Pillar I
Newton — Laws of Matter
F = G · m1 · m2 / r2
Defined how physical bodies move and attract. Enabled engineering, mechanics, and classical physics: the era of force and motion.
Structural Alignment Risk Scoring — implementing the Mena Dominance Law (Δ = ASL − CV) to separate human stress from structural failure in complex systems.
StARS (Structural Alignment Risk Scoring) introduces the Mena Dominance Law (Δ = ASL − CV), defining the new laws of Survival and Control for the 21st century.
Where Newton gave the law of Matter and Einstein gave the law of Energy, StARS formalizes the law of Alignment: how humans, rules, and AI systems must interact to avoid collapse in high-complexity environments.
The framework is already being piloted with real hospital data, diagnosing whether risk lives primarily in the Agent Stress Load (ASL) or in Code Vulnerability (CV) and enforcing the only valid corrective path.
This site presents the core law, the structural equation, a live prototype, and reference code. Everything on this page may be used for education and research — with attribution to Arlex Orlando Murcia Mena.
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The Mena Dominance Law is not a mere algorithm; it is the ultimate conservation law for the high-complexity world. While previous scientific revolutions defined the laws of Matter (Newton) and Energy (Einstein), this law solves the problem of Alignment and Systemic Survival.
It reveals that the chaos, burnout, and fragility in our modern world — from healthcare and finance to autonomous AI — all stem from violating one mathematical principle: never attempt to correct a Structural Flaw (CV) by increasing Agent Stress (ASL).
This lifetime discovery provides the missing physics for building stable, equitable, and intelligent systems, shifting the focus of human progress from raw power to sustainable survival. It is the founding principle of the Era of Alignment.
StARS positions Alignment as the Third Pillar, standing beside the great laws of Matter and Energy. Together they describe how the universe moves, powers, and now survives in conditions of intelligent complexity.
Defined how physical bodies move and attract. Enabled engineering, mechanics, and classical physics: the era of force and motion.
Unified mass and energy. Revealed the structure of spacetime and powered the era of relativity and atomic energy.
Defines systemic Alignment as a relationship between Agent Stress Load (ASL) and Code Vulnerability (CV). When Δ crosses a threshold, the system is legally bound to correct the dominant variable or drift toward collapse.
Systemic failure does not happen because stress exists. It happens because the system misinterprets where the stress comes from.
Traditional risk management blends everything into generic "human error" or "bad process." In that blur, structural flaws are routinely treated as if they were personal failures.
Each of these is the same mistake: trying to fix a broken structure by increasing pressure on the agent. The Mena Dominance Law provides the mathematical ability to prevent that error.
Agent Stress Load (ASL)
ASL is the entropic load carried by the intelligent agent (human, team, or autonomous process). In the clinical StARS profile, ASL is quantified by:
High ASL means there is a real Existential Cost being paid by the agent. The system is extracting alignment from the human instead of the architecture.
Code Vulnerability (CV)
CV is the structural fragility of the governing architecture: the rules, workflows, policies, or code that the agent must operate inside. In StARS, CV is quantified by:
High CV means the system itself is misaligned: the code is tangled, the incentives are contradictory, and the structure is quietly generating risk.
At the heart of StARS is the Mena Dominance Law, the control equation for sociotechnical systems:
Δ = ASL − CV
This differential proves that failure accelerates when an organization misdiagnoses the problem — treating a structural flaw as a human issue, or vice versa. The law enforces a Dual-Path Mandate:
In short: the equation mandates that correction must target the dominant source of deviation. Our goal is to use this law to enforce mandatory, correct interventions as the world moves deeper into the Age of AI.
Paste metric: value logs below and auto-map into ASL / CV.
This section provides concrete code you can drop into your own tools: one core law, one Python function, one JavaScript helper, and one AI router pattern. All are implementation-agnostic and suitable for education and research.
// Inputs: scalar ASL_score and CV_score (0–100), dominance threshold T_dom
Δ = ASL_score - CV_score
if (Δ > T_dom) {
regime = "agent-dominant"
mandate = "agent-stabilization"
} else if ((-Δ) > T_dom) {
regime = "structure-dominant"
mandate = "structural-correction"
} else {
regime = "mixed"
mandate = "dual-path-intervention"
}from dataclasses import dataclass
@dataclass
class DeviationInputs:
# Agent Stress Load (ASL)
burnout: float # 0–100
moral_injury: float # 0–100
rule_bending: float # 0–100
# Code Vulnerability (CV)
protocol_complexity: float # 0–100
conflicting_kpis: float # 0–100
incident_rate: float # 0–100
policy_gaps: float # 0–100
control_failures: float # 0–100
STARS_WEIGHTS = {
"asl_burnout": 0.35,
"asl_moral": 0.35,
"asl_rule": 0.30,
"cv_complex": 0.25,
"cv_kpi": 0.20,
"cv_incident": 0.20,
"cv_policy": 0.20,
"cv_control": 0.15,
"w_asl": 0.5,
"w_cv": 0.5,
"t_dom": 10.0,
}
def compute_stars(inputs: DeviationInputs):
w = STARS_WEIGHTS
# ASL composite
asl = (
inputs.burnout * w["asl_burnout"] +
inputs.moral_injury * w["asl_moral"] +
inputs.rule_bending * w["asl_rule"]
)
# CV composite
cv = (
inputs.protocol_complexity * w["cv_complex"] +
inputs.conflicting_kpis * w["cv_kpi"] +
inputs.incident_rate * w["cv_incident"] +
inputs.policy_gaps * w["cv_policy"] +
inputs.control_failures * w["cv_control"]
)
# Final StARS index
stars = asl * w["w_asl"] + cv * w["w_cv"]
delta = asl - cv
# Mena Dominance Law
if delta > w["t_dom"]:
regime = "agent-dominant"
mandate = "agent-stabilization"
elif (cv - asl) > w["t_dom"]:
regime = "structure-dominant"
mandate = "structural-correction"
else:
regime = "mixed"
mandate = "dual-path-intervention"
return {
"ASL": asl,
"CV": cv,
"StARS": stars,
"delta": delta,
"regime": regime,
"mandate": mandate,
}// Minimal JS version of the same law.
