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+---
+id: wiki-2026-0508-homeostasis-항상성
+title: Homeostasis (항상성)
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [homeostasis, 항상성, allostasis, set-point, regulation]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.92
+verification_status: applied
+tags: [biology, physiology, homeostasis, control-systems, allostasis]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: Biology / Physiology
+  applicable_to: [Biology, Cybernetics, Control Theory]
+---
+
+# Homeostasis (항상성)
+
+## 매 한 줄
+> **"매 internal environment 의 의 의 stable 의 maintain"**. Cannon 1929. 매 negative feedback loop. 매 응용: 매 body temperature, glucose, pH, blood pressure. 매 modern: 매 allostasis (Sterling) — 매 anticipatory regulation.
+
+## 매 핵심
+
+### 매 mechanism
+- **Sensor** → **comparator** → **effector** → **feedback**.
+- **Negative feedback**: 매 dominant (95%).
+- **Positive feedback**: 매 amplification (childbirth, blood clotting).
+
+### 매 example
+- **Body temp**: 36.5-37.5°C.
+- **Blood glucose**: 70-100 mg/dL.
+- **Blood pH**: 7.35-7.45.
+- **Osmolality**.
+- **Calcium**.
+
+### 매 vs allostasis
+- **Homeostasis**: 매 fixed set-point.
+- **Allostasis** (Sterling 1988): 매 변화 의 anticipate.
+
+### 매 응용
+1. 매 medical diagnostics.
+2. 매 control system design.
+3. 매 robot adaptation.
+4. 매 RL (intrinsic motivation).
+5. 매 organization theory.
+
+## 💻 패턴
+
+### Negative feedback (PID)
+```python
+class PIDController:
+    def __init__(self, kp, ki, kd, setpoint):
+        self.kp, self.ki, self.kd = kp, ki, kd
+        self.setpoint = setpoint
+        self.integral = 0; self.prev_error = 0
+    
+    def update(self, current, dt):
+        error = self.setpoint - current
+        self.integral += error * dt
+        derivative = (error - self.prev_error) / dt
+        self.prev_error = error
+        return self.kp * error + self.ki * self.integral + self.kd * derivative
+```
+
+### Glucose homeostasis (simplified)
+```python
+def glucose_regulation(glucose, insulin_secretion=True):
+    if glucose > 100:
+        insulin = (glucose - 100) * 0.1  # 매 pancreas 의 release
+        glucose -= insulin * 5  # 매 cells 의 uptake
+    elif glucose < 70:
+        glucagon = (70 - glucose) * 0.1
+        glucose += glucagon * 3  # 매 liver 의 release
+    return glucose
+```
+
+### Body temperature
+```python
+def thermoregulation(core_temp):
+    if core_temp > 37.5:
+        return {'sweat': True, 'vasodilation': True, 'shiver': False}
+    if core_temp < 36.5:
+        return {'sweat': False, 'vasoconstriction': True, 'shiver': True}
+    return {'normal': True}
+```
+
+### Set-point + tolerance
+```python
+class HomeostaticVar:
+    def __init__(self, name, set_point, tolerance):
+        self.name = name; self.set_point = set_point; self.tolerance = tolerance
+    
+    def deviation(self, current):
+        return abs(current - self.set_point) / self.tolerance
+    
+    def is_stable(self, current):
+        return abs(current - self.set_point) <= self.tolerance
+```
+
+### Allostasis (anticipatory)
+```python
+def allostatic_adjust(predicted_demand, current_state):
+    """매 매 demand 의 의 의 의 의 의 adjust."""
+    # 매 e.g., before exercise → cortisol rises
+    if predicted_demand == 'physical_exertion':
+        return adjust(current_state, cortisol=+0.3, hr=+20, glucose=+10)
+    if predicted_demand == 'cold_exposure':
+        return adjust(current_state, metabolism=+0.2, thyroid=+0.1)
+    return current_state
+```
+
+### Robot adaptive control
+```python
+class HomeostatRobot:
+    def __init__(self, target_battery=80):
+        self.target = target_battery
+    
+    def step(self, battery, env):
+        if battery < self.target * 0.3:
+            return 'return_to_charge'
+        if battery < self.target * 0.7:
+            return 'reduce_power'
+        return 'normal'
+```
+
+### Intrinsic motivation (RL)
+```python
+def homeostatic_reward(state, target_state, weights):
+    """매 매 deviation 의 의 의 의 의 reward."""
+    dev = sum(w * abs(state[k] - target_state[k]) for k, w in weights.items())
+    return -dev
+```
+
+### Organizational metaphor
+```yaml
+org_homeostasis:
+  set_points:
+    - growth_rate: 25%
+    - margin: 30%
+    - employee_satisfaction: 7/10
+  feedback_mechanisms:
+    - quarterly_review
+    - employee_survey
+    - financial_audit
+  effectors:
+    - hiring / firing
+    - investment
+    - culture initiative
+```
+
+### Allostatic load
+```python
+def allostatic_load(biomarkers):
+    """매 cumulative wear from chronic stress."""
+    score = 0
+    if biomarkers.cortisol_pm > 8: score += 1
+    if biomarkers.crp > 3: score += 1  # 매 inflammation
+    if biomarkers.hba1c > 5.7: score += 1  # 매 glucose
+    if biomarkers.sbp > 130: score += 1  # 매 hypertension
+    if biomarkers.hdl < 40: score += 1  # 매 cholesterol
+    return score  # 매 0-5 (more = more wear)
+```
+
+## 매 결정 기준
+| 상황 | Concept |
+|---|---|
+| Static target | Homeostasis |
+| Anticipatory | Allostasis |
+| Engineering | PID controller |
+| Biology medicine | Set-point + tolerance |
+| Long-term stress | Allostatic load |
+
+**기본값**: 매 fixed-point system = homeostasis (PID). 매 anticipatory = allostasis. 매 chronic = allostatic load monitor.
+
+## 🔗 Graph
+- 부모: [[Cybernetics Foundations|Cybernetics]]
+- 변형: [[Allostasis]]
+- Adjacent: [[Cybernetics Foundations|Cybernetics]] · [[Free-Energy-Principle]]
+
+## 🤖 LLM 활용
+**언제**: 매 medical / control. 매 biology.
+**언제 X**: 매 simple stateless.
+
+## ❌ 안티패턴
+- **Fixed set-point in dynamic env**: 매 allostasis 의 ignore.
+- **No allostatic load monitor**: 매 chronic stress invisible.
+- **Homeostasis without feedback**: 매 open-loop.
+
+## 🧪 검증 / 중복
+- Verified (Cannon 1929, Sterling allostasis 1988, control theory).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — homeostasis + allostasis + 매 PID / glucose / load code |