexity.- 6.6. Complexity Within GSPS.- Notes.- Exercises.- 7: Goal-Oriented Systems.- 7.1. Primitive, Basic, and Supplementary Concepts.- 7.2. Goal and Performance.- 7.3. Goal-Oriented Systems.- 7.4. Structure Systems as Paradigms of Goal-Oriented Behavior Systems.- 7.5. Design of Goal-Oriented Systems.- 7.6. Adaptive Systems.- 7.7. Autopoietic Systems.- Notes.- Exercises.- 8: Systems Similarity.- 8.1. Similarity.- 8.2. Similarity and Models of Systems.- 8.3. Models of Source Systems.- 8.4. Models of Data Systems.- 8.5. Models of Generative Systems.- 8.6. Models of Structure Systems.- 8.7. Models of Metasystems.- Notes.- Exercises.- 9: GSPS: Architecture, USE, Evolution.- 9.1. Epistemological Hierarchy of Systems: Formal Definition.- 9.2. Methodological Distinctions: A Summary.- 9.3. Problem Requirements.- 9.4. Systems Problems.- 9.5. GSPS Conceptual Framework: Formal Definition.- 9.6. Overview of GSPS Architecture.- 9.7. GSPS Use: Some Case Studies.- 9.8. GSPS Evolution.- Notes.- Exercises.- Appendices.- A: List of Symbols.- B: Glossary of Relevant Mathematical Terms.- C: Some Relevant Theorems.- D: Refinement Lattices.- E: Classes of Structures Relevant to Reconstructability Analysis.- References.- Author Index.
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paper_title: Reconstructability analysis using probability intervals

paper_content:

It is proposed that probability intervals be used in reconstructability analysis. A probability interval is a subinterval of the real interval [0,1]. Regarded as “interval-valued probabilities”, these intervals generalize real-valued probabilities and arise naturally in many situations. They may represent confidence intervals resulting from sampling; imprecisely stated subjective probabilities; known linear equality or inequality constraints; etc. Thus, probability intervals are sometimes a more realistic characterization of uncertainty than are real-valued probabilities. Furthermore, the problem of inconsistency can often be avoided by their use Although the utility of interval-valued probability distributions for the identification problem is emphasized, a reconstruction technique is also developed. This reconstruction method employs a metric distance for interval distributions that is monotonic with respect to model refinement.
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paper_title: Covariance Structure Models: An Introduction to LISREL

paper_content:

While many readers may be unfamiliar with the full complexity of the covariance structure model, many may have mastered at least one of its two components, each of which is a powerful and well-known statistical technique in its own right. The first is the confirmatory factor model frequently used in psychometrics; the second, the structural equation model, is familiar to econometricians. The discussion in this volume will be particularly useful for estimating models with equality constraints and correlated errors across some but not all equations. The final chapter includes a guide to appropriate software packages.
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paper_title: Multi-valued functional decomposition as a machine learning method

paper_content:

In the past few years, several authors have presented methods of using functional decomposition as applied to machine learning. These authors explore the ideas of functional decomposition, but left the concepts of machine learning to the papers that they reference. In general, they never fully explain why a logic synthesis method should be applied to machine learning. This paper explores and presents the basic concepts of machine learning, and how some concepts match nicely with multi-valued logic synthesis, while others pose great difficulties. The main reason for using multi-valued synthesis is that many problems are naturally multi-valued (i.e., values taken from a discrete set). Thus, mapping the problem directly to a multi-valued set of inputs and outputs is much more natural than encoding the problem into a binary form. The paper also shows that any multi-valued logic synthesis method could be applied to the machine learning problem. But, this paper focuses on multivalued functional decomposition because of its generality of minimizing a given data set.
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paper_title: Qualitative reasoning for system reconstruction using Lebesgue discretization

paper_content:

