Stocks and Cryptocurrencies: Anti-fragile or Robust?

Antifragility was recently defined as a property of complex systems that benefit from disorder. However, its original formal definition is difficult to apply. Our approach has been to define and test a much simpler measure of antifragility for complex systems. In this work we use our antifragility measure to analyze real data from the stock market and cryptocurrency prices. Results vary between different antifragility interpretations and for each system. Our results suggest that the stock market favors robustness rather than antifragility, as in most cases the highest and lowest antifragility values are reached either by young agents or constant ones. There are no clear correlations between antifragility and different good-performance measures, while the best performers seem to fall within a robust threshold. In the case of cryptocurrencies, there is an apparent correlation between high price and high antifragility.Comment: 11 pages, 5 figure

Is Soccer a lie or simply a complex system?

Understanding soccer as a complex system we base on nature and the collective behavior of many organisms that "do calculations," seeking to generate solutions in a bioinspired way. When soccer mysteries appear, complex systems science emerges as a means to provide explanations. However, given the variety of interpretations that complexity and its associated properties can have and the understanding of what a complex system is, it is convenient to provide some elements to understand how unpredictability in soccer gives way to hundreds of counterintuitive results and how the science of complexity could contribute to the understanding of many phenomena in this sport. In this context, the manuscript's objective is to synthetically address some of the most important aspects of applied complexity to soccer to bring science and sport closer togetherComment: 15 pages, in Spanish language, 6 Figure

Antifragility Predicts the Robustness and Evolvability of Biological Networks through Multi-class Classification with a Convolutional Neural Network

Robustness and evolvability are essential properties to the evolution of biological networks. To determine if a biological network is robust and/or evolvable, it is required to compare its functions before and after mutations. However, this sometimes takes a high computational cost as the network size grows. Here we develop a predictive method to estimate the robustness and evolvability of biological networks without an explicit comparison of functions. We measure antifragility in Boolean network models of biological systems and use this as the predictor. Antifragility occurs when a system benefits from external perturbations. By means of the differences of antifragility between the original and mutated biological networks, we train a convolutional neural network (CNN) and test it to classify the properties of robustness and evolvability. We found that our CNN model successfully classified the properties. Thus, we conclude that our antifragility measure can be used as a predictor of the robustness and evolvability of biological networks.Comment: 22 pages, 10 figure