CYCLE STRUCTURE LAB
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Structural drift, selection, and governability in reinforcing systems under constraint.
Cycle Structure Lab is an independent research project led by Y. Hori.
The project develops structural models for understanding how reinforcing systems grow, drift, allocate value, and remain governable under constraint.
Research Orientation
Cycle Structure Lab studies systems in which growth is not merely a question of acceleration, but of reinforcement, selection, and control.
The work focuses on:
The work focuses on:
reinforcement under constraint in digital systems
structural drift under amplification
selection latency and binding constraint
governable units for action, allocation, and knowledge use
systems-theoretic diagnostics for AI-mediated environments
Analytical Lens
Forward × Cycle × Backward (FxCxB)
Forward × Cycle × Backward (FxCxB) is used in this work as a research framework for describing three interacting dynamics in reinforcing systems under constraint.
As a framework, FxCxB interprets growth, allocation, and governance as related forms of reinforcement under constraint.
Forward
Forward
Forward initiates variation.
It refers to the ignition of new participation, signals, opportunities, or trajectories before they are filtered by selection or constraint.
Cycle
Cycle
Cycle amplifies trajectories through reinforcement.
It describes how feedback loops, repeated interaction, accumulation, or platform dynamics strengthen some trajectories over others.
Backward
Backward
Backward selects what is allowed to persist under constraint.
It represents explicit structural selection: the rules, reallocations, termination conditions, validation processes, and control mechanisms that determine which reinforced trajectories remain admissible.
Structural Drift
The central concern of this framework is structural drift:
a rate-mismatch condition in which visible expansion continues while the system’s capacity for validation, selection, control, or reallocation fails to update at the same pace.
Structural drift can coexist with positive surface metrics. A system may continue to grow while its governability weakens underneath.
This framing is used to examine how reinforcing systems remain durable only when selection mechanisms update at a pace commensurate with reinforcement-driven amplification.
Research Threads
The work can be read through three connected threads:
Reinforcement under Constraint
Reinforcement under Constraint
How do reinforcing systems grow, drift, and remain governable when amplification and selection operate at different speeds?
Structural Drift and Selection Latency
Structural Drift and Selection Latency
How can drift be detected before visible failure, when reinforcement continues but selection begins to lag?
Governable Units in Digital Systems
Governable Units in Digital Systems
How can action, allocation, and knowledge use be represented as governable units under constraint?
Published Work
Reinforcement under Constraint: A Systems Model of Forward, Cycle, and Backward Dynamics (FxCxB)
Presented at the 70th Annual Meeting of the International Society for the Systems Sciences (ISSS 2026).
Full paper and presentation slides published in the ISSS 2026 Proceedings.
Selected Outputs
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