We introduce a model for the adaptive evolution of a network of company ownerships. In a recent work it has been shown that the empirical global network of corporate control is marked by a central, tightly connected "core" made of a small number of large companies which control a significant part of the global economy. Here we show how a simple, adaptive "rich get richer" dynamics can account for this characteristic, which incorporates the increased buying power of more influential companies, and in turn results in even higher control. We conclude that this kind of centralized structure can emerge without it being an explicit goal of these companies, or as a result of a well-organized strategy.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3855616 | PMC |
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0080303 | PLOS |
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Toronto 3D Knowledge Synthesis and Clinical Trials Unit, Clinical Nutrition and Risk Factor Modification Center, St. Michael's Hospital, Unity Health Toronto, Toronto, ON M5B 1W8, Canada.
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Centre for Psychedelic Research, Division of Psychiatry, Department Brain Sciences, Imperial College London, United Kingdom.
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Front Neurorobot
December 2024
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan, China.
Existing image fusion methods primarily focus on complex network structure designs while neglecting the limitations of simple fusion strategies in complex scenarios. To address this issue, this study proposes a new method for infrared and visible image fusion based on a multimodal large language model. The method proposed in this paper fully considers the high demand for semantic information in enhancing image quality as well as the fusion strategies in complex scenes.
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