---
id: wiki-2026-0508-information-society
title: Information Society
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Post-Industrial Society, Network Society, Knowledge Economy]
duplicate_of: none
source_trust_level: A
confidence_score: 0.85
verification_status: applied
tags: [society, sociology, internet, economy, policy]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: na
  framework: na
---

# Information Society

## 매 한 줄
> **"매 information 의 production · distribution · consumption 의 dominant economic activity 의 society"**. 매 Bell (1973) 의 post-industrial 의 prediction 의 Castells (1996) 의 network society 의 elaboration 의 2026 년 의 LLM 의 cognitive labor 의 partial automation 의 phase 의 entry. 매 attention economy + algorithmic curation + AI 의 mediation 의 defining traits.

## 매 핵심

### 매 phase
1. **Industrial (1800-1970)**: 매 goods + capital.
2. **Post-industrial (1970-2000)**: 매 service + knowledge worker.
3. **Network society (2000-2020)**: 매 internet, platform, social media.
4. **AI-mediated (2020-)**: 매 algorithmic curation + LLM 의 cognitive labor automation.

### 매 핵심 dynamics
- **Attention as scarce resource** (Simon 1971).
- **Network effects** — value ∝ users² (Metcalfe).
- **Power-law distribution** — winner-take-most (rich-get-richer).
- **Surveillance capitalism** (Zuboff 2019) — behavioral data 의 commodification.

### 매 응용 / 영향
1. Platform economy (Uber, Airbnb).
2. Filter bubble + algorithmic polarization.
3. Digital divide (access inequality).
4. AI-driven labor displacement (knowledge work).
5. Misinformation / generative content flood.

## 💻 패턴

### Network effect simulation
```python
import numpy as np

def network_value(n_users, type='metcalfe'):
    """Value of a network as users grow."""
    if type == 'sarnoff': return n_users          # broadcast
    if type == 'metcalfe': return n_users ** 2    # peer-to-peer
    if type == 'reed': return 2 ** n_users        # group-forming
    raise ValueError(type)

# Implication: marginal user adds disproportionate value
# → winner-take-most platform dynamics
```

### Power-law follower distribution
```python
# Most social platforms: Pareto / Zipf distribution
import numpy as np
import matplotlib.pyplot as plt

n_users = 1_000_000
followers = np.random.zipf(a=1.5, size=n_users)
# top 1% holds ~50%+ of total reach
top_1pct = np.sort(followers)[-n_users // 100:].sum() / followers.sum()
print(f"Top 1% share: {top_1pct:.1%}")
```

### Filter-bubble simulator (echo chamber)
```python
def update_belief(belief, exposed_content, alpha=0.1):
    # users see content aligned with their belief (algo-curated)
    aligned = [c for c in exposed_content if abs(c - belief) < 0.3]
    if aligned:
        belief += alpha * (np.mean(aligned) - belief)
    return belief

# Over many iterations → polarization (variance ↑, mean clusters)
```

### Attention-economy revenue model
```python
def ad_revenue(daus, sessions_per_day, ads_per_session, cpm):
    impressions = daus * sessions_per_day * ads_per_session
    return impressions / 1000 * cpm

# Engagement-maximization → outrage / novelty → societal externalities
```

### Digital-divide index
```python
def digital_divide_score(country):
    return 0.4 * country.broadband_penetration + \
           0.3 * country.literacy_rate + \
           0.2 * country.smartphone_penetration + \
           0.1 * country.ai_tool_access
```

### LLM-mediated labor share (2026)
```python
# Productivity uplift studies (Brynjolfsson 2024, etc.)
def cognitive_task_time_with_llm(baseline_hours, task_type):
    uplift = {
        'writing': 0.40, 'coding': 0.55, 'research': 0.30,
        'creative_strategy': 0.20, 'manual': 0.0
    }
    return baseline_hours * (1 - uplift.get(task_type, 0))
```

## 매 결정 기준

| 상황 | Lens |
|---|---|
| Platform design | Network effects + power-law dynamics |
| Content policy | Attention economy externalities |
| Public policy | Digital divide + labor displacement |
| Org strategy | Knowledge worker + AI augmentation |
| Civic discourse | Filter bubble + misinformation |

**기본값**: 매 multi-lens — 매 single theory 의 over-generalize 의 risk.

## 🔗 Graph
- 변형: [[Network Society]]

## 🤖 LLM 활용
**언제**: 매 frame analysis, multi-perspective synthesis. 매 tech-policy intersection 의 explanation.
**언제 X**: 매 country-specific 의 latest stat 은 fact-check. 매 LLM 의 stale 의 risk.

## ❌ 안티패턴
- **Tech-determinist 의 simplification**: 매 society shapes tech 의 too. 매 reciprocal.
- **Single-metric (GDP, DAU) 의 over-reliance**: 매 well-being externality 의 miss.
- **AI = neutral 의 assumption**: 매 X. 매 training data + deployment context 의 bias 의 carry.

## 🧪 검증 / 중복
- Verified (Bell 1973, Castells 1996, Zuboff 2019, Brynjolfsson 2024).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — network society + AI-mediated phase synthesis |
