Abstract
Complexity analysis of dynamic systems provides a better understanding of the internal behaviours that are associated with tension and efficiency, which in the socio-technical systems may lead to innovation. One of the popular approaches for the assessment of complexity is associated with self-similarity. The dynamic component of dynamic systems represents the relationships and interactions among the inner elements (and its surroundings) and fully describes its behaviour. The approach used in this work addresses complexity analysis in terms of system behaviour, i.e., the so-called behavioural analysis of complexity. The self-similarity of a system (structural or behavioural) can be determined, for example, using fractal geometry, whose toolbox provides a number of methods for the measurement of the so-called fractal dimension. Other instruments for measuring the self-similarity in a system, include the Hurst exponent and the framework of complex system theory in general. The approach introduced in this work defines the complexity analysis in a social-technical system under tension. The proposed procedure consists of modelling the key dynamic components of a discrete event dynamic system by any definition of Petri nets. From the stationary probabilities, one can then decide whether the system is self-similar using the abovementioned tools. In addition, the proposed approach allows for finding the critical values (phase transitions) of the analysed systems.
Highlights
The complexity analysis of systems is currently a widespread theme
There are a number of complexity measures that have been defined in a number of areas such as physics, biology, sociology, and others
The more self-similar the dynamic process is, the greater is its potential for simplification, i.e., its complexity can be reduced
Summary
The complexity analysis of systems is currently a widespread theme. It reflects system features such as comprehensibility, modifiability, uncertainty, or maintainability. These features are especially important during design optimization (e.g., information systems). In the field of complex systems, exist the so-called phase transitions that change the structural and behavioural properties of the system, and its complexity. These changes represent, for example, the change of the information system, change of the top management, etc. One of the most widespread examples of complex systems is socio-technical systems that deal with human interaction with technical systems
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