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2017
DOI: 10.3390/e19110572
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A Behavioural Analysis of Complexity in Socio-Technical Systems under Tension Modelled by Petri Nets

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 address… Show more

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Cited by 3 publications
(2 citation statements)
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References 35 publications
(34 reference statements)
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“…We have therefore admitted quantitative methods and provided some supplementary reflections. Sneak circuit analysis and Markov analysis are engineering methods, although there are some rare occasions where Markov analysis has been used to predict human-machine interaction or socio-technical-related problems [44]. Their use in socio-technical systems and application in workplace safety has been, however, limited to simple systems, and their predictive utility remains limited to micro-level ergonomics [45].…”
Section: Reflections On Quantitative Methods and Techniquesmentioning
confidence: 99%
“…We have therefore admitted quantitative methods and provided some supplementary reflections. Sneak circuit analysis and Markov analysis are engineering methods, although there are some rare occasions where Markov analysis has been used to predict human-machine interaction or socio-technical-related problems [44]. Their use in socio-technical systems and application in workplace safety has been, however, limited to simple systems, and their predictive utility remains limited to micro-level ergonomics [45].…”
Section: Reflections On Quantitative Methods and Techniquesmentioning
confidence: 99%
“…The proposed minimax entropy model is used to estimate the parameters with the preference learning. Ibl and Capek [ 19 , 20 ] used level of uncertainty (entropy) as an indicator for determining the degree of predictability of modelled systems. The authors focused on measuring the uncertainty of a process model that was modelled using stochastic Petri nets.…”
Section: Related Workmentioning
confidence: 99%