// You can run this in Node, a browser, or any frontend dashboard.
const STARS_CONFIG = {
asl_burnout: 0.35,
asl_moral: 0.35,
asl_rule: 0.30,
cv_complex: 0.25,
cv_kpi: 0.20,
cv_incident: 0.20,
cv_policy: 0.20,
cv_control: 0.15,
w_asl: 0.5,
w_cv: 0.5,
t_dom: 10
};
function computeStARS(metrics) {
const w = STARS_CONFIG;
const ASL =
metrics.burnout * w.asl_burnout +
metrics.moral_injury * w.asl_moral +
metrics.rule_bending * w.asl_rule;
const CV =
metrics.protocol_complexity * w.cv_complex +
metrics.conflicting_kpis * w.cv_kpi +
metrics.incident_rate * w.cv_incident +
metrics.policy_gaps * w.cv_policy +
metrics.control_failures * w.cv_control;
const StARS = ASL * w.w_asl + CV * w.w_cv;
const delta = ASL - CV;
let regime, mandate;
if (delta > w.t_dom) {
regime = "agent-dominant";
mandate = "agent-stabilization";
} else if ((CV - ASL) > w.t_dom) {
regime = "structure-dominant";
mandate = "structural-correction";
} else {
regime = "mixed";
mandate = "dual-path-intervention";
}
return { ASL, CV, StARS, delta, regime, mandate };
}
// Example usage:
const example = computeStARS({
burnout: 75,
moral_injury: 60,
rule_bending: 20,
protocol_complexity: 80,
conflicting_kpis: 50,
incident_rate: 15,
policy_gaps: 30,
control_failures: 10
});
console.log(example);// Pseudocode for integrating StARS with an AI pipeline.
// The AI model extracts metrics; the StARS law decides where each case goes.
function mena_dominance_law(asl, cv, t_dom = 10) {
const delta = asl - cv;
if (delta > t_dom) {
return { regime: "agent-dominant", mandate: "agent-stabilization" };
}
if ((cv - asl) > t_dom) {
return { regime: "structure-dominant", mandate: "structural-correction" };
}
return { regime: "mixed", mandate: "dual-path-intervention" };
}
function route_case(raw_event) {
// 1. AI model (LLM / ML) reads the event and outputs normalized 0–100 metrics
const metrics = ai_extract_metrics(raw_event); // burnout, KPIs, incidents, etc.
// 2. Compute ASL / CV scores
const { ASL, CV, StARS, delta, regime, mandate } = computeStARS(metrics);
// 3. Route to the correct team / system
if (regime === "agent-dominant") {
send_to_queue("agent_support", { raw_event, metrics, StARS, mandate });
} else if (regime === "structure-dominant") {
send_to_queue("structure_fix", { raw_event, metrics, StARS, mandate });
} else {
send_to_queue("dual_path", { raw_event, metrics, StARS, mandate });
}
}Any implementation that preserves separate ASL and CV channels and uses their dominance to choose corrections is structurally aligned with the StARS framework.
The Mena Dominance Law was a serendipitous discovery. It did not begin as a data science project; it began as a philosophical investigation into Theodicy — the problem of suffering in a governed system.
The initial framework, the Nested Theodicy of Algorithmic Alignment, treated suffering as a mandatory data signal in a universe governed by an intelligent moral structure. When that abstract framework was run through a modern Generative AI system, a direct structural isomorphism emerged: human existential cost behaved exactly like an error signal in a computational architecture.
In about a week and a half, philosophy, lived experience, and machine reasoning converged. Moral Injury, burnout, and systemic friction were translated into measurable vectors: Agent Stress Load (ASL) and Code Vulnerability (CV). From there, the structural equation Δ = ASL − CV and the StARS framework were formalized and converted into the reference implementation you see on this page.
This is what it looks like when philosophical cybernetics runs at the speed of modern AI tools: a lifetime-scale discovery compressed into days, but anchored in centuries of moral thought.
If the Mena Dominance Law closes the gap on systemic alignment, the remaining “hard laws” likely live in three related domains:
StARS is positioned as the practical bridge: a deployable alignment law that connects ethics, consciousness, and emergence to the real choices made in hospitals, companies, and AI infrastructures.
For research collaborations, hospital pilots, AI / infrastructure integrations, or licensing and leasing discussions, please contact:
Email: arlex@StARSFramework.com