Irreversibility Detection in Directional Systems: A Reproducible Framework Based on The Pleasure Order

Main Description

This paper proposes a reproducible methodological framework for detecting irreversibility in directional systems using directional potential (Ω) and recovery efficiency dynamics.

Irreversibility is operationalized as sustained failure of recovery efficiency rather than terminal collapse. The framework introduces a minimal dynamic model separating recovery buffers and burdens and detects irreversible tendencies through critical dwell within evolving state space structures.

To ensure empirical applicability, abstract directional changes are mapped into event-based inputs derived from publicly available policy intervention and outbreak event datasets. The framework further extends irreversibility detection into a three-dimensional phase space composed of directional potential (Ω), subsystem coupling (K), and thematic alignment (Align), enabling quantitative risk density estimation and transition probability analysis.

This work does not claim a complete theory of irreversibility. Instead, it provides a falsifiable, reproducible, and extensible methodological entry point for detecting irreversible tendencies across complex directional systems.

Methodological Scope Note

The framework is designed to be compatible with publicly available policy and outbreak event datasets. Exact implementation may vary depending on domain-specific event transformation rules.

The degradation function included in the dynamic model represents monotonic loss of recovery efficiency under excessive burden conditions.

Theoretical Relation Note

This framework is presented as an independent reproducible method. It is structurally related to the broader theoretical framework known as The Pleasure Order, which introduces directional potential as a general structural concept.

Version Note

Version: v1.0

This version presents the initial reproducible detection framework. Future revisions may extend empirical validation, boundary formalization, and cross-scale analysis.