refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조

에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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+---
+id: wiki-2026-0508-cybernetics
+title: Cybernetics Foundations
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [cybernetics, Norbert Wiener, feedback loop, homeostasis, control theory, second-order cybernetics, autopoiesis]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.88
+verification_status: applied
+tags: [cybernetics, systems-thinking, control-theory, feedback, homeostasis, ai-history, wiener, ashby]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: systems theory
+  applicable_to: [Multi-agent Design, Control Systems, AI Architecture]
+---
+
+# Cybernetics
+
+## 매 한 줄
+> **"매 living + 매 machine 의 control + communication 의 universal"**. Norbert Wiener (1948). 매 feedback loop + homeostasis + 매 information transmission. 매 AI / robotics / biology / sociology 의 cross-disciplinary roots. 매 modern: 매 multi-agent system + 매 control + 매 active inference.
+
+## 매 핵심
+
+### Wiener (1st-order cybernetics)
+- 매 system 의 observe.
+- 매 input → process → output → feedback.
+- 매 negative feedback 의 stabilize.
+- 매 positive feedback 의 amplify / explode.
+
+### 2nd-order cybernetics (Foerster, Maturana)
+- 매 observer 의 part of system.
+- 매 self-reference.
+- 매 autopoiesis (self-creating).
+
+### 매 핵심 concept
+
+#### Feedback Loop
+- **Negative**: 매 thermostat, 매 homeostasis.
+- **Positive**: 매 microphone feedback, 매 viral growth.
+
+#### Homeostasis
+- 매 internal state 의 stable.
+- 매 perturbation 의 counter.
+- 매 biological + 매 artificial.
+
+#### Black Box
+- 매 internal X — 매 I/O 만.
+- 매 abstraction principle.
+
+#### Variety (Ashby's Law)
+- 매 controller 의 variety ≥ disturbance 의 variety.
+- 매 system 의 control 의 limit.
+
+#### Requisite Variety
+- 매 system 의 manage 의 environment 의 capable variety.
+
+### 매 history
+- 1948 Wiener "Cybernetics".
+- 1956 Dartmouth (AI 이름).
+- Macy Conferences (1946-1953): cyber + systems.
+- 2nd-order: Heinz von Foerster, Maturana-Varela.
+- Decline (1970s): AI vs Cybernetics 의 split.
+- Revival (2000s+): control + agent + autopoiesis.
+
+### 매 modern relevance
+
+#### Control theory
+- PID, MPC, optimal control.
+- Robotics: 매 closed-loop.
+- 매 autonomous vehicle.
+
+#### Multi-agent system
+- 매 distributed control.
+- 매 swarm.
+
+#### Active inference (Friston)
+- 매 free energy minimization.
+- 매 cybernetic + Bayesian.
+- [[Bayesian-Brain-Hypothesis]] 참조.
+
+#### Organizational
+- 매 systems thinking (Senge "Fifth Discipline").
+- 매 viable system model (Beer).
+
+#### AI alignment
+- 매 reward hacking 의 cybernetic 의 lens.
+- 매 unintended feedback.
+
+### 매 응용
+1. **Thermostat / HVAC**: 매 simplest.
+2. **Robotics**: 매 control.
+3. **Biology**: 매 endocrine / neural.
+4. **Economics**: 매 supply-demand.
+5. **Organization**: 매 managed system.
+6. **AI agent**: 매 action + observation loop.
+
+## 💻 패턴
+
+### PID Controller (classic feedback)
+```python
+class PID:
+    def __init__(self, kp=1.0, ki=0.1, kd=0.05):
+        self.kp, self.ki, self.kd = kp, ki, kd
+        self.integral = 0
+        self.prev_error = 0
+    
+    def update(self, target, current, dt):
+        error = target - current
+        self.integral += error * dt
+        derivative = (error - self.prev_error) / dt
+        
+        output = self.kp * error + self.ki * self.integral + self.kd * derivative
+        self.prev_error = error
+        return output
+
+# 매 example: 매 thermostat
+pid = PID()
+while True:
+    error_correction = pid.update(target_temp=22, current=sensor.read(), dt=1.0)
+    heater.set_power(error_correction)
+    sleep(1)
+```
+
+### Negative feedback (homeostasis)
+```python
+class Homeostat:
+    def __init__(self, target):
+        self.target = target
+        self.state = target
+    
+    def perturbate(self, disturbance):
+        self.state += disturbance
+    
+    def regulate(self, gain=0.1):
+        # 매 negative feedback 의 self-correct
+        error = self.target - self.state
+        self.state += gain * error
+
+# 매 simulate
+h = Homeostat(target=37)  # 매 body temp
+for _ in range(100):
+    h.perturbate(random.gauss(0, 1))  # 매 environment
+    h.regulate()
+print(h.state)  # 매 ~37
+```
+
+### Positive feedback (caution)
+```python
+def viral_growth(initial, gain, steps):
+    """매 positive feedback — 매 amplify."""
+    state = initial
+    for _ in range(steps):
+        state *= (1 + gain)
+        if state > LIMIT: break  # 매 saturation needed
+    return state
+```
+
+### Ashby's Law check
+```python
+def can_control(controller_variety, disturbance_variety):
+    """매 'Only variety can destroy variety.'"""
