II. Pedia Sapiens: A Planetary Progeny Comes to Her/His Own Actual Factual Knowledge

B. The Spiral of Science: Manican to American to Earthicana Phases

Zammito, John. A Nice Derangement of Epistemes. Chicago: University of Chicago Press, 2004. Although written for the postmodern academy, the work offers a carefully reasoned exit from the corner and quandary that philosophy has worked itself into. Setting aside excuses of incommensurability, constructivism, linguistic positivism, and so on, the Rice University historian argues that real knowledge via the natural and especially human sciences is of course achievable and we should get on with this necessary project.

Zeng, An, et al. The Science of Science: From the Perspective of Complex Systems. Physics Reports. Online November, 2017. Beijing Normal University systems scientists are joined by a founder of this nonlinear revolution, H. Eugene Stanley, Boston University, to achieve this 77 page, 464 reference entry. In so doing, a most comprehensive study to date shows how, as everywhere else, natural network dynamics equally distinguish and guide worldwide research endeavors. Just an inkling in 2004, here it is robustly quantified and exemplified across many aspects from collaborative teams to citation rankings and knowledge creation. A further significance then accrues, which is the deep basis of this sourcebook. By an ability to apply universal network complexities to human cognitive advances, they well validate an emergent global brain, a biosphere unto a noosphere presently learning on her/his own. While efforts broach this nascent phase, a perception that an actual planetary prodigy is attaining her/his own revolutionary discovery still eludes. Contributions like this about an affinity with how a brain is made and thinks, and complex, self-organizing systems applied to science studies, give credence to a worldwise sapiensphere realm we desperately need.

The science of science (SOS) is a rapidly developing field which aims to understand, quantify and predict scientific research and the resulting outcomes. The problem is essentially related to almost all scientific disciplines and thus has attracted attention of scholars from different backgrounds. While different measurements have been designed to evaluate the scientific impact of scholars, journals and academic institutions, the multiplex structure, dynamics and evolution mechanisms of the whole system have been much less studied until recently. In this article, we review the recent advances in SOS, aiming to cover the topics from empirical study, network analysis, mechanistic models, ranking, prediction, and many important related issues. The results summarized in this review significantly deepen our understanding of the underlying mechanisms and statistical rules governing the science system. (Abstract)

Zenil, Hector, et al. The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence. arXiv:2307.07522. In this year when Earthuman acumen seems in ascent to a computational planetsphere, twenty two senior theorists based at the Alan Turing Institute including Alan Bundy, Carla Gomes and Hiroaki Hirano post a copious document with 125 references so to scope a careful transition across many fields of scientific researches. After a wide and deep survey, vital benefits such as global health wellness and mitigating climate change are cited.

Recent advances in machine learning and AI, including Generative AI and LLMs are disrupting technological innovation, product development, and society as a whole. Yet, AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access. Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery, including self-driven hypothesis generation and open-ended autonomous exploration. Such a vision would push the boundaries of new fundamental science rather than automatize current workflows and instead open doors for technological innovation to tackle some of the greatest challenges facing humanity today. (Excerpt)

Zha, Yilong, et al. Unfolding Large-Scale Online Collaborative Human Dynamics. Proceedings of the National Academy of Science. 113/14627, 2016. Chinese systems theorists further quantify how the instant multitude of worldwide communications and publications display the same natural complex network invariant topologies and fluidity.

This paper uncovered a universal double–power-law distribution of interupdate times for articles in Wikipedia and unfolded the seemingly complex collaborative patterns into three generic modules related to individual behavior, interaction among individuals, and population growth. The model is analytically solved and fully supported by the real data. As this model does not depend on any specific rules of Wikipedia, it is highly applicable for other online collaborative systems like software development and email communication. Similar scaling properties and models were reported for earthquake recurrence times, suggesting that interacting natural and social systems share universal collective mechanisms. (Significance)

Large-scale interacting human activities underlie all social and economic phenomena, but quantitative understanding of regular patterns and mechanism is very challenging and still rare. Self-organized online collaborative activities with a precise record of event timing provide unprecedented opportunity. Our empirical analysis of the history of millions of updates in Wikipedia shows a universal double–power-law distribution of time intervals between consecutive updates of an article. This unfolding allows us to obtain an analytical formula that is fully supported by the universal patterns in empirical data. Our modeling approaches reveal “simplicity” beyond complex interacting human activities. (Abstract)

Ziman, John. Real Science. Cambridge, UK: Cambridge University Press, 2000. A review of how the scientific process can be appreciated as an evolutionary self-organization. The proliferation of nested research specialities is seen to reflect a fractal geometry.

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