System reconstruction refers to the following problem: given a behaviour system, viewed as an overall system, determine which sets of its subsystems are adequate for reconstructing the given system with an acceptable degree of approximation. The reconstruction problem can be solved by finding multiple variable relationships in complex data. To deal with this problem, we apply the idea of qualitative reasoning as discussed in artificial intelligence. The basic idea is to convert continuous data into discrete data (e.g. through clustering) so that qualitative rules can be constructed. In this paper a method of compressing data (called the Lebesgue discretization) is proposed to establish relationships between the dependent variable and other variables. Qualitative rules can be constructed from this relationship and then stored in a knowledge base. This technique also facilitates automated knowledge acquisition.
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paper_title: Architecture of Systems Problem Solving

paper_content:

1: Introduction.- 1.1. Systems Science.- 1.2. Systems Problem Solving.- 1.3. Hierarchy of Epistemological Levels of Systems.- 1.4. The Role of Mathematics.- 1.5. The Role of Computer Technology.- 1.6. Architecture of Systems Problem Solving.- Notes.- 2: Source and Data Systems.- 2.1. Objects and Object Systems.- 2.2. Variables and Supports.- 2.3. Methodological Distinctions.- 2.4. Discrete Versus Continuous.- 2.5. Image Systems and Source Systems.- 2.6. Data Systems.- Notes.- Exercises.- 3: Generative Systems.- 3.1. Empirical Investigation.- 3.2. Behavior Systems.- 3.3. Methodological Distinctions.- 3.4. From Data Systems to Behavior Systems.- 3.5. Measures of Uncertainty.- 3.6. Search for Admissible Behavior Systems.- 3.7. State-Transition Systems.- 3.8. Generative Systems.- 3.9. Simplification of Generative Systems.- 3.10. Systems Inquiry and Systems Design.- Notes.- Exercises.- 4: Structure Systems.- 4.1. Wholes and Parts.- 4.2. Systems, Subsystems, Supersystems.- 4.3. Structure Source Systems and Structure Data Systems.- 4.4. Structure Behavior Systems.- 4.5. Problems of Systems Design.- 4.6. Identification Problem.- 4.7. Reconstruction Problem.- 4.8. Reconstructability Analysis.- 4.9. Simulation Experiments.- 4.10. Inductive Reasoning.- 4.11. Inconsistent Structure Systems.- Notes.- Exercises.- 5: Metasystems.- 5.1. Change versus Invariance.- 5.2. Primary and Secondary Systems Traits.- 5.3. Metasystems.- 5.4. Metasystems versus Structure Systems.- 5.5. Multilevel Metasystems.- 5.6. Identification of Change.- Notes.- Exercises.- 6: Complexity.- 6.1. Complexity in Systems Problem Solving.- 6.2. Three Ranges of Complexity.- 6.3. Measures of Systems Complexity.- 6.4. Bremermann's Limit.- 6.5. Computational Complexity.- 6.6. Complexity Within GSPS.- Notes.- Exercises.- 7: Goal-Oriented Systems.- 7.1. Primitive, Basic, and Supplementary Concepts.- 7.2. Goal and Performance.- 7.3. Goal-Oriented Systems.- 7.4. Structure Systems as Paradigms of Goal-Oriented Behavior Systems.- 7.5. Design of Goal-Oriented Systems.- 7.6. Adaptive Systems.- 7.7. Autopoietic Systems.- Notes.- Exercises.- 8: Systems Similarity.- 8.1. Similarity.- 8.2. Similarity and Models of Systems.- 8.3. Models of Source Systems.- 8.4. Models of Data Systems.- 8.5. Models of Generative Systems.- 8.6. Models of Structure Systems.- 8.7. Models of Metasystems.- Notes.- Exercises.- 9: GSPS: Architecture, USE, Evolution.- 9.1. Epistemological Hierarchy of Systems: Formal Definition.- 9.2. Methodological Distinctions: A Summary.- 9.3. Problem Requirements.- 9.4. Systems Problems.- 9.5. GSPS Conceptual Framework: Formal Definition.- 9.6. Overview of GSPS Architecture.- 9.7. GSPS Use: Some Case Studies.- 9.8. GSPS Evolution.- Notes.- Exercises.- Appendices.- A: List of Symbols.- B: Glossary of Relevant Mathematical Terms.- C: Some Relevant Theorems.- D: Refinement Lattices.- E: Classes of Structures Relevant to Reconstructability Analysis.- References.- Author Index.
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paper_title: Information-theoretic feature selection for a neural behavioral model

paper_content:

