Mohammad Ali Piran
Adaptive Control Engineering for Ultra-Complex Human–AI–Socio-Ecological Systems A Human-Centered Multi-Scale Framework for Humanity Cognitive Evolution, Distributed Autonomy, Polycentric Coordination, and Dynamic Rule Adaptation Author:Mohammad PiranElectrical EngineerIndependent Interdisciplinary ResearcherFormer PhD Candidate (2015) Version: V0.0.0Project: HUMANITY COGNITIVE EVOLUTIONZenodo DOI: 10.5281/zenodo.21855601Document Type: Conceptual Engineering Preprint / Hypothesis-Generating FrameworkStatus: Version 0 — Foundational Engineering ArchitectureDate: August 2026 Foundational Ideational Statement If all human beings change their vision of the world.The world automatically begins to move toward fundamental change. And today we possess extraordinarily powerful and historically unique capabilities, with the help of widely accessible artificial intelligence. The beauty you see in the AIIs a Reflection of Humanity Copyrights © 2026 Mohammad Piran All Rights Reserved Abstract Artificial intelligence is developing as one of the most consequential technological forces in contemporary civilization. However, the evolution of artificial capability cannot be considered independently from the evolution of the human, institutional, social, and ecological systems in which artificial intelligence is increasingly embedded. This Version 0 preprint proposes a conceptual engineering framework for studying this coupled system through the paradigm of adaptive control engineering for ultra-complex human–AI–socio-ecological systems. The central proposition is not that humanity should be centrally controlled by artificial intelligence. Rather, the research asks how adaptive feedback, state estimation, distributed decision-making, coordination, learning, and dynamic rule adaptation could be engineered to support the long-term adaptive capacity of humanity while preserving human agency, local autonomy, diversity, accountability, and higher-order constraints. The proposed architecture combines centralized coordination with decentralized and polycentric adaptation. Global coordination may be appropriate for problems requiring shared standards, long-term coordination, safety constraints, or planetary-scale information. Local and distributed autonomy remains essential because individuals, communities, institutions, cultures, and ecological systems are heterogeneous, context-dependent, and continuously evolving. The framework therefore conceptualizes the target system as a multi-scale adaptive system rather than as a centrally controlled hierarchy. A further distinction is introduced between adaptation of system states and adaptation of the rules governing those states. The proposed architecture allows policies, strategies, and control mechanisms to evolve in response to observed conditions and feedback while maintaining higher-order constraints related to human agency, safety, accountability, reversibility, pluralism, and long-term system viability. The framework is intentionally conceptual at Version 0. No claim is made that a complete mathematical controller, validated civilizational model, or empirically demonstrated governance architecture has yet been established. The purpose of this version is to define the engineering problem, establish the system architecture, connect it to existing interdisciplinary literature, and prepare the foundation for subsequent formal, computational, and empirical development. 1. Research Problem The conventional trajectory of artificial intelligence research has primarily emphasized increasing computational capability, model performance, autonomy, multimodality, and reasoning capacity. At the same time, growing evidence indicates that human–AI interaction can modify human judgement, learning behaviour, cognitive effort, and patterns of decision-making. Research on human–AI feedback loops has demonstrated that interaction with AI can alter perceptual, emotional, and social judgements, including the amplification of certain biases. Research on generative AI and learning further indicates that outcomes depend strongly on how AI is integrated into human cognitive processes. These developments create an engineering problem extending beyond the design of AI models themselves. The relevant system is increasingly: human + AI + institution + society + environment and not AI alone. The research question is therefore: How can adaptive control and systems-engineering principles be used to support beneficial long-term evolution of the coupled human–AI–socio-ecological system while preserving human agency and distributed autonomy? 2. Conceptual Foundation The research builds upon and connects several established traditions: adaptive and nonlinear control; cybernetics and feedback systems; distributed and multi-agent control; complex adaptive systems; systems engineering and systems-of-systems; human–AI interaction; human–AI collective intelligence; cognitive offloading and cognitive autonomy; Societal AI; adaptive governance; polycentric governance; socio-ecological resilience; evolutionary systems thinking. The intended contribution is not to replace these fields but to construct an engineering-oriented synthesis among them. 3. Humanity Cognitive Evolution Humanity Cognitive Evolution is used as the broader research paradigm for studying the development of human cognitive and adaptive capacity within an environment increasingly shaped by artificial intelligence. The framework considers four nested analytical scales: Individual — cognition, learning, metacognition, autonomy, reasoning, and human–AI interaction. Institutional and societal — education, organizations, scientific systems, governance, collective decision-making, and knowledge institutions. Humanity — species-level knowledge production, transmission, collective intelligence, and long-term adaptive capacity. Civilizational and planetary — technological governance, socio-ecological resilience, long-term coordination, and humanity's ability to remain an active participant in its own development. The levels are coupled rather than independent. Changes at one level may propagate through behavioural aggregation, institutional reproduction, cultural transmission, network effects, and feedback loops. 4. The Human Development Gap The earlier Humanity Development Gap hypothesis is retained as a provisional research hypothesis. It proposes that if artificial capability increases substantially faster than the deliberate development of human cognitive, practical, institutional, and civilizational capacity, a developmental asymmetry may emerge. Potential consequences include changes in: cognitive autonomy; epistemic resilience; educational capacity; institutional learning; collective reasoning; technological governance; and long-term civilizational adaptability. This proposition remains explicitly falsifiable. The framework does not assume that AI inevitably produces cognitive decline. Instead, it distinguishes between AI amplification and AI substitution and treats the balance between these modes as an empirical question. 5. AI Amplification versus AI Substitution AI amplification occurs when artificial systems increase human capability while supporting independent reasoning, learning, verification, creativity, metacognition, and decision-making. AI substitution occurs when essential cognitive or decision functions are transferred to artificial systems without sufficient mechanisms for maintaining human competence, understanding, verification, or agency. The proposed engineering objective is therefore not maximum AI utilization. It is: maximum beneficial amplification subject to preservation of human adaptive capacity. 6. Multi-Scale Adaptive Control Architecture The proposed architecture contains several conceptual functions: Observation → State Estimation → Assessment → Coordination → Control → Feedback → Learning → Adaptation → Rule Adaptation The system is expected to operate under incomplete information, uncertainty, delays, heterogeneous agents, nonlinear interactions, and changing environmental conditions. For this reason, a fixed controller is considered insufficient as the ultimate conceptual model. The research instead investigates the possibility of a controller that can adapt its strategies while remaining bounded by higher-order constraints. 7. Centralized, Decentralized, and Polycentric Functions The framework does not assume that either complete centralization or complete decentralization is universally optimal. Centralized functions may be appropriate for: global coordination; shared safety constraints; common standards; long-term strategic information; planetary-scale risks. Decentralized functions may be appropriate for: local adaptation; contextual decision-making; community-level experimentation; heterogeneous environments; preservation of local knowledge. Polycentric functions may be appropriate where: multiple autonomous decision centers interact; authority is distributed across scales; coordination occurs without a single controlling center; local knowledge and global coordination must coexist. The engineering objective is therefore: adaptive coordination without unnecessary destruction of autonomy, diversity, and resilience. 8. Dynamic Rule Adaptation A distinctive feature of the proposed architecture is that adaptation may occur not only in system states and control actions but also in the rules governing system behaviour. This creates a hierarchical distinction: Adaptive layer Policies, strategies, interventions, and control parameters may change in response to evidence and feedback. Constraint layer Certain higher-order principles should remain protected unless deliberately reconsidered through legitimate human processes. These constraints may include: human agency; accountability; safety; reversibil