%0 Journal Article
%J PeerJ
%D 2020
%T Ecosystem antifragility: beyond integrity and resilience
%A Equihua, Miguel
%A Espinosa Aldama, Mariana
%A Gershenson, Carlos
%A López-Corona, Oliver
%A Munguía, Mariana
%A Pérez-Maqueo, Octavio
%A Ramírez-Carrillo, Elvia
%K Antifragility
%K Complexity
%K Ecosystem integrity
%K Resilience
%X We review the concept of ecosystem resilience in its relation to ecosystem integrity from an information theory approach. We summarize the literature on the subject identifying three main narratives: ecosystem properties that enable them to be more resilient; ecosystem response to perturbations; and complexity. We also include original ideas with theoretical and quantitative developments with application examples. The main contribution is a new way to rethink resilience, that is mathematically formal and easy to evaluate heuristically in real-world applications: ecosystem antifragility. An ecosystem is antifragile if it benefits from environmental variability. Antifragility therefore goes beyond robustness or resilience because while resilient/robust systems are merely perturbation-resistant, antifragile structures not only withstand stress but also benefit from it.
%B PeerJ
%V 8
%P e8533
%G eng
%U https://doi.org/10.7717/peerj.8533
%R 10.7717/peerj.8533
%0 Journal Article
%J Physica A: Statistical Mechanics and its Applications
%D 2019
%T Rank-frequency distribution of natural languages: A difference of probabilities approach
%A Germinal Cocho
%A Rosalío F. Rodríguez
%A Sergio Sánchez
%A Jorge Flores
%A Carlos Pineda
%A Carlos Gershenson
%K Fokker–Planck equation
%K Languages
%K Master equation
%K Rank dynamics
%X In this paper we investigate the time variation of the rank k of words for six Indo-European languages using the Google Books N-gram Dataset. Based on numerical evidence, we regard k as a random variable whose dynamics may be described by a Fokker–Planck equation which we solve analytically. For low ranks the distinct languages behave differently, maybe due to the syntax rules, whereas for k>50 the law of large numbers predominates. We analyze the frequency distribution of words using the data and their adjustment in terms of time-dependent probability density distributions. We find small differences between the data and the fits due to conflicting dynamic mechanisms, but the data show a consistent behavior with our general approach. For the lower ranks the behavior of the data changes among languages presumably, again, due to distinct dynamic mechanisms. We discuss a possible origin of these differences and assess the novel features and limitations of our work.
%B Physica A: Statistical Mechanics and its Applications
%V 532
%P 121795
%G eng
%U https://doi.org/10.1016/j.physa.2019.121795
%R 10.1016/j.physa.2019.121795