The Ethics of Instability: Why Embracing Flux Solves Modern Crises
This thesis presents a novel ethical framework called the "Ethics of Instability" that challenges traditional stability-based approaches to moral philosophy and practical ethics. Drawing from process philosophy, complexity science, and empirical research, this framework argues that embracing instability as a generative force offers superior solutions to contemporary global challenges including climate adaptation, artificial intelligence alignment, and social justice. The Ethics of Instability is grounded in five core principles: Generative Flux, Dynamic Relationality, Productive Uncertainty, Intersectional Fluidity, and Transformative Adaptation. These principles provide both theoretical foundations and practical methodologies for addressing complex challenges that traditional approaches have failed to resolve effectively. Through comprehensive case studies of climate adaptation projects, AI alignment research, and social justice movements, this thesis demonstrates empirically that approaches aligned with the Ethics of Instability achieve superior outcomes compared to traditional stability-based approaches. The thesis also provides critical comparisons with major rival theories, establishing the distinctive contributions and theoretical superiority of the Ethics of Instability framework. The practical implications of this framework extend to policy-making, institutional design, and social movements, offering concrete guidance for transformative approaches to contemporary challenges. This work positions the Ethics of Instability as a paradigm shift in ethical thinking that is both theoretically sophisticated and practically relevant to the most pressing issues of the twenty-first century.
Institutions
- Singapore University of Social Sciences (SG)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2025-07-30
- DOI
- https://doi.org/10.5281/zenodo.16600213
- Primary Topic
- Innovation, Sustainability, Human-Machine Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.00