Meta-synthesis approach to complex system modeling

Abstract

Meta-synthesis method is proposed to tackle with open complex giant system problems which cannot be effectively solved by traditional reductionism methods by a Chinese system scientist Qian, Xuesen (Tsien HsueShen) around the early 1990s. The method emphasizes the synthesis of collected information and knowledge of various kinds of experts, and combining quantitative methods with qualitative knowledge. Since then, continuous endeavors have been taken to put those ideas into practice. In this paper, firstly we review meta-synthesis approach and other research relevant to complex system modeling briefly. Then we discuss two main issues, model integration and opinion synthesis, which are often confronted when applying meta-synthesis approach, together with an exhibit of the development of an embryonic meta-synthetic support prototype. Such a demonstration shows how to model complex problems, such as macro-economic problems in Hall for Workshop on Meta-Synthetic Engineering with versatile resources in information collection, model integration and opinion synthesis. Finally, some future work is indicated.

Introduction

Since the 1970s, difficulties confronted in dealing with modeling complex problems, especially in the areas of energy, environment, population, socioeconomic and sustainable development, etc. drove people to change their problem solving ways from simple mathematical modeling to considerations on those factors which had been neglected by quantitative modeling and towards a synthesis of models from different domains on a common problem along a system rethinking trend (Hafele and Basile, 1979; Tomlinson and Kiss, 1984; Flood and Jackson, 1991). Those endeavors reflect the limitations of analytical thinking dealing with human and organizational elements on system design and mathematical modeling for unstructured messy problems. Then a lot of new system approaches have been proposed, such as Ackoff’s interactive planning, Checkland’s soft system methodologies (SSM), Mason and Mitroff’s strategic assumption surfacing and testing (SAST), etc. To be differentiated with those analytical modeling for problem solving which are regarded as hard system approaches, those approaches are referred as soft system approaches. Table 1 lists some comparisons between two categories of system approaches.

There are other sayings, like soft system analysis, soft operational research (OR), etc. which are also regarded as a same category as soft system approaches. Typical soft OR methods are discussed in Rosenhead and Mingers (2001), Mingers and Rosenhead (2004) and Keys (1991). Despite the differences between those soft approaches due to different origins and different applied domains, common grounds behind those approaches are of more attentions; the most salient feature of those approaches is for problem structuring, a basic but very difficult while a continuous goal and task for system analysts, modelers, strategic planners and decision makers. Decision support system (DSS) aims to provide effective support for solving unstructured, ill-structured or wicked problems for decision makers as its initial emergence in the late 1960s. Through more practice, people have gradually realized that for more effective support for problem solving, studying the concerned problem from different perspectives is a necessity for comprehensive definition of the problem, and one of the principal tasks in problem structuring process is how to synthesize those multiple and varied perspectives so as to handle more ‘softer’ information and broader concerns than mathematical models (Shim et al., 2002). Here the research on DSS and soft approaches are overlapped in the methods of problem structuring. System modeling is a dedicated activity of building model-based DSS while structuring process based on soft system approaches is itself a system modeling process.

Nowadays, tremendous progress in technology brings much influence to DSS study. In 2002, the major journal of DSS research, Decision Support Systems, published a special issue, “DSS: directions for the next decade” edited by Carlsson and Turban (2002). In witness of “an unparalleled digital revolution”, the special issue studied the problems of those unimplemented goals of DSS and indicated directions for the next decade. Among those problems, “people problems”, which may refer to human’s limited capacity in cognition, subjective prejudice and world views, and belief in experts, are key reasons instead of technology-related problems. Those human problems may bring or increase uncertainties to decision making process. Even we suppose those uncertainties may change a structured problem into ill or unstructured problem, or a tame problem into a wicked problem.

It is not till present that people begin to pay attention to those human problems. Discussions on man–machine interaction, interactive modeling, etc. have already been undertaken with practice since 1980s (Fedra and Loucks, 1985; Loucks, 1992). The main feature of those discussions and practice is more emphasis on human roles in system modeling, as well as combination of human judgment (qualitative) and mathematical models (quantitative). Here we consider problem solving is equivalent to system modeling, as we regard the process of building a model of a system as the process to define a problem and find its solutions. Despite those “people problems”, human involvement or man–machine cooperative work is still among top foci for DSS researchers, which is also the focus of problem structuring process (Vidal, 2004).

In parallel to many western schools in approaches and methodologies for unstructured problem solving, eastern inquiry modes are studied and new system approaches have also been proposed based on comparisons between western and eastern system thoughts by oriental system scientists. Meta-synthesis approach (MSA) is one of those approaches proposed by a Chinese system scientist Xuesen Qian (HsueShen Tsien) to tackle with open complex giant system (OCGS) from the view of systems in the early 1990s (Qian et al., 1990; Qian, 2001). Here, we regard OCGS problems are ill-structured or wicked problems.

In this paper, we present our explorations in MSA and building a computerized embryonic prototype of MSA practicing platform. Firstly basic ideas of MSA and its practicing framework are reviewed. Relations between MSA and other oriental system approaches are discussed. Then a major project sponsored by National Natural Science Foundation of China (NSFC) for a demonstration of man–machine meta-synthetic support for macroeconomic decision making based on MSA is introduced. We present our study on some basic issues and methods for HWMSE implementation in that major project. Those main issues include model integration, opinion synthesis, macroeconomic modeling, etc.

Next meta-synthesis approach and its engineering practice framework are addressed.