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<p>Object-oriented modeling grounds the identity of a system in stable objects and classifications. Dynamic and viewpoint-based classification relax this assumption by allowing entities to change roles and perspectives while preserving identity, but they still treat objects and classifications as the primary ontological foundation. This paper argues that, for systems exhibiting deep functional overlap, contextual modulation, and non-deterministic interaction, identity cannot be adequately explained at this level. We introduce <i>synergic modeling</i>, a paradigm in which the identity of a system is grounded in <i>coherence constraints</i>. Rather than prescribing behavior or enforcing invariants, coherence constraints delimit families of admissible evolutions that preserve system intelligibility across concurrent, uncoordinated functional interactions. Building on the Dynamic Classification Notation (DCN), synergic modeling allows multiple viewpoint-specific functions to act concurrently on shared observations, giving rise to constrained non-determinism and emergent execution order. Determinism is thus relocated from individual state transitions to coherence-preserving trajectories, providing a foundation for modeling complex and adaptive systems.</p>

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Synergic modeling of coherent systems

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月12日

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  • Antonio Bucchiarone,
  • Alfonso Pierantonio,
  • Hans Vangheluwe

摘要

Object-oriented modeling grounds the identity of a system in stable objects and classifications. Dynamic and viewpoint-based classification relax this assumption by allowing entities to change roles and perspectives while preserving identity, but they still treat objects and classifications as the primary ontological foundation. This paper argues that, for systems exhibiting deep functional overlap, contextual modulation, and non-deterministic interaction, identity cannot be adequately explained at this level. We introduce synergic modeling, a paradigm in which the identity of a system is grounded in coherence constraints. Rather than prescribing behavior or enforcing invariants, coherence constraints delimit families of admissible evolutions that preserve system intelligibility across concurrent, uncoordinated functional interactions. Building on the Dynamic Classification Notation (DCN), synergic modeling allows multiple viewpoint-specific functions to act concurrently on shared observations, giving rise to constrained non-determinism and emergent execution order. Determinism is thus relocated from individual state transitions to coherence-preserving trajectories, providing a foundation for modeling complex and adaptive systems.