Employers of hourly workers typically experience high employee turnover. Due to costs associated with: training, hiring and termination, the overhead from this high turnover rate is substantial. It is therefore desirable to construct employee selection procedures and analytic models to estimate the likely tenure of applicants for employment prior to a hiring decision. A critical component in the success of this effort to create a neural network model to estimate tenure was the application of information-theoretic feature selection. The benefits of this technique are demonstrated by comparison with results obtained using no feature selection and alternate methods of feature selection.
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paper_title: Prestructuring Neural Networks for Pattern Recognition Using Extended Dependency Analysis

paper_content:

We consider the problem of matching domain-specific statistical structure to neural-network (NN) architecture. In past work we have considered this problem in the function approximation context; here we consider the pattern classification context. General Systems Methodology tools for finding problem-domain structure suffer exponential scaling of computation with respect to the number of variables considered. Therefore we introduce the use of Extended Dependency Analysis (EDA), which scales only polynomially in the number of variables, for the desired analysis. Based on EDA, we demonstrate a number of NN pre-structuring techniques applicable for building neural classifiers. An example is provided in which EDA results in significant dimension reduction of the input space, as well as capability for direct design of an NN classifier.
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paper_title: Directed Extended Dependency Analysis for Data Mining

paper_content:

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.
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paper_title: Reconstructability Analysis Detection Of Optimal Gene Order In Genetic Algorithms

paper_content:

The building block hypothesis implies that genetic algorithm efficiency will be improved if sets of genes that improve fitness through epistatic interaction are near to one another on the chromosome. We demonstrate this effect with a simple problem, and show that information‐theoretic reconstructability analysis can be used to decide on optimal gene ordering.
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paper_title: Improving ANN generalization using a priori knowledge to pre-structure ANNs

paper_content:

This is a continuation of work reported by Lendaris at el. (1994) whose objective has been to develop a method that uses certain a priori information about a problem domain to pre-structure artificial neural networks (ANNs) into modules before training. The method is based on a general systems theory methodology, based on information-theoretic ideas, that generates structural information of the problem domain by analyzing I/O pairs from that domain. The notion of performance subset of an ANN structure is described. Extensive experiments on 5-input/1-output and 7-input/1-output Boolean mappings show that significantly improved generalization follows from successful pre-structuring. As the previous work already showed, such pre-structuring also yields improved training speed.
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paper_title: Prestructuring Neural Networks for Pattern Recognition Using Extended Dependency Analysis

paper_content:

We consider the problem of matching domain-specific statistical structure to neural-network (NN) architecture. In past work we have considered this problem in the function approximation context; here we consider the pattern classification context. General Systems Methodology tools for finding problem-domain structure suffer exponential scaling of computation with respect to the number of variables considered. Therefore we introduce the use of Extended Dependency Analysis (EDA), which scales only polynomially in the number of variables, for the desired analysis. Based on EDA, we demonstrate a number of NN pre-structuring techniques applicable for building neural classifiers. An example is provided in which EDA results in significant dimension reduction of the input space, as well as capability for direct design of an NN classifier.
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paper_title: On Matching ANN Structure to Problem Domain Structure

paper_content:

To achieve reduced training time and improved generalization with artificial neural networks (ANN, or NN), it is important to use a reduced complexity NN structure. A "problem" is defined by constraints among the variables describing it. If knowledge about these constraints could be obtained a priori, this could be used to reduce the complexity of the ANN before training it. Systems theory literature contains methods for determining and representing structural aspects of constrained data (these methods are herein called GSM, general systems method). The suggestion here is to use the GSM model of the given data as a pattern for modularizing a NN prior to training it. The present work assumes the GSM model for the given problem context has been determined (represented here in the form of Boolean functions of known decompositions). This means that certain information is available about constraint among the system variables, and is used to develop a modularized NN. The modularized NN and an equivalent general NN (full interconnect, feed-forward NN) are both trained on the same data. Various predictions are offered: 1) The general NN and the modularized NN will both learn the task, but the modularized NN will learn it faster. 2) If trained on an (appropriate) subset of possible inputs, the modularized NN will perform better generalization than the general NN. 3) If trained on a non-decomposable function of the same variables, the general NN will learn the task, but the modularized NN will not. All of these predictions are verified experimentally. Future work will explore more decomposition types and more general data types.
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paper_title: On temporal characteristics of behavior in certain complex systems

paper_content:

