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Knowledge, Volume 5, Issue 1 (March 2025) – 3 articles

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19 pages, 1053 KiB  
Article
Epistemology in the Age of Large Language Models
by Jennifer Mugleston, Vuong Hung Truong, Cindy Kuang, Lungile Sibiya and Jihwan Myung
Knowledge 2025, 5(1), 3; https://doi.org/10.3390/knowledge5010003 - 1 Feb 2025
Viewed by 390
Abstract
Epistemology and technology have been working in synergy throughout history. This relationship has culminated in large language models (LLMs). LLMs are rapidly becoming integral parts of our daily lives through smartphones and personal computers, and we are coming to accept the functionality of [...] Read more.
Epistemology and technology have been working in synergy throughout history. This relationship has culminated in large language models (LLMs). LLMs are rapidly becoming integral parts of our daily lives through smartphones and personal computers, and we are coming to accept the functionality of LLMs as a given. As LLMs become more entrenched in societal functioning, questions have begun to emerge: Are LLMs capable of real understanding? What is knowledge in LLMs? Can knowledge exist independently of a conscious observer? While these questions cannot be answered definitively, we can argue that modern LLMs are more than mere symbol-manipulators and that LLMs in deep neural networks should be considered capable of a form of knowledge, though it may not qualify as justified true belief (JTB) in the traditional definition. This deep neural network design may have endowed LLMs with the capacity for internal representations, basic reasoning, and the performance of seemingly cognitive tasks, possible only through a compressive but generative form of representation that can be best termed as knowledge. In addition, the non-symbolic nature of LLMs renders them incompatible with the criticism posed by Searle’s “Chinese room” argument. These insights encourage us to revisit fundamental questions of epistemology in the age of LLMs, which we believe can advance the field. Full article
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21 pages, 831 KiB  
Article
A DEMATEL Based Approach for Evaluating Critical Success Factors for Knowledge Management Implementation: Evidence from the Tourism Accommodation Sector
by Natalia Chatzifoti, Panos T. Chountalas, Konstantina K. Agoraki and Dimitrios A. Georgakellos
Knowledge 2025, 5(1), 2; https://doi.org/10.3390/knowledge5010002 - 22 Jan 2025
Viewed by 660
Abstract
The significance of knowledge management in the tourism accommodation sector is increasingly vital due to rapid market changes and intense competition. Although the value of identifying and implementing critical success factors (CSFs) for knowledge management is widely recognized in the sector, there is [...] Read more.
The significance of knowledge management in the tourism accommodation sector is increasingly vital due to rapid market changes and intense competition. Although the value of identifying and implementing critical success factors (CSFs) for knowledge management is widely recognized in the sector, there is still a lack of comprehensive understanding and practical application of these factors. This study employs the decision-making trial and evaluation laboratory (DEMATEL) methodology to systematically identify and analyze the interrelationships among these CSFs. The findings reveal a complex web of dependencies within this network. Specifically, leadership commitment and support is identified as the most influential CSF, acting as a fundamental element that enables the successful adoption and integration of knowledge management initiatives. Additionally, strategic alignment and a supportive organizational culture are crucial, working synergistically to ensure that knowledge management initiatives are aligned with overarching organizational goals and create an environment that encourages change and collaboration. Furthermore, the study highlights a mutually reinforcing relationship between knowledge processes, governance, and employee training. This relationship suggests that strong governance structures and clearly defined knowledge processes facilitate and improve the effectiveness of employee training programs while also creating a continuous improvement cycle where improved training further refines governance and knowledge processes. Moreover, the study highlights the integration of the ISO 30401:2018 standard as a systematic framework to support these CSFs, providing a structured approach to improve knowledge management systems. By mapping the cause-and-effect relationships among the identified CSFs, this research offers practical insights for industry professionals to effectively prioritize and address these factors. Full article
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16 pages, 6537 KiB  
Article
A Deterministic Model for Harmful Algal Bloom (HAB) Patterns Under Turing’s Instability Perspective
by Tri Nguyen-Quang, Louis Labat and Qurat Ul An Sabir
Knowledge 2025, 5(1), 1; https://doi.org/10.3390/knowledge5010001 - 22 Jan 2025
Viewed by 679
Abstract
Turing’s instability has been widely introduced to explain the formation of several biological and ecological patterns, such as the skin patterning of fish or animals, wings of butterflies, pigmentation, and labyrinth patterns of the cerebral cortex of mammals. Such a mechanism may occur [...] Read more.
Turing’s instability has been widely introduced to explain the formation of several biological and ecological patterns, such as the skin patterning of fish or animals, wings of butterflies, pigmentation, and labyrinth patterns of the cerebral cortex of mammals. Such a mechanism may occur in the ecosystem due to the differential diffusion dispersal that happen if one of the constituent species results in the activator or the prey, showing a tendency to undergo autocatalytic growth. The diffusion of the constituent species activator is a random mobility function called passive diffusion. If the other species in the system (the predator/inhibitor) disperses sufficiently faster than the activator, then the spatially uniform distribution of species becomes unstable, and the system will settle into a stationary state. This paper introduced Turing’s mechanism in our reaction–taxis–diffusion model to simulate the harmful algal bloom (HAB) pattern. A numerical approach, the Runge–Kutta method, was used to deal with this system of reaction–taxis–diffusion equations, and the findings were qualitatively compared to the aerial patterns obtained by a drone flying over Torment Lake in Nova Scotia (Canada) during the bloom season of September 2023. Full article
(This article belongs to the Special Issue New Trends in Knowledge Creation and Retention)
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