Cognitive Nests: Nested Data-Driven Decision Support System in Regenerative Design from Biology to Ecology

Area of Science:

• Environmental Science
• Systems Design
• Ecological Engineering

Background:

• Regenerative design emphasizes synergistic relationships between sociocultural and ecological systems.
• It necessitates a shift from sustainable patterns to a holistic paradigm fostering circular systems and resource regeneration.
• Current design methodologies often lack the integrated approach required for complex environmental challenges.

Purpose of the Study:

• To present a holistic, data-driven, and self-adaptive design strategy for regenerative adaptive design.
• To develop an integrated problem-solver model applicable across scales, from micro to macro.
• To facilitate evidence-based solutions for strategic environmental and natural resource management through design.

Main Methods:

• Utilizing machine learning feedback loop mechanisms and nested coevolutionary loops.
• Integrating an inclusive feedback loop frame with monitoring systems and intuitive datasets.
• Applying a data-driven, self-adaptive strategy for problem-solving at the intersection of design, construction, and the built environment.

Main Results:

• The proposed model offers a comprehensive approach to regenerative adaptive design.
• It enables optimization of decision-making processes for designers, planners, and city managers.
• The framework supports precise, data-driven feedback for managing complex systems at various scales.

Conclusions:

• Regenerative design offers a paradigm shift beyond sustainability, focusing on restoration and equilibrium.
• Data-driven, self-adaptive strategies are crucial for effective environmental and natural resource management.
• This integrated model provides a scalable solution for harmonious coexistence with the environment through design.