Emotion Tension Index (ETI): Computational Architecture

1 WHITEPAPER (COMPUTATIONAL LAYER) Emotion Tension Index (ETI): Computational Architecture Author: Author: Jim Y. Huang*1 Version 1.0 — 2025 Abstract This whitepaper introduces the computational architecture of the Emotion Tension Index (ETI). While the theoretical layer defines what emotion is—a structural field generated by external conditions—the computational layer defines how emotional tension becomes inferable from structural data. ETI does not quantify emotion directly. It measures the event structure that produces emotional strain, using two complementary forms: 1. Arithmetic ETI — a direct aggregation of event-level pressures. 2. Geometric ETI — a shape-based representation that reveals how pressures cluster, concentrate, and distort across life domains. The distance between these two layers reflects the internal–external coherence of emotional life. When the arithmetic and geometric surfaces diverge, structural misalignment is present. * Doctoral Researcher , University of Toronto (OISE). Chartered Professional Accountant (CPA, Canada), Cer�fied Prac�sing Accountant (CPA, Australia), and Trust and Estate Prac��oner (TEP). Research focuses on fiscal architecture, cross-border capital movement, intergenera�onal post-tax capacity, and ins�tu�onal inequality. Contact email : jimy[email protected]onto.ca 2 1. Computa�onal Premise The computational layer begins from the three axioms established in the theoretical whitepaper: 1. Emotion always exists 2. Emotion originates in external structures 3. Emotion becomes visible through structural events Given these axioms, ETI does not measure feeling. It measures the event patterns that generate feeling. Thus the computational question becomes: How can event structures be encoded so that emotional tension appears as an interpretable surface? The answer uses a dual-surface model: arithmetic & geometric. 2. Input Domain: Structural Event Data ETI uses structural, observable data—never introspective ratings. Depending on age group, event inputs include: Children (K–12) • attendance stability • early withdrawal or avoidance patterns • classroom conflict or disciplinary events • academic irregularities (sharp drops or spikes) • peer stability • teacher-reported disruptions • home–school transitions Youth & University Students • academic workload shocks • financial stress indicators • scholarship/loan pressure • social network stability • course withdrawals • performance volatility 3 Adults • financial load (bills, liabilities, income gaps) • workplace demand intensity • family conflict • caregiving responsibilities • social isolation or overload • major life-course transitions (relocation, breakup, unemployment) These inputs can be encoded as event units, each carrying structural weight but not subjective meaning. 3. Arithme�c ETI (ETI-A) Definition Arithmetic ETI is a direct structural aggregation: 𝐸𝐸𝐸𝐸𝐼𝐼𝐴𝐴=�𝑤𝑤𝑖𝑖 𝑛𝑛 𝑖𝑖=1 𝐸𝐸𝑖𝑖 Where: • 𝐸𝐸𝑖𝑖= the i-th event’s structural pressure • 𝑤𝑤𝑖𝑖= its contextual weight (derived from structural logic, not self-report) • 𝑛𝑛= the number of relevant events in the evaluation period Interpretation Arithmetic ETI answers the question: How much structural pressure exists? It is linear, transparent, and decomposable. Each domain (financial, academic, social, family) contributes additively. 4 4. Geometric ETI (ETI-G) Definition Geometric ETI constructs a shape from the distribution of events: • clusters • gaps • ridges • turbulence • directional pulls • instability zones These are not subjective; they are properties of the event pattern. ETI-G is the emergent geometry generated by placing events into a structured surface (e.g., time × domain grid or domain × intensity grid). Interpretation ETI-G answers the question: How is structural pressure arranged? Is it clustered, chaotic, concentrated, or dispersed? Two people may have identical ETI-A values but radically different emotional realities if their events produce different geometric patterns. 5. The ETI Divergence (ETI-D) The signature innovation in your architecture is this: 𝐸𝐸𝐸𝐸𝐼𝐼𝐷𝐷=∣𝐸𝐸𝐸𝐸𝐼𝐼𝐴𝐴−𝐸𝐸𝐸𝐸𝐼𝐼𝐺𝐺∣ This divergence is the core signal: • Low divergence = the arithmetic structure matches the lived arrangement → emotional stability • High divergence = the pressure is unevenly distributed → emotional distortion, strain, or instability 5 No introspection. No self-ratings. Only structure. 6. Why Dual-Index Architecture? 6.1 Emotion is not linear Event pressure is linear; emotional experience is not. The dual-index forces the system to capture both. 6.2 Arithmetic shows volume; geometry shows shape Together, they reveal structure & distortion. 6.3 Divergence detects hidden strain A moderate ETI-A might hide a highly unstable ETI-G pattern. Only their difference exposes this. 6.4 This mirrors physical sciences • electricity: voltage vs. current • mechanics: force vs. acceleration • signal theory: amplitude vs. waveform Volume ≠ Pattern. That is the entire point. 7. Output Interpreta�on ETI-A → Structural Load (Sum of pressures) ETI-G → Structural Arrangement (Pattern of pressures) ETI-D → Internal Consistency / Emotional Coherence (Alignment vs. distortion) 6 These outputs can be visualized as: • napkin-style maps • heat surfaces • vector fields • path instability diagrams • turbulence clouds 8. No Psychological Claims ETI deliberately avoids: • personality traits • symptom checklists • clinical diagnosis • emotional vocabulary • normative judgments It is structural, not psychological. It reads the world’s pressure, not the mind’s confession. 9. Conclusion The computational layer of ETI provides a rigorous, structural approach to quantifying emotional tension using external event data only. Its dual-index architecture mirrors the logic behind complex structural systems: volume and pattern must both be measured, and their divergence carries the deepest informational value. This whitepaper forms the technical base for future possible implementations in education, workplace settings, community analysis, or structural mental health research—without departing from the foundational principle: Emotion is the shadow cast by structure. To understand the shadow, one must map the structure.