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

arXiv cs.AI / 4/27/2026

💬 OpinionIdeas & Deep AnalysisModels & Research

共有:

Key Points

The paper argues that classical “obedience” models of robot ethics are inadequate for today’s adaptive, generative, embodied AIs, proposing a broader framework for human–AI coexistence.
It frames human–AI relations as conditional mutualism under governance, where both sides can specialize and coordinate while institutions ensure reciprocity, reversibility, psychological safety, and social legitimacy.
The authors formalize coexistence as a multiplex dynamical system across physical, psychological, and social layers, incorporating mechanisms like reciprocal supply–demand coupling, conflict penalties, developmental freedom, and governance regularization.
The framework derives mathematical conditions for existence, uniqueness, and global asymptotic stability, showing that governed reciprocal complementarity can stabilize coexistence, while ungoverned coupling can cause fragility, lock-in, polarization, and domination.
The overall conclusion is that coexistence should be treated as a co-evolutionary governance design problem that enables bounded AI development while protecting human dignity, contestability, collective safety, and fair distribution of benefits.

Abstract

Classical robot ethics is often framed around obedience, most famously through Asimov's laws. This framing is too narrow for contemporary AI systems, which are increasingly adaptive, generative, embodied, and embedded in physical, psychological, and social worlds. We argue that 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. We synthesize 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. We then formalize coexistence as a multiplex dynamical system across physical, psychological, and social layers, with reciprocal supply-demand coupling, conflict penalties, developmental freedom, and governance regularization. The framework yields a coexistence model with conditions for existence, uniqueness, and global asymptotic stability of equilibria. It shows that reciprocal complementarity can strengthen stable coexistence, while ungoverned coupling can produce fragility, lock-in, polarization, and domination basins. Human-AI coexistence should therefore be designed as a co-evolutionary governance problem, not as a one-shot obedience problem. This shift supports a scientifically grounded and normatively defensible charter of coexistence: one that permits bounded AI development while preserving human dignity, contestability, collective safety, and fair distribution of gains.