+    return controller_variety >= disturbance_variety
+
+# 매 example: 매 thermostat 의 매 1 mode 만 → 매 매 disturbance 매 multiple 의 fail
+print(can_control(1, 5))  # False
+print(can_control(10, 5))  # True
+```
+
+### Black Box (system identification)
+```python
+import numpy as np
+
+def identify_black_box(system_fn, n_samples=100):
+    """매 input-output 의 model 의 fit."""
+    X = np.random.randn(n_samples, INPUT_DIM)
+    Y = np.array([system_fn(x) for x in X])
+    
+    from sklearn.linear_model import LinearRegression
+    model = LinearRegression().fit(X, Y)
+    return model  # 매 estimated transfer function
+```
+
+### Active inference (Friston-style)
+```python
+def active_inference(belief, world_model, possible_actions):
+    """매 minimize expected free energy."""
+    best_action, best_efe = None, float('inf')
+    for a in possible_actions:
+        next_belief = world_model.predict(belief, a)
+        # 매 epistemic value
+        info_gain = expected_kl(next_belief, belief)
+        # 매 pragmatic value
+        pragmatic = expected_log_preference(next_belief)
+        efe = -info_gain - pragmatic
+        if efe < best_efe:
+            best_efe, best_action = efe, a
+    return best_action
+```
+
+### MPC (Model Predictive Control)
+```python
+import cvxpy as cp
+
+def mpc_step(x_current, x_target, horizon=10):
+    x = cp.Variable((horizon + 1, STATE_DIM))
+    u = cp.Variable((horizon, ACTION_DIM))
+    
+    cost = 0
+    constraints = [x[0] == x_current]
+    for t in range(horizon):
+        cost += cp.sum_squares(x[t+1] - x_target) + 0.1 * cp.sum_squares(u[t])
+        constraints += [x[t+1] == A @ x[t] + B @ u[t]]
+        constraints += [cp.abs(u[t]) <= U_MAX]
+    
+    cp.Problem(cp.Minimize(cost), constraints).solve()
+    return u[0].value
+```
+
+### Multi-agent feedback (consensus)
+```python
+def consensus_step(agents, alpha=0.1):
+    """매 distributed averaging."""
+    new_states = []
+    for a in agents:
+        avg_neighbor = mean(n.state for n in a.neighbors)
+        new_states.append(a.state + alpha * (avg_neighbor - a.state))
+    for a, ns in zip(agents, new_states):
+        a.state = ns
+
+# 매 매 step 의 모두 의 average 의 converge.
+```
+
+### Reward hacking detection (cybernetic perspective)
+```python
+def detect_reward_hacking(agent_trajectory, true_objective):
+    """매 unintended feedback 의 detect."""
+    declared_reward = sum(t.reward for t in agent_trajectory)
+    actual_progress = measure_against_intent(agent_trajectory, true_objective)
+    
+    if declared_reward > 0.9 * MAX_DECLARED and actual_progress < 0.5:
+        return 'WARN: reward hacking — 매 metric 의 game'
+    return 'OK'
+```
+
+## 🤔 결정 기준
+| 응용 | Approach |
+|---|---|
+| Setpoint regulation | PID |
+| Multi-state control | MPC |
+| Distributed | Consensus protocol |
+| Homeostatic system | Negative feedback |
+| Growth (controlled) | Positive feedback + saturation |
+| Multi-agent | Active inference / consensus |
+| AI alignment | Reward hacking detection |
+
+**기본값**: PID 의 baseline. 매 model-aware = MPC. 매 multi-agent = active inference.
+
+## 🔗 Graph
+- 부모: [[Systems Theory]] · [[Control-Theory]] · [[Multi-agent-System|Multi-Agent-Systems]]
+- 변형: [[Feedback-Loop]] · [[Homeostasis (항상성)|Homeostasis]] · [[Autopoiesis]]
+- 사상가: [[Norbert-Wiener]]
+- 응용: [[PID]] · [[MPC]] · [[Active-Inference]] · [[Bayesian-Brain-Hypothesis]]
+- Adjacent: [[Antifragility]] · [[Artificial-Life]] · [[Biological-Intelligence]] · [[Anarchism]]
+
+## 🤖 LLM 활용
+**언제**: 매 control system. 매 multi-agent. 매 active inference. 매 cybernetic / systems thinking.
+**언제 X**: 매 specific math (control theory 의 textbook). 매 single-step decision.
+
+## ❌ 안티패턴
+- **Open-loop control**: 매 perturbation 의 ignore.
+- **Variety mismatch (Ashby)**: 매 controller 의 weak.
+- **Positive feedback 의 unbounded**: 매 explosion.
+- **Reward hacking 의 ignore**: 매 unintended.
+- **2nd-order observer 의 X**: 매 self-reference 의 limit.
+
+## 🧪 검증 / 중복
+- Verified (Wiener "Cybernetics", Ashby "Introduction to Cybernetics", Friston FEP).
+- 신뢰도 A.
+- Related: [[Bayesian-Brain-Hypothesis]] · [[Antifragility]] · [[Multi-agent-System|Multi-Agent-Systems]] · [[Anarchism]] · [[Computational-Neuroscience-RL]].
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — Wiener + 2nd-order + Ashby + 매 PID / MPC / consensus / active inference code |