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+---
+id: wiki-2026-0508-economics-of-information
+title: Economics of Information
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [information economics, asymmetric information, signaling, screening, market for lemons]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.93
+verification_status: applied
+tags: [economics, information, asymmetric, signaling, screening, akerlof, mechanism-design]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: Economics / Game Theory
+  applicable_to: [Mechanism Design, ML Markets, Pricing, Insurance]
+---
+
+# Economics of Information
+
+## 매 한 줄
+> **"매 information 의 asymmetric 가 의 market 의 fail"**. Akerlof 'Market for Lemons' (1970), Spence signaling, Stiglitz screening — 2001 Nobel. 매 modern: 매 platform economics + 매 ML markets + 매 LLM trust.
+
+## 매 핵심
+
+### 매 information asymmetry
+- **Adverse selection**: 매 hidden type (insurance).
+- **Moral hazard**: 매 hidden action.
+- **Signaling** (Spence): 매 informed party 의 reveal.
+- **Screening** (Stiglitz): 매 uninformed party 의 elicit.
+
+### 매 famous
+- **Akerlof Lemons**: 매 used car.
+- **Spence Education**: 매 degree as signal.
+- **Rothschild-Stiglitz Insurance**.
+- **Mechanism Design** (Hurwicz, Maskin, Myerson).
+
+### 매 응용
+1. **Insurance**: 매 deductible (screen).
+2. **Job market**: 매 degree, certification.
+3. **Online review**: 매 reputation.
+4. **E-commerce**: 매 warranty, return.
+5. **Search ad**: 매 quality score.
+6. **Auction**: 매 VCG.
+7. **ML / LLM**: 매 calibration as signal.
+
+### 매 modern AI implication
+- **LLM trust**: 매 source attribution = signal.
+- **Output verification**: 매 buyer of AI = lemon problem.
+- **AI labor market**: 매 model card = certificate.
+- **Data markets**: 매 quality opacity.
+
+## 💻 패턴
+
+### Adverse selection (Lemons model)
+```python
+import numpy as np
+
+def lemons_market(qualities, buyer_willingness_factor=1.5):
+    """매 Akerlof. 매 quality 의 sellers 의 sort."""
+    avg_q = qualities.mean()
+    buyer_p = buyer_willingness_factor * avg_q
+    sellers_in_market = qualities[qualities * 1.0 <= buyer_p]
+    
+    if len(sellers_in_market) < len(qualities):
+        return lemons_market(sellers_in_market, buyer_willingness_factor)
+    return sellers_in_market, buyer_p
+```
+
+### Spence signaling (education)
+```python
+def signaling_equilibrium(types, signal_cost_high, signal_cost_low, wage_high, wage_low):
+    """매 high type 의 separate?"""
+    # 매 high type signal: 매 wage_high - signal_cost_high > wage_low
+    high_signals = wage_high - signal_cost_high > wage_low
+    # 매 low type 의 NOT signal: 매 wage_high - signal_cost_low < wage_low
+    low_doesnt = wage_high - signal_cost_low < wage_low
+    return high_signals and low_doesnt
+```
+
+### Screening (insurance contract)
+```python
+def screen_high_low(premium_high, deductible_high, premium_low, deductible_low,
+                    p_loss_high, p_loss_low, loss):
+    """매 high-risk 매 contract 1, low-risk 매 contract 2 의 prefer?"""
+    # 매 high-risk utility 의 contract 1: -premium - p * deductible
+    u_high_1 = -premium_high - p_loss_high * deductible_high
+    u_high_2 = -premium_low - p_loss_high * deductible_low
+    u_low_1 = -premium_high - p_loss_low * deductible_high
+    u_low_2 = -premium_low - p_loss_low * deductible_low
+    return u_high_1 > u_high_2 and u_low_2 > u_low_1
+```
+
+### Vickrey (second-price) auction
+```python
+def vickrey_auction(bids):
+    """매 truthful — 매 dominant strategy."""
+    sorted_bids = sorted(bids.items(), key=lambda x: -x[1])
+    winner = sorted_bids[0][0]
+    price = sorted_bids[1][1]  # 매 second highest
+    return winner, price
+```
+
+### VCG mechanism (multi-item)
+```python
+def vcg(bids, allocation_fn):
+    """매 generalized truthful auction."""
+    welfare_with = allocation_fn(bids)
+    payments = {}
+    for bidder in bids:
+        bids_without = {k: v for k, v in bids.items() if k != bidder}
+        welfare_without = allocation_fn(bids_without)
+        # 매 externality
+        payments