The lecture by Tuan Pham, Leonhard Horstmeyer and Ruggiero Lo Sardo from Medical University of Vienna and the Complexity Science Hub Vienna will take place at the Complexity Science Hub Vienna in Room 201.
If you are interested in participating, please email to email@example.com
Title: “Quantization: a novel Precursor for collapses of ecosystems”
The collapse of ecosystems, the extinction of species or the breakdown of company networks, banking networks and political systems usually hinges on topological properties of the underlying interaction network, such as the existence of keystone nodes.
Knowing just the macroscopic non-structural information, such as species abundances, company revenues etc, it seems impossible to make predictions about impending collapses of these systems. We show, however, that such macroscopic predictions are possible in adaptive networks with catalytic interactions. The appearance of the most vulnerable autocatalytic structure, a single catalytic cycle, can be sensed macroscopically by a phenomenon that we dub quantization. This allows us to accurately estimate the expected time until collapse.
Title: “Novel Insights into the Precursor Theory of Adaptive Networks”
Anticipating critical transitions in adaptive networks, in which node states and the network itself co-evolve, is crucial to navigating through climate crises, ecological crises, epidemic outbreaks, financial crises etc. Out of the class of adaptive network models, we consider the adaptive SIS model introduced by Gross et. al. and investigate the critical behavior of the network topology near the persistence threshold by looking at the densities of motives, the clustering, the compactness, the degree distribution and assortativity, the effective branching ratio and spectral distribution.
We observe a crossover of two critical scaling laws that lead to a wide range of resulting critical curves, some of which exhibit local maxima long before the phase transition. Classical early-warning signs that rely on estimating the critical point by fitting power laws completely mispredict the distance to the threshold. We propose to use the maxima of certain network quantities as a robust early warning signs instead.