Space-Time Intelligence System Software for the Analysis of Complex Systems

Discerning the dynamics of complex systems in a mathematically rigorous and physically consistent manner is as fascinating as intimidating of a challenge, stirring deeply and intrinsically with the most fundamental Physics, while at the same time percolating through the deepest meanders of quotidian life. The socio-natural coevolution in climate dynamics is an example of that, exhibiting a striking articulation between governing principles and free will, in a stochastic-dynamic resonance that goes way beyond a reductionist dichotomy between cosmos and chaos. Subjacent to the conceptual and operational interdisciplinarity of that challenge, lies the simple formal elegance of a lingua franca for communication with Nature. This emerges from the innermost mathematical core of the Physics of Coevolutionary Complex Systems, articulating the wealth of insights and flavours from frontier natural, social and technical sciences in a coherent, integrated manner. Communicating thus with Nature, we equip ourselves with formal tools to better appreciate and discern complexity, by deciphering a synergistic codex underlying its emergence and dynamics. Thereby opening new pathways to see the “invisible” and predict the “unpredictable” – including relative to emergent non-recurrent phenomena such as irreversible transformations and extreme geophysical events in a changing climate. Frontier advances will be shared pertaining a dynamic that translates not only the formal, aesthetical and functional beauty of the Physics of Coevolutionary Complex Systems, but also enables and capacitates the analysis, modelling and decision support in crucial matters for the environment and society. By taking our emerging Physics in an optic of operational empowerment, some of our pioneering advances will be addressed such as the intelligence system Earth System Dynamic Intelligence and the Meteoceanics QITES Constellation, at the interface between frontier non-linear dynamics and emerging quantum technologies, to take the pulse of our planet, including in the detection and early warning of extreme geophysical events from Space.

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Do Fractals Confirm the General Theory of Relativity?

Symmetry ◽

10.3390/sym11060740 ◽

2019 ◽

Vol 11 (6) ◽

pp. 740

Author(s):

Irina Rozgacheva

Keyword(s):

General Relativity ◽

Complex Systems ◽

Large Scale ◽

Geometric Theory ◽

Hubble Constant ◽

Space Time ◽

Scale Transformation ◽

Theory Of Relativity ◽

Complex Scalar ◽

Complex Scalar Field

The relatively high abundance of fractal properties of complex systems on Earth and in space is considered an argument in support of the general relativity of the geometric theory of gravity. The fractality may be called the fractal symmetry of physical interactions providing self-similarities of complex systems. Fractal symmetry is discrete. A class of geometric solutions of the general relativity equations for a complex scalar field is offered. This class allows analogy to spatial fractals in large-scale structures of the universe due to its invariance with respect to the discrete scale transformation of the interval d s ↔ q d s ˜ . The method of constructing such solutions is described. As an application, the treatment of spatial variations of the Hubble constant H 0 H S T (Riess et al., 2016) is considered. It is noted that the values H 0 H S T form an almost fractal set. It has been shown that: a) the variation H 0 H S T may be connected with the local gravitational perturbations of the space-time metrics in the vicinity of the galaxies containing Cepheids and supernovae selected for measurements; b) the value of the variation H 0 H S T can be a consequence of variations in the space-time metric on the outskirts of the local supercluster, and their self-similarity indicates the fractal distribution of matter in this region.

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Complexity by Design

Mechanical Engineering ◽

10.1115/1.2012-mar-1 ◽

2012 ◽

Vol 134 (03) ◽

pp. 29-33 ◽

Cited By ~ 1

Author(s):

Shannon Flumerfelt ◽

Gary Halada ◽

Franz-Josef Kahlen

Keyword(s):

Complex Systems ◽

Human Factor ◽

High Reliability ◽

Systems Design ◽

Embedded Sensors ◽

System Software ◽

High Reliability Organizations ◽

Feedback Systems ◽

Automated Control ◽

Design Engineers

This article discusses various engineering revolutions taking place to deal with challenges of complex systems’ design. Engineers who design complex systems have to understand how the various components of a system fit together and anticipate how the interactions between these components could lead to failure. The development of sophisticated expert system software that can provide rapid and intuitive access to vast amounts of data on materials and design features of available components also enables an individual engineer to tap into the expertise of many others. Adaptive risk management structures, such as those used in high-reliability organizations, which rely on expertise, planning, and communication, can help to reduce the uncertainty of human factor risk. Some automated control and feedback systems use embedded sensors and extremely rapid response mechanisms to prevent or limit damage from a failure far faster than a human operator could. The experts suggest that the rise of complex systems creates a challenge to traditional ways of engineering.

