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Quantifying the unexpected: A scientific approach to Black Swans

Complexity Digest - Sun, 08/21/2022 - 11:57

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

Prevalence and scalable control of localized networks

Complexity Digest - Sat, 08/20/2022 - 11:50

Chao Duan, Takashi Nishikawa, and Adilson E. Motter

PNAS 119 (32) e2122566119

The control of large-scale networks is a pressing problem of relevance to numerous natural and engineered systems. Despite recent advances in network and control science, there has been a lack of fundamental understanding about the network properties that can enable effective and efficient control of such systems. Here, we demonstrate that network locality, which we show to be a rather common property, can dramatically improve our ability to control large-scale networks. In particular, we demonstrate that locality can be exploited to substantially simplify the task of controlling nonlinear networks for desirable dynamical performance while minimizing the control effort. Our theory and algorithms provide a unified framework and show that local computation and communication suffice to achieve near-optimal control outcomes.

Read the full article at: www.pnas.org

Hidden Chaos Found to Lurk in Ecosystems

Complexity Digest - Fri, 08/19/2022 - 13:59

New research finds that chaos plays a bigger role in population dynamics than decades of ecological data seemed to suggest.

Read the full article at: www.quantamagazine.org

GATTACA AS A LENS ON CONTEMPORARY GENETICS Marking 25 years into the film’s “not-too-distant” future

Complexity Digest - Fri, 08/19/2022 - 11:52

C. Brandon Ogbunu, Michael D. Edge

The 1997 film Gattaca has emerged as a canonical pop culture reference used to discuss modern controversies in genetics and bioethics. It appeared in theaters a few years prior to the announcement of the “completion” of the human genome (2000), as the science of human genetics was developing a renewed sense of its social implications. The story is set in a near-future world in which parents can, with technological assistance, influence the genetic composition of their offspring on the basis of predicted life outcomes. This moment—25 years after the film’s release—offers an opportunity to reflect on where so- ciety currently stands with respect to the ideas explored in Gattaca. Here, we review and discuss several active areas of genetic research—genetic prediction, embryo selection, forensic genetics, and others–that interface directly with scenes and concepts in the film. On its silver anniversary, we argue that Gattaca remains an important reflection of society’s expectations and fears with respect to the ways that genetic science has manifested in the real world. In an accompanying appendix, we offer some thought questions to guide group discussions inside and outside of the classroom.

Read the full article at: osf.io

Causal Machine Learning: A Survey and Open Problems

Complexity Digest - Thu, 08/18/2022 - 13:55

Jean Kaddour, Aengus Lynch, Qi Liu, Matt J. Kusner, Ricardo Silva
Causal Machine Learning (CausalML) is an umbrella term for machine learning methods that formalize the data-generation process as a structural causal model (SCM). This perspective enables us to reason about the effects of changes to this process (interventions) and what would have happened in hindsight (counterfactuals). We categorize work in CausalML into five groups according to the problems they address: (1) causal supervised learning, (2) causal generative modeling, (3) causal explanations, (4) causal fairness, and (5) causal reinforcement learning. We systematically compare the methods in each category and point out open problems. Further, we review data-modality-specific applications in computer vision, natural language processing, and graph representation learning. Finally, we provide an overview of causal benchmarks and a critical discussion of the state of this nascent field, including recommendations for future work.

Read the full article at: arxiv.org

Worldwide scaling of waste generation in urban systems

Complexity Digest - Thu, 08/18/2022 - 11:49

Mingzhen Lu, Chuanbin Zhou, Chenghao Wang, Robert B. Jackson, Christopher P. Kempes
The production of waste as a consequence of human activities is one of the most fundamental challenges facing our society and global ecological systems. Waste generation is rapidly increasing, with corresponding shifts in the structure of our societies where almost all nations are moving from rural agrarian societies to urban and technological ones. However, the connections between these radical societal shifts and waste generation have not yet been described. Here we apply scaling theory to establish a new understanding of waste in urban systems. We identify universal scaling laws of waste generation across diverse urban systems worldwide for three forms of waste: wastewater, municipal solid waste, and greenhouse gasses. We show that wastewater generation scales superlinearly, municipal solid waste scales linearly, and greenhouse gasses scales sublinearly with city size. In specific cases production can be understood in terms of city size coupled with financial and natural resources. For example, wastewater generation can be understood in terms of the increased economic activity of larger cities, and the deviations around the scaling relationship – indicating relative efficiency – depend on GDP per person and local rainfall. We also show how the temporal evolution of these scaling relationships reveals a loss of economies of scale and the general increase in waste production, where sublinear scaling relationships become linear. Our findings suggest general mechanisms controlling waste generation across diverse cities and global urban systems. Our approach offers a systematic approach to uncover these underlying mechanisms that might be key to reducing waste and pursing a more sustainable future.

Read the full article at: arxiv.org

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