Unstable Configuration Principle

This work develops a general framework for understanding collective dynamics beyond the classical assumptions of agents, interactions, and stable structures. While contemporary research in complex systems, network science, and multi-agent artificial intelligence has revealed increasing levels of interdependence, non-linearity, and unpredictability, most approaches still rely on the implicit idea that collective phenomena can ultimately be described as systems composed of interacting units (Rahwan et al., 2024; Battiston et al., 2021). This paper argues that such assumptions become inadequate beyond a certain threshold of complexity. The proposed framework introduces a shift from agent-based representations to a field-based perspective. Collective dynamics are modeled as constraint fields within which local configurations emerge, interact, and dissolve. Interactions are replaced by interferences, locality gives way to distributed dependencies, and composability is no longer assumed. As a result, no global structure, stable identity, or equilibrium can be defined. Instead, all configurations are shown to be inherently local, transient, and dependent on evolving constraints. This leads to the formulation of the principle of unstable configuration, which states that no configuration possesses intrinsic stability and that all observable forms are temporary stabilizations within an inherently unstable field. The framework further demonstrates that classical notions such as system state, causality, and control must be reconsidered: states are replaced by trajectories, causality becomes distributed and non-local, and control gives way to navigation under constraints. By synthesizing recent advances across multiple domains, this work provides a unified perspective on the limits of collective modeling and the emergence of instability as a fundamental condition. Rather than proposing a new model of collective systems, it establishes a more general regime in which such systems cannot be globally stabilized or fully represented. The implications are both theoretical and practical. They suggest that future research and engineering efforts—particularly in large-scale AI systems and socio-technical infrastructures—should move away from attempts to enforce global coherence, and instead focus on maintaining viable trajectories within dynamically evolving environments. This work contributes to an emerging paradigm in which collective phenomena are no longer understood as systems to be organized, but as fields to be navigated.