Spiral-time structures resolve outcomes via constraint satisfaction, suggesting a shift in understanding causality.
We introduce the concept of spiral-time structure as a framework for modeling processes that prioritize the emergence of globally consistent strategic configurations over linear temporal causation. Spiral-time models depart from conventional processual thinking by resolving outcomes through mutual constraint satisfaction across distributed elements, rather than computing them step-by-step in time. This framework is formally grounded in fixed-point logic and constraint-satisfaction systems, drawing from structural explanation, non-causal quantum computation, and equilibrium-based modeling. We provide a mathematical formulation of strategic distributions and analyze how spiral-time systems can settle into coherent configurations across multiple domains—ranging from asynchronous communication and distributed inference to genetic regulatory networks. A simulation of semantic message transmission illustrates the feasibility of spiral-time resolution in recovering meaning without causal message order. Additionally, we explore philosophical connections to agential realism, highlighting how temporal ordering may emerge from intra-actional structure rather than underlie it. Spiral-time thus reframes dynamics as emergent structural coherence rather than as linear evolution.
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Khatibi et al. (2025) studied this question.
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