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General Systems Methodology and Political Science
Abstract
As part of an earlier presentation to this congress Cavallo and Klir characterized the situation regarding the role of general systems research with respect to the social sciences. Fundamental to their presentation was an attitude toward epistemology and science which places primary emphasis on the process-related aspects of research and knowledge acquisition rather than on the finished-research-report status accorded to “theory” by many positivist accounts. In this regard their position parallels that of the physicist Werner Heisenberg who perceived that “in the development of natural science [it] will be rather the exception than the rule… [that a] sentence belongs to a closed system of concepts and axioms [Heisenberg 1958].” Their development of the notion of a general systems problem solver (GSPS), which is methodologically oriented, is based on a view also similar to that of a Nobel physicist, Neils Bohr, who argues that “physics is to be regarded not so much as the study of something a priori given, but rather as the development of methods for ordering and surveying human experience [Bohr 1963].”
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This paper is concerned with establishing broadly based system theoretic foundations and practical techniques for the problem of system identification that are rigorous, intuitively clear and conceptually powerful. A general formulation is first given in which two order relations are postulated on a class of models: a constant one of complexity and a variable one of approximation induced by an observed behaviour. An admissible model is such that any less complex model is a worse approximation. The general problem of identification is that of finding the admissible subspace of models induced by a given behaviour. It is proved under very general assumptions that, if deterministic models are required, then nearly all behaviours require models of nearly maximum complexity. A general theory of approximation between models and behaviour is then developed based on subjective probability concepts and semantic information theory. The role of structural constraints such as causality, locality, finite memory, etc., are then discussed as rules of the game. These concepts and results are applied to the specific problem of stochastic automaton, or grammar, inference. Computational results are given to demonstrate that the theory is complete and fully operational. Finally the formulation of identification proposed in this paper is analysed in terms of Klir's epistemological hierarchy and both arc discussed in terms of the rich philosophical literature on the acquisition or knowledge.
The study of varying forms of conflict within nations has long been an important area of focus from within political science. The orientation of much of this research has been on intensive study of single aspects of individual cases or on comparison of one of these aspects — such as revolution — over a small number of cases. In the last decade or so, an alternate orientation has developed that attempts to take advantage of modeling forms that allow the processing of larger quantities of data than had been previously possible. The main focus of this orientation has been on cross-national study of relations among various domestic conflict indicators (e.g., Rummel, 1963, 1965; Tanter, 1966; Firestone and McCormick, 1972; Banks, 1972; Gurr and Bishop, 1976; Hibbs, 1973), with the intention of constructing verifiable general statements about conflict activity.
A summary only is given of this paper. The author explains that the linguistic approch to system analysis is intended to be used in situations in which the behavior of a system is too complex or too ill-defined to admit of analysis by conventional quantitative techniques. The central concept in the linguistic approach is that of a linguistic variable**1. In contrast to a numerical variable, the values of a linguistic variable are not numbers but sentences in a natural or artificial language which represent labels of fuzzy subsets of a universe of discourse. The main function of linguistic variables is to provide an approximate means of characterization of system variables and their interrelations. The author states that by combining assignment and conditional statements of the type exemplified by certain equations shown to illustrate the method, together with fuzzy GO TO statements, one can construct fuzzy algorithms for describing the behavior of a system and measuring its performance. Examples of such algorithms are given in a reference to an earlier paper by the author bearing the same title as the one indexed in Engineering Index 1973, abstract 003612.
A general methodology for empirical investigaiion is described which consists of: (i) the selection of a system on the object of investigation with respect to the purpose and constraints of investigation, (ii) the gathering of data for the system and organizing them in the form of activity arrays, (iii) the processing of the data through which certain time-invariant properties representing the data are determined, (iv) the interpretation of these time-invariant data representations from the standpoint of the purpose of investigation.It is shown that each activity array yields many different representations. A procedure through which the various representations can be obtained is described that consists of: (1) the selection of a mask specifying a pattern for sampling the data, (2) the sampling procedure through which certain types of time-invariant relations are determined for the given mask, (3) the simplification of the time-invariant relations, if necessary or desirable.A basis for the objective comparison of possible representations and ascertainment of the representation with the highest possible degree of determinism, subject to given constraints, is developed.The procedure is applicable to both well defined and fuzzy variables of any scale. No classification into input and output variables is required, although it is not prohibited.
A conceptual framework is presented in this paper within which it is possible to integrate and operationalize results of systems research. The framework is described in terms of an interactive general systems problem solver (GSPS). The paper consists of a discussion of both the motivation for developing GSPS and its implications, and a formal description of its basic aspects. An epistemologieal hierarchy of systems, which underlies the problem solver, and a complete specification of an example of a small version of GSPS are given in two appendices.
A hierarchy of epistemological levels of systems is introduced. It consists of source systems, data systems, generative systems, structure systems, and various levels of metasystems. The problem of structure identification is described as follows: Given a neutral data system, identify the best representation of this system by a directed structure system whose elements are associated with subsets of variables of the data system, A procedure for solving the problem is described. It consists of generating meaningful structure candidates for a given data system, analyzing each of them on the basis of the given data and within the framework of an overall sampling mask, and comparing the results with the data. A partial ordering is introduced for the structure candidates. For each particular number of variables in the data system, the structure candidates together with the partial ordering form a lattice. The ordering in the lattice makes it possible to reach the best structure system by analyzing only a fraction of all possible structure candidates.
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Rejoinder to the Critiques of the Correlates of War Project
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