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Sina Khajehabdollahi, Jan Prosi, Emmanouil Giannakakis, Georg Martius, Anna Levina

Artificial Life (In Press)

It has long been hypothesized that operating close to the critical state is beneficial for natural and artificial evolutionary systems. We put this hypothesis to test in a system of evolving foraging agents controlled by neural networks that can adapt the agents’ dynamical regime throughout evolution. Surprisingly, we find that all populations that discover solutions evolve to be subcritical. By a resilience analysis, we find that there are still benefits of starting the evolution in the critical regime. Namely, initially critical agents maintain their fitness level under environmental changes (for example, in the lifespan) and degrade gracefully when their genome is perturbed. At the same time, initially subcritical agents, even when evolved to the same fitness, are often inadequate to withstand the changes in the lifespan and degrade catastrophically with genetic perturbations. Furthermore, we find the optimal distance to criticality depends on the task complexity. To test it we introduce a hard task and a simple task: For the hard task, agents evolve closer to criticality, whereas more subcritical solutions are found for the simple task. We verify that our results are independent of the selected evolutionary mechanisms by testing them on two principally different approaches: a genetic algorithm and an evolutionary strategy. In summary, our study suggests that although optimal behaviour in the simple task is obtained in a subcritical regime, initializing near criticality is important to be efficient at finding optimal solutions for new tasks of unknown complexity.

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Arunima Malik, Mengyu Li, Manfred Lenzen, Jacob Fry, Navoda Liyanapathirana, Kathleen Beyer, Sinead Boylan, Amanda Lee, David Raubenheimer, Arne Geschke & Mikhail Prokopenko
Nature Food volume 3, pages 631–643 (2022)

Disasters resulting from climate change and extreme weather events adversely impact crop and livestock production. While the direct impacts of these events on productivity are generally well known, the indirect supply-chain repercussions (spillovers) are still unclear. Here, applying an integrated modelling framework that considers economic and physical factors, we estimate spillovers in terms of social impacts (for example, loss of job and income) and health impacts (for example, nutrient availability and diet quality) resulting from disruptions in food supply chains, which cascade across regions and sectors. Our results demonstrate that post-disaster impacts are wide-ranging and diverse owing to the interconnected nature of supply chains. We find that fruit, vegetable and livestock sectors are the most affected, with effects flowing on to other non-food production sectors such as transport services. The ability to cope with disasters is determined by socio-demographic characteristics, with communities in rural areas being most affected.

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Herrera-Diestra JL, Tildesley M, Shea K, Ferrari MJ (2022) Cattle transport network predicts endemic and epidemic foot-and-mouth disease risk on farms in Turkey. PLoS Comput Biol 18(8): e1010354.

Contact network epidemiology has been extensively used in the context of infectious diseases, primarily focusing on epidemic diseases. In this paper we use detailed recorded data about cattle exchange between farms in Turkey from 2007 to 2012, to build, analyze and characterize the directed-weighted complex network of shipments of cattle. Additionally, using outbreaks data about recorded cases of foot-and-mouth disease (FMD) in Turkey, we assess the correlation between the “farm’s” position in the network (importance) and the risk of being infected with FMD, which has been endemic in Turkey for a long time. We find some network measures that are more likely to identify high-risk and low-risk farms (in-degree and in-coreness, respectively) when proposing strategies for surveillance or containment of an infectious disease.

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Giordano De Marzo, Andrea Gabrielli, Andrea Zaccaria, and Luciano Pietronero
Phys. Rev. Research 4, 033079 – Published 27 July 2022

Many natural and socioeconomic systems are characterized by power-law distributions that make the occurrence of extreme events not negligible. Such events are sometimes referred to as Black Swans, but a quantitative definition of a Black Swan is still lacking. Here, by leveraging on the properties of Zipf-Mandelbrot law, we investigate the relations between such extreme events and the dynamics of the upper cutoff of the inherent distribution. This approach permits a quantification of extreme events and allows to classify them as White, Gray, or Black Swans. Our criterion is in accordance with some previous findings but also allows us to spot new examples of Black Swans, such as Lionel Messi and the Turkish Airlines Flight 981 disaster. The systematic and quantitative methodology we developed allows a scientific and immediate categorization of rare events, also providing insight into the generative mechanism behind Black Swans.

Read the full article at: link.aps.org