2nd/10_Wiki/Topic_Programming/AI_and_ML/Homeostasis (항상성).md

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id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-homeostasis-항상성	Homeostasis (항상성)	10_Wiki/Topics	verified	self	homeostasis 항상성 allostasis set-point regulation	none	A	0.92	applied	biology physiology homeostasis control-systems allostasis		2026-05-10	pending	language applicable_to Biology / Physiology 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)

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)

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

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

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)

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

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)

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

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

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
• 변형: Allostasis
• Adjacent: Cybernetics Foundations · 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