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Statistical Laws in Complex Systems

Statistical laws describe regular patterns observed in diverse scientific domains, ranging from the magnitude of earthquakes (Gutenberg-Richter law) and metabolic rates in organisms (Kleiber's law), to the frequency distribution of words in texts (Zipf's and Herdan-Heaps' laws), and productivity metrics of cities (urban scaling laws). The origins of these laws, their empirical validity, and the insights they provide into underlying systems have been subjects of scientific inquiry for centuries. This monograph provides an unifying approach to the study of statistical laws, critically evaluating their role in the theoretical understanding of complex systems and the different data-analysis methods used to evaluate them. Through a historical review and a unified analysis, we uncover that the persistent controversies on the validity of statistical laws are predominantly rooted not in novel empirical findings but in the discordance among data-analysis techniques, mechanistic models, and the interpretations of statistical laws. Starting with simple examples and progressing to more advanced time-series and statistical methods, this monograph and its accompanying repository provide comprehensive material for researchers interested in analyzing data, testing and comparing different laws, and interpreting results in both existing and new datasets.

https://arxiv.org/abs/2407.19874



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Statistical Laws in Complex Systems

Statistical laws describe regular patterns observed in diverse scientific domains, ranging from the magnitude of earthquakes (Gutenberg-Richter law) and metabolic rates in organisms (Kleiber's law), to the frequency distribution of words in texts (Zipf's and Herdan-Heaps' laws), and productivity metrics of cities (urban scaling laws). The origins of these laws, their empirical validity, and the insights they provide into underlying systems have been subjects of scientific inquiry for centuries. This monograph provides an unifying approach to the study of statistical laws, critically evaluating their role in the theoretical understanding of complex systems and the different data-analysis methods used to evaluate them. Through a historical review and a unified analysis, we uncover that the persistent controversies on the validity of statistical laws are predominantly rooted not in novel empirical findings but in the discordance among data-analysis techniques, mechanistic models, and the interpretations of statistical laws. Starting with simple examples and progressing to more advanced time-series and statistical methods, this monograph and its accompanying repository provide comprehensive material for researchers interested in analyzing data, testing and comparing different laws, and interpreting results in both existing and new datasets.

https://arxiv.org/abs/2407.19874

BY Complex Systems Studies




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Telegram, which does little policing of its content, has also became a hub for Russian propaganda and misinformation. Many pro-Kremlin channels have become popular, alongside accounts of journalists and other independent observers. "And that set off kind of a battle royale for control of the platform that Durov eventually lost," said Nathalie Maréchal of the Washington advocacy group Ranking Digital Rights. In February 2014, the Ukrainian people ousted pro-Russian president Viktor Yanukovych, prompting Russia to invade and annex the Crimean peninsula. By the start of April, Pavel Durov had given his notice, with TechCrunch saying at the time that the CEO had resisted pressure to suppress pages criticizing the Russian government. On December 23rd, 2020, Pavel Durov posted to his channel that the company would need to start generating revenue. In early 2021, he added that any advertising on the platform would not use user data for targeting, and that it would be focused on “large one-to-many channels.” He pledged that ads would be “non-intrusive” and that most users would simply not notice any change. He adds: "Telegram has become my primary news source."
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