What is it about?
The chapter combines emerging research on the digital twin, particularly on the natural language processing (NLP) technologies of AI and self-leadership. It applies the theories to one of the fastest-growing challenges of late postmodern humans: addiction. The chapter addresses the question of how language-based digital twins (LBDT) affect the self-leadership process of addiction redundancy. After reviewing the contemporary research literature, the chapter presents a case in which LBDT engages in understanding an appropriate approach to the self-leadership phenomenon and strategy.
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Why is it important?
The case suggests that the addictive worldview and behavior is something more general for a late post-human society than a distinct health care problem. Self-leadership as a concept shifts the study of addiction toward strategic action.
Perspectives
Self-leadership research has yet to adopt an LBDT perspective in general and on phenomena such as addiction. Furthermore, the self-leadership methodology addresses a person's view on the level of strategies but does not combine them against various self-leadership challenges. The case highlights how LBDT can be associated with a user-designed, multiple-level, self-leadership approach in the context of addictive worldviews and behaviors. The limitations related to LBDT transparency, extension, and epistemology are considered.
Ville Pietiläinen
University of Lapland
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This page is a summary of: Emerging technologies in digital twin, June 2025, Taylor & Francis,
DOI: 10.1201/9781003498117-8.
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