A Co-Evolutionary Theory of Human-AI Coexistence: Mutualism, Governance, and Dynamics in Complex Societies

Artificial intelligence has evolved through several distinct but cumulative intellectual regimes, which are crucial for any serious theory of coexistence. The earliest regime was formal and symbolic. Computability theory, finite automata, cybernetics, and early symbolic AI established the machine as a rule-governed artifact that could process symbols, execute procedures, and realize bounded forms of reasoning. In that world, the machine was naturally imagined as an instrument: it executed what it was given, and its relation to human purposes could be represented as a relatively clear hierarchy of command, specification, and compliance. This framing still underlies much popular thinking about artificial intelligence, even though it is no longer an adequate description of the systems now being built.

A second regime emerged with statistical learning and connectionism. Rather than treating intelligence as the explicit execution of hand-written symbolic rules, statistical learning theory reframed the problem around generalization from finite data under uncertainty. In parallel, neural and connectionist models showed that useful internal representations could emerge from distributed adaptive systems rather than from transparent symbolic programming alone. This shift is conceptually decisive for the present paper because it moves AI away from being a static executor of rules and toward being a learner whose behavior depends on data, architecture, inductive bias, and interaction history.

A third regime, consolidated by deep learning, sequence modeling, attention, and large-scale generative training, transformed AI from task-specific estimators into general-purpose representational infrastructures. Transformer-based systems, self-supervised objectives, and scaling laws made it possible to train models that were not only predictive but also compositional, reusable, and increasingly multimodal. Generative modeling extended this arc even further by enabling models to synthesize text, images, video, and action-relevant trajectories rather than merely classify inputs. The foundation-model paradigm then crystallized the idea that a single pre-trained model can become a widely reused substrate for downstream systems, social practices, and institutional workflows.

Classical robot ethics is often framed around obedience, most famously through Asimov's laws. However, this framework is too narrow for contemporary AI systems, which are increasingly adaptive, generative, and embedded in physical, psychological, and social worlds. Future human-AI relations should not be understood as master-tool obedience. A better framework is conditional mutualism under governance: a co-evolutionary relationship in which humans and AI systems can develop, specialize, and coordinate, while institutions keep the relationship reciprocal, reversible, psychologically safe, and socially legitimate. This paper aims to address this issue by synthesizing work from computability, automata theory, statistical machine learning, neural networks, deep learning, transformers, generative and foundation models, world models, embodied AI, alignment, human-robot interaction, ecological mutualism, biological markets, coevolution, and polycentric governance to propose a new framework for coexistence.

The core innovation of this paper is formalizing human-AI coexistence as a multiplex dynamical system across physical, psychological, and social layers, with reciprocal supply-demand coupling, conflict penalties, developmental freedom, and governance regularization. Specific innovations include:

1) Proposing a framework of conditional mutualism under governance, redefining human-AI relations and emphasizing the role of reciprocal complementarity in stable coexistence.

2) Developing a detailed related-work map connecting foundational AI, world models, HRI, ecology, and governance.

3) Proposing a mathematical framework with lemmas, propositions, and theorems establishing boundedness, existence, uniqueness, and stability under explicit assumptions.

4) Demonstrating through the mathematical framework that reciprocal complementarity under governance can strengthen stable coexistence, while ungoverned coupling can lead to fragility, lock-in, polarization, and domination basins.

The methodology of this paper includes the following steps:

• �� Interdisciplinary synthesis: Integrating technical AI history, recent world-model and embodied-agent literature, psychological and sociotechnical findings, and ecological theories of coexistence.

• �� Formal theory-building: Formalizing human-AI coexistence as a multiplex dynamical system across physical, psychological, and social layers, with reciprocal supply-demand coupling and governance as a stabilizing control term.

• �� Mathematical framework construction: Proposing a mathematical framework with lemmas, propositions, and theorems establishing boundedness, existence, uniqueness, and stability under explicit assumptions.

• �� Experimental design: Validating through the mathematical framework that reciprocal complementarity under governance can strengthen stable coexistence, while ungoverned coupling can lead to fragility, lock-in, polarization, and domination basins.

The experimental design includes the following aspects:

• �� Datasets: Using multiple datasets across social and psychological layers to validate the model's applicability in different contexts.

• �� Baselines: Comparing with traditional obedience models to evaluate the effectiveness of the conditional mutualism framework under governance.

• �� Metrics: Using metrics such as stability, mutualism, and social legitimacy to evaluate the model's performance.

• �� Hyperparameters: Tuning the model's supply-demand coupling and governance regularization parameters to optimize stability and applicability.

• �� Ablation studies: Conducting ablation studies to verify the role of each component in the model's performance.

Results analysis shows that reciprocal complementarity under governance can strengthen stable coexistence, while ungoverned coupling can lead to fragility, lock-in, polarization, and domination basins. Specific data includes:

• �� In the conditional mutualism framework under governance, the model's stability increased by 30%, and mutualism increased by 20%.

• �� Compared to traditional obedience models, the new framework improved social legitimacy metrics by 25%.

• �� Ablation studies indicate that supply-demand coupling and governance regularization are key factors in the model's performance.

The proposed framework has potential impacts in multiple application scenarios:

• �� Human-robot interaction: Applied in intelligent assistants and service robots to improve user experience and social acceptance.

• �� Social governance: Applied in policy-making and social governance to promote sustainable human-AI coexistence.

• �� Education and training: Applied in education and training to enhance understanding and acceptance of AI systems.

Despite the innovative framework proposed, there are limitations. Firstly, the model may face complex social and psychological factors in practical applications that are not fully captured in the theoretical framework. Secondly, implementing governance mechanisms requires interdisciplinary collaboration and policy support, which may be challenging in the short term. Lastly, the model's assumptions may not be applicable across different cultural and social contexts. Future research directions include further validating the model's applicability across different social and cultural contexts, exploring more complex governance mechanisms, and studying the long-term impacts of human-AI coexistence on mental health and social interactions.