Research: Antifragility = Elasticity + Resilience + Machine Learning

Research: “Antifragility = Elasticity + Resilience + Machine Learning”

“Antifragility = Elasticity + Resilience + Machine Learning. Models and Algorithms for Open System Fidelity”. In Proc. of the 1st International Workshop “From Dependable to Resilient, from Resilient to Antifragile Ambients and Systems” (ANTIFRAGILE 2014), Hasselt, Belgium, 2-5 June, 2014. Elsevier Science, Procedia Computer Science.
Vincenzo De Florio – PATS research group, University of Antwerp & iMinds Research Institute, Middelheimlaan 1, 2020 Antwerpen, Belgium
We introduce a model of the fidelity of open systems—fidelity being interpreted here as the compliance between corresponding figures of interest in two separate but communicating domains. A special case of fidelity is given by real-timeliness and synchrony, in which the figure of interest is the physical and the system’s notion of time. Our model covers two orthogonal aspects of fidelity, the first one focusing on a system’s steady state and the second one capturing that system’s dynamic and behavioural characteristics. We discuss how the two aspects correspond respectively to elasticity and resilience and we highlight each aspect’s qualities and limitations. Finally we sketch the elements of a new model coupling both of the first model’s aspects and complementing them with machine learning. Finally, a conjecture is put forward that the new model may represent a first step towards compositional criteria for antifragile systems (Vincenzo De Florio)

Antifragility = Elasticity + Resilience + Machine Learning. Models and Algorithms for Open System Fidelity

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