Little is known about the behavior of dynamic systems with many intricately interacting parts, and about the factors which tend to affect their behavior in general, rather than detailed, ways. This paper describes a study of such systems built up out of unit elements which compute recursive logical functions.
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paper_title: The Structure of the Elementary Cellular Automata Rule Space

paper_content:

A new bi-colored miniature rose plant which has abundant flowers and good keepability. The variety successfully propagates from softwood cuttings and is suitable for year round production in commercial glasshouses. This new and distinct variety has shown to be uniform and stable in the resulting generations from such asexual propagation.
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paper_title: A Software Architecture for Reconstructability Analysis

paper_content:

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.
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paper_title: Directed Extended Dependency Analysis for Data Mining

paper_content:

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.
---
paper_title: State-Based Reconstructability Modeling for Decision Analysis

paper_content:

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.
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paper_title: SAPS — A Software System for Inductive Modelling

paper_content:

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.
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paper_title: SAPS-II: A new implementation of the systems approach problem solver

paper_content:

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.
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paper_title: Genetic algorithms : A search technique applied to behavior analysis

paper_content:

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
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paper_title: Implicit Representation of Discrete Objects

paper_content:

This tutorial paper discusses the known representations based on Binary Decision Diagrams (BDDs) for various types of discrete objects: incompletely specified functions, sets, finite state machines, binary and multi-valued relations, etc. While presenting the known material, the emphasis is on those aspects of implicit representations that are important to achieve speed-up in computation. The new material includes implicit representations for dichotomies, partitions, set systems, and information measures. The last type of objects, information measures, constitute a promising approach to problem solving in a number of areas, including decomposition of discrete functions and finite state machines. Multi-valued relations (MVRs) are presented as the most general representation for all the considered classes on discrete objects. Two complementary ways of representing MVRs are proposed: binary-encoded multivalued decision diagrams (BEMDDs) and labeled rough partitions (LRPs). The sizes of BEMDDs and LRPs are compared using a set of multi-valued benchmarks.
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paper_title: Reconstructability Analysis With Fourier Transforms

paper_content:

Fourier methods used in two‐ and three‐dimensional image reconstruction can be used also in reconstructability analysis (RA). These methods maximize a variance‐type measure instead of information‐theoretic uncertainty, but the two measures are roughly collinear and the Fourier approach yields results close to that of standard RA. The Fourier method, however, does not require iterative calculations for models with loops. Moreover, the error in Fourier RA models can be assessed without actually generating the full probability distributions of the models; calculations scale with the size of the data rather than the state space. State‐based modeling using the Fourier approach is also readily implemented. Fourier methods may thus enhance the power of RA for data analysis and data mining.
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Title: An Overview of Reconstructability Analysis

Section 1: Introduction
Description 1: Introduce the concept and importance of reconstructability analysis (RA), its applications, and relation to other methodologies such as log-linear (LL) statistical methods and logic design and machine learning (LDL).

Section 2: More Detailed Explanation
Description 2: Provide a detailed explanation of information-theoretic RA (especially IRA), including the basic reconstruction steps of projection, composition, and evaluation.

Section 3: Examples of Variable-based Reconstruction
Description 3: Illustrate variable-based reconstruction with examples, showcasing both exhaustive consideration of models in data analysis and the heuristic use of RA in data mining with multiple variables.

Section 4: Software
Description 4: Discuss the software tools available for conducting RA, including a description of the OCCAM software package and other related programs.

Section 5: Discussion
Description 5: Provide a concluding discussion on the current limitations and potential future developments of RA methodologies, particularly in terms of scalability and application to larger and more complex datasets.