Henk Koppelaar

Chaos Theory and Social Science: A Methodological Analysis

This article investigates the relevance of chaos theory for social science. The application of chaos models in the analysis of social phenomena is accompanied by some important scientific problems. First, whether observations of social phenomena are generated by nonlinear dynamics cannot be ascertained beyond considerable doubt, especially when these observations contain measurement errors; i.e., there is a problem of external validity. Secondly, and more important, as a theory of irregular cyclical social behaviour is lacking, inductive-statistical theoryformation about such behaviour, which is based on fitting a mathematical model of chaos to observations of social phenomena, is impossible unless additional information is used concerning the context and circumstances wherein the social phenomena occur; i.e., the internal validity of any theoretical explanation that is derived from only a fitted mathematical model (of chaos) cannot be assessed. So, research into the suggestion derived from mathematical chaos theory that irregular cycles may be present in the development of social phenomena over time requires theory-formation about irregular cyclical social behaviour on the basis of established theoretical insights and empirical evidence instead of fitting sophisticated mathematical models of chaos to observations of social phenomena.

MDE and customization of modeling and simulation web applications

Abstract In this paper, we present a model-driven approach to construction of web-based collaborative environments that could be efficiently tailored to modeling and simulation needs of an arbitrary number of M&S application domains. To achieve broad applicability, our approach is based on general concepts and taxonomies in fields of Modeling and Simulation, Distributed Systems, and Collaborative Software. Such stable concepts constitute the collaborative Modeling and online Simulation (cMoS) framework. cMoS provides a general basis for a family of Web-Based M&S applications. Specific M&S applications are supported through customization of the variation points in cMoS. To enable efficient tailoring to specific applications during the operation phase, the variation points are not implicitly hardcoded as traditionally, but are explicitly expressed as models. The use of the resulting models is still limited due to a high barrier of their implementation. This barrier is sought to be amended by model-driven engineering (MDE): models of the variation points are computerized and their implementation is automatically generated. The end result is a model-driven and mostly programming free cMoS system adaptable to new M&S applications through abstract modeling of the variation points. The proposed framework and model-driven construction of a cMoS system are demonstrated. The system customization has been verified for a number of domains: Data Flow Diagrams (DFD), Discrete Event System Specification (DEVS), Process Interaction (PI) and Dynamic Traffic Routing (DTR). A demonstration of the latter is included in this paper. Generic cMoS functionality, such as modeling, collaborative sharing of conceptual models, online simulation and management of shared simulation resources is demonstrated as well.

Data reconciliation of immersive heart inspection

IVUS images are complicated medical datasets suffering from some artifacts caused by the data acquisition method of immersive heart inspection. Data reconciliation, which removes tracing and tracking uncertainties of these datasets, is an important step for the medical application of remodeling the arteries in virtual reality to aid diagnosing and treating heart diseases. This paper provides an empirical data reconciliation method, which fuses the features of the coronary longitudinal movement with motion compensation model. It explains the distortion of the data set well and provides a method to analyze and reconcile the dataset

Architectural knowledge representation using the Galois lattice technique

Representation of spatial information is very important in architecture. Information of a design may be organized as the objects and relations between them. Besides the limitation of those techniques, essentially they are problem specific, the main inefficiency of those methods is that the hierarchical nature of architectural information cannot be represented by them. To combat the inefficiency they have to handle information in a hierarchical manner. In such a representation system not only we can decompose information into smaller pieces also we can focus on the required parts of the problem without involving with many detailed unnecessary information. Also the implicit information in design specifications that is vital must be extracted. Without such information the generated solutions will not be so realistic. The extracted information is essential for hierarchical abstraction of the given design problem. A technique namely the Galois lattice, as a mathematical model, has been used to construct a hierarchical representation scheme using the design specification and to externalize the hidden information in that specification.

Application of a neural network to a design optimization process

This article discusses the implementation of a class of design problems in a neural network. the problems, identified at an abstract (i.e., connectivity) level of building design, are characterized as optimization types of problems. Architects often have to generate floor‐plan layouts of a building optimized with respect to several points of view. Examples of these points of view, often stated in terms of normative requirements, are the social norms: community, privacy, and circulation‐cost. an architectural design problem in the presence of even a single norm from among the above norms is computationally hard and intractable. During the last four decades there have been attempts to automate floor‐plan design considering a single norm or a limited number norms. Most of these attempts have traditionally been based on combinatorial enumeration methods. Recent progress in AI has paved the way for intelligent handling of the architectural design processes using knowledge‐based system technology and heuristics programming. This article examines the possibility of the neural networks approach in generating connectivity patterns of building with respect to specific social norms. Our first attempt has been focused on linear‐tree type designs with respect to single norms. the idea in this work was borrowed from the Hopfield model of the neural network for implementation of the Traveling Salesman Problem, because of the similarity of our design problems with this problem. Hopfield and other researchers used constant parameters for different‐sized problems. Close examination of the network and experiments revealed that this approach does not guarantee a convergence for every case, and chaotic behavior is expected in cases for which the chosen parameters are not appropriate. to overcome this problem this article suggests problem‐dependent and problem‐size‐dependent parameters that vary for each case. Test results from the implementation convey that the approach yields satisfactory results and is worth it to explore its application to other classes of optimization problems.