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Design and implementation of a Space-Time Intelligence System for disease surveillance

Journal of Geographical Systems ◽

10.1007/s10109-005-0147-6 ◽

2005 ◽

Vol 7 (1) ◽

pp. 7-23 ◽

Cited By ~ 40

Author(s):

Geoffrey M. Jacquez ◽

Dunrie A. Greiling ◽

Andrew M. Kaufmann

Keyword(s):

Disease Surveillance ◽

Space Time ◽

Intelligence System ◽

Design And Implementation

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An Agent-Based Approach to Environmental and Urban Systems within Geographic Information Systems

Integrating Geographic Information Systems and Agent-Based Modeling Techniques for Understanding Social and Ecological Processes ◽

10.1093/oso/9780195143362.003.0014 ◽

2002 ◽

Author(s):

Bin Jiang ◽

H. Randy Gimblett

Keyword(s):

Information Systems ◽

Geographic Information Systems ◽

Complex Systems ◽

Geographic Information ◽

Space Time ◽

Urban Systems ◽

Agent Based ◽

Time Dynamics ◽

Long Time ◽

Terrain Representation

Both environment and urban systems are complex systems that are intrinsically spatially and temporally organized. Geographic information systems (GIS) provide a platform to deal with such complex systems, both from modeling and visualization points of view. For a long time, cell-based GIS has been widely used for modeling urban and environment system from various perspectives such as digital terrain representation, overlay, distance mapping, etc. Recently temporal GIS (TGIS) has been challenged to model dynamic aspects of urban and environment system (e.g., Langran, Clifford and Tuzhilin, Egenhofer and Golledge), in pursuit of better understanding and perception of both spatial and temporal aspects of these systems. In regional and urban sciences, cellular automata (CA) provide useful methods and tools for studying how regional and urban systems evolve. Because of its conceptual resemblance to cell-based GIS, CA have been extensively used to integrate GIS as potentially useful qualitative forecasting models. This approach intends to look at urban and environment systems as self-organized processes; i.e., how coherent global patterns emerge from local interaction. Thus this approach differentiates it from TGIS in that there is no database support for space-time dynamics. An agent-based approach was initially developed from distributed artificial intelligence (DAI). The basic idea of agent-based approaches is that programs exhibit behaviors entirely described by their internal mechanisms. By linking an individual to a program, it is possible to simulate an artificial world inhabited by interacting processes. Thus it is possible to implement simulation by transposing the population of a real system to its artificial counterpart. Each member of population is represented as an agent who has built-in behaviors. Agent-based approaches provide a platform for modeling situations in which there are large numbers of individuals that can create complex behaviors. It is likely to be of particular interest for modeling space-time dynamics in environmental and urban systems, because it allows researchers to explore relationships between microlevel individual actions and the emergent macrolevel phenomena. An agent-based approach has great potential for modeling environmental and urban systems within GIS. Previous work has focused on modeling people environment interaction, virtual ecosystems, and integration of agent based approach and GIS.

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Improving Competitive Intelligence Through System Synamics

Information and Communications Technology for Competitive Intelligence ◽

10.4018/978-1-59140-142-1.ch006 ◽

2011 ◽

pp. 129-157

Author(s):

Özge Pala ◽

Dirk Vriens ◽

Jac A.M. Vennix

Keyword(s):

Complex Systems ◽

System Dynamics ◽

Competitive Intelligence ◽

Accurate Information ◽

Environmental Cues ◽

Simulation Methodology ◽

Intelligence System ◽

Making Sense ◽

Competitive Intelligence System

To survive in a complex and dynamic world, organizations need relevant, timely, and accurate information about their environment. Due to the increasing complexity and dynamics of the environment, organizations run into several difficulties in their efforts to structure the intelligence activities. Two particularly persistent problems are (1) determining the relevant environmental cues and (2) making sense of the particular values of these cues. The current available methods for competitive intelligence do not eliminate these problems. In this chapter, system dynamics (SD) is proposed as an appropriate tool for competitive intelligence. System dynamics is a simulation methodology that deals with the dynamics of complex systems from a feedback perspective. How SD can help in dealing with the problems in direction (selecting the relevant environmental cues) and analysis (making sense of cues) stages and how ICT can support the use of SD in intelligence activities are discussed.

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