Algorithmic Ritual Systems: Structural Dynamics of Belief-Light Ritual Environments

This paper identifies and analyzes a class of large-scale digital coordination environments in which ritualized participation persists independently of shared belief systems. We refer to these environments as Algorithmic Ritual Systems (ARS). In ARS, repeated, socially legible behaviors are stabilized through mimetic replication and algorithmic visibility selection rather than doctrinal coherence or institutional authority. Drawing on structural analysis, we describe how algorithmic amplification mechanisms select for recognizable ritual grammars, producing participation loops that lack durable closure and exhibit non-cumulative recognition. We further show how these dynamics converge into pyramidal attention hierarchies characterized by upward extraction of participation labor and limited redistributive return. Finally, we outline four recurrent system-level failure modes---exhaustion, semantic inflation, fragmentation, and external capture---that emerge from the thermodynamic properties of these environments. This paper makes no normative claims and proposes no interventions; it offers a descriptive framework for analyzing belief-light ritual ecosystems stabilized by algorithmic selection.