---
id: wiki-2026-0508-feedback-loops
title: Feedback Loops
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Feedback Control, Closed Loop, Cybernetic Feedback]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [systems, control-theory, cybernetics, dynamics]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: python
  framework: control-systems
---

# Feedback Loops

## 매 한 줄
> **"매 system output 의 input 의 re-entry — 매 stability 또는 amplification 의 결정"**. 매 1948 Wiener 의 Cybernetics 가 unifying frame. 매 2026 의 RLHF, autoscaling, climate tipping points, social media engagement loop 의 modern instances.

## 매 핵심

### 매 2 polarities
- **Negative (balancing)**: 매 deviation 의 dampen — 매 thermostat, homeostasis, PID controller.
- **Positive (reinforcing)**: 매 deviation 의 amplify — 매 viral growth, asset bubble, ice-albedo feedback, runaway selection.

### 매 5 archetypes (Senge)
- **Limits to growth**: 매 reinforcing + balancing — 매 S-curve.
- **Shifting the burden**: 매 quick fix 의 underlying issue 의 weaken.
- **Tragedy of the commons**: 매 individual reinforcing → collective collapse.
- **Fixes that fail**: 매 short-term fix 의 long-term backfire.
- **Success to the successful**: 매 winner-take-all reinforcing.

### 매 stability concepts
- **Gain**: 매 output/input ratio.
- **Phase margin**: 매 stability buffer (>45° robust).
- **Time delay**: 매 instability driver (Bode-Nyquist).
- **Setpoint vs. error**: 매 target — actual.

### 매 응용
1. PID controller (industrial process).
2. RLHF (LLM 의 preference loop).
3. Autoscaling (Kubernetes HPA, target CPU).
4. Insulin-glucose homeostasis.
5. Market price discovery.

## 💻 패턴

### PID controller
```python
class PID:
    def __init__(self, kp: float, ki: float, kd: float, setpoint: float):
        self.kp, self.ki, self.kd = kp, ki, kd
        self.setpoint = setpoint
        self.integral = 0.0
        self.prev_error = 0.0

    def step(self, measurement: float, dt: float) -> float:
        error = self.setpoint - measurement
        self.integral += error * dt
        derivative = (error - self.prev_error) / dt if dt > 0 else 0
        self.prev_error = error
        return self.kp * error + self.ki * self.integral + self.kd * derivative
```

### Logistic growth (limits-to-growth archetype)
```python
import numpy as np
from scipy.integrate import odeint

def logistic(N, t, r, K):
    return r * N * (1 - N / K)

t = np.linspace(0, 50, 500)
N = odeint(logistic, y0=1, t=t, args=(0.3, 1000))
# Reinforcing (rN) + balancing ((1 - N/K))
```

### Autoscaling reactive loop
```python
def autoscale_step(current_replicas: int, cpu_utilization: float,
                   target: float = 0.7, max_replicas: int = 100) -> int:
    desired = int(current_replicas * cpu_utilization / target)
    return max(1, min(desired, max_replicas))
```

### Reinforcement learning (RLHF reward model loop)
```python
def rlhf_iteration(policy, reward_model, prompts, ppo_optimizer):
    rollouts = [policy.generate(p) for p in prompts]
    rewards = [reward_model.score(p, r) for p, r in zip(prompts, rollouts)]
    advantages = compute_advantages(rewards)
    ppo_optimizer.step(policy, rollouts, advantages)
    # Loop closes: policy → output → reward → policy update
```

### Stability check (root locus)
```python
import numpy as np
from scipy.signal import TransferFunction, bode

# Open-loop transfer function
sys = TransferFunction([1], [1, 2, 3, 1])  # 3rd order
w, mag, phase = bode(sys)
# Phase margin: phase at gain crossover + 180°
```

### Detect runaway positive feedback
```python
def detect_runaway(time_series: list[float], window: int = 10, threshold: float = 1.5) -> bool:
    """Exponential growth detector — log-linear fit slope."""
    import numpy as np
    if len(time_series) < window:
        return False
    y = np.log(np.maximum(time_series[-window:], 1e-9))
    slope = np.polyfit(range(window), y, 1)[0]
    return slope > np.log(threshold) / window
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| Process regulation, setpoint tracking | PID (negative feedback) |
| Growth modeling | logistic / Gompertz (mixed) |
| Cascading failure prevention | rate limiters + circuit breakers |
| Slow process w/ delay | feed-forward + smith predictor |
| ML training | RLHF / GRPO with KL regularization |

**기본값**: 매 negative feedback 의 default for stability. 매 positive feedback 의 explicit guard (rate limit, kill switch).

## 🔗 Graph
- 부모: [[Cybernetics Foundations|Cybernetics]] · [[Control Theory]] · [[Systems_Thinking|Systems Thinking]]
- 응용: [[RLHF]] · [[Homeostasis (항상성)|Homeostasis]]

## 🤖 LLM 활용
**언제**: 매 archetype identification, 매 PID gain initial estimation, 매 system dynamics diagram 의 stock-flow conversion.
**언제 X**: 매 safety-critical control gain tuning — 매 hardware-in-the-loop testing, 매 actual phase margin verification 필수.

## ❌ 안티패턴
- **Ignoring delay**: 매 time-delay 의 PID 의 instability — 매 dead-time compensation 필요.
- **High gain assumption = better tracking**: 매 oscillation, 매 noise amplification.
- **Open-loop control for safety-critical**: 매 disturbance rejection X — 매 closed-loop 필수.
- **Reinforcing loop 의 무방어 deploy**: 매 viral metric 의 optimization — 매 social harm runaway (engagement maximization → polarization).

## 🧪 검증 / 중복
- Verified (Wiener "Cybernetics" 1948, Åström & Murray "Feedback Systems" 2nd ed, Sterman "Business Dynamics" 2000, Senge "Fifth Discipline" rev. ed).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — PID, Senge archetypes, RLHF/autoscaling 추가 |