2nd/10_Wiki/Topics/Domain_Programming/Architecture/Systems_Thinking.md

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

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-systems-thinking	Systems Thinking	10_Wiki/Topics	verified	self	Systems Theory Holistic Thinking Feedback Loops	none	A	0.9	applied	systems thinking modeling complexity		2026-05-10	pending	language framework methodology causal-loop

Systems Thinking

매 한 줄

"매 part 의 sum 의 X — 매 interaction 의 emergent behavior". 매 element 의 isolated analysis 의 대신 매 stocks, flows, feedback loops, delays 의 holistic 의 see. Donella Meadows 의 "Thinking in Systems" 의 canonical — 2026 의 software, climate, policy 의 적용.

매 핵심

매 핵심 vocabulary

• Stock: 매 accumulation (inventory, $, customers, tech debt).
• Flow: 매 rate of change (sales/day, hires/month).
• Feedback loop: 매 output 의 input 의 영향.
  • Reinforcing (R): amplifies — 매 viral growth.
  • Balancing (B): stabilizes — 매 thermostat.
• Delay: 매 cause → effect 의 lag — oscillation 의 cause.

매 leverage points (Meadows, ranked)

1. Paradigm (mindset).
2. Goals of system.
3. Self-organization rules.
4. Information flows.
5. Rules / incentives.
6. Negative feedback strength.
7. Positive feedback strength.
8. Material flows / stocks.
9. Numbers / parameters (lowest leverage).

매 archetypes

• Limits to Growth: R + B → S-curve.
• Shifting the Burden: short-term fix 의 weakens long-term solution.
• Tragedy of the Commons: shared resource 의 overuse.
• Fixes That Fail: 매 quick fix 의 root cause 의 worsen.
• Success to the Successful: rich-get-richer.
• Escalation: arms race.

매 응용

1. Software incident analysis (alerts → fatigue → ignore).
2. Tech debt dynamics (speed ↔ debt ↔ slowdown).
3. Org design (incentives → behavior → outcomes).
4. Climate / policy modeling.

💻 패턴

Causal Loop Diagram (CLD) — text format

Tech debt:
  velocity (-) → tech_debt        // less time to fix → debt grows
  tech_debt (-) → velocity        // more debt → slower work
  // R loop: vicious cycle
  
  tech_debt (+) → cleanup_priority
  cleanup_priority (-) → tech_debt
  // B loop: self-correcting (if priority actually given)

Stock-flow — Python (simple SIR)

import numpy as np
import matplotlib.pyplot as plt

def sir(beta=0.3, gamma=0.1, S0=999, I0=1, R0=0, days=160, dt=1):
    S, I, R = [S0], [I0], [R0]
    N = S0 + I0 + R0
    for _ in range(int(days/dt)):
        dS = -beta * S[-1] * I[-1] / N
        dI = beta * S[-1] * I[-1] / N - gamma * I[-1]
        dR = gamma * I[-1]
        S.append(S[-1] + dS*dt)
        I.append(I[-1] + dI*dt)
        R.append(R[-1] + dR*dt)
    return S, I, R

S, I, R = sir()
plt.plot(I, label='Infected')
plt.show()

Limits to Growth — code

def growth_with_limit(K=1000, r=0.1, P0=10, T=200):
    P = [P0]
    for _ in range(T):
        # R loop: r*P (reinforcing)
        # B loop: (1 - P/K) (balancing as P → K)
        dP = r * P[-1] * (1 - P[-1] / K)
        P.append(P[-1] + dP)
    return P
# Logistic curve: explosive then plateau

Behavior over time graph (BoT)

^                          ___
|                       __/
|                    __/         ← logistic (limits to growth)
|                ___/
|            ___/
|        ___/
|     __/
|  __/
| /
+----------------------------> time

Archetype detector — checklist

shifting_the_burden:
  symptoms:
    - "Quick fix repeatedly applied"
    - "Underlying problem persists or worsens"
    - "Capability for fundamental fix atrophies"
  examples:
    - Painkillers vs. cause
    - Hotfixes vs. refactoring
    - Hiring more on-call vs. reducing alerts

Causal vs. correlational

# Bad: regression on snapshot
# Good: simulate stocks/flows over time, validate
#       with both reference modes (BoT) and structure

매 결정 기준

상황	Approach
Persistent recurring issue	Identify archetype
Counter-intuitive outcome	Look for delays / loops
Quick decision	Mental CLD sketch
Forecast / policy test	Stock-flow simulation
Single-cause obvious	매 systems thinking 의 overkill

기본값: 매 CLD sketch 의 first — 매 archetype 의 명확 시 의 simulation.

🔗 Graph

• 부모: Cybernetics Foundations
• 응용: Tech Debt
• Adjacent: Feedback Loops · Mental_Models · Causal Inference

🤖 LLM 활용

언제: archetype identification, CLD draft from narrative, 매 hidden loops 의 surface. 언제 X: quantitative simulation (use Vensim, Stella, SimPy) — LLM 의 numeric simulation 의 unreliable.

❌ 안티패턴

• Linear thinking on systemic problem: 매 single cause 의 search — 매 loop 의 miss.
• Ignore delays: 매 oscillation 의 surprise — 매 delay 의 model.
• Optimize parts in isolation: 매 local optim 의 global degrade.
• Paralysis by complexity: 매 over-modeling — 매 핵심 archetype 의 enough.

🧪 검증 / 중복

• Verified (Donella Meadows "Thinking in Systems"; Senge "5th Discipline"; Sterman "Business Dynamics").
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — stocks/flows, archetypes, CLD, leverage points