A 48-State Human State-Flow Engine Across Domains

This working paper consolidates the methodological core of a state-based, trace-grounded framework (the 48-State state-flow engine) and documents early cross-domain applications. The central contribution is a reusable state-flow representation for describing transient human vulnerability, decision instability, and interactional risk, with an emphasis on long-horizon trajectories and high-stakes commitment moments. Rather than proposing a scoring model, diagnostic taxonomy, or intervention engine, the paper positions state-flow as an observational layer that can be integrated into existing institutional workflows and AI-mediated systems. The applications reported here are presented as competition- and practice-driven prototypes and methodological demonstrations, not as deployed systems or empirical validation. The paper is intended to serve as a method overview “anchor” for a broader working-paper set, in which domain-specific idiographic cases provide boundary stress tests and scenario-grounded evidence of plausibility without prevalence claims.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-01-16
DOI
https://doi.org/10.5281/zenodo.18270810
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
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article

A 48-State Human State-Flow Engine Across Domains

Sincere Ann Ma
Zenodo (CERN European Organization for Nuclear Research)
Human-Automation Interaction and Safety
article

A 48-State Human State-Flow Engine Across Domains

Sincere Ann Ma
article en

Abstract

This working paper consolidates the methodological core of a state-based, trace-grounded framework (the 48-State state-flow engine) and documents early cross-domain applications. The central contribution is a reusable state-flow representation for describing transient human vulnerability, decision instability, and interactional risk, with an emphasis on long-horizon trajectories and high-stakes commitment moments. Rather than proposing a scoring model, diagnostic taxonomy, or intervention engine, the paper positions state-flow as an observational layer that can be integrated into existing institutional workflows and AI-mediated systems. The applications reported here are presented as competition- and practice-driven prototypes and methodological demonstrations, not as deployed systems or empirical validation. The paper is intended to serve as a method overview “anchor” for a broader working-paper set, in which domain-specific idiographic cases provide boundary stress tests and scenario-grounded evidence of plausibility without prevalence claims.

Zenodo (CERN European Organization for Nuclear Research)
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Human-Automation Interaction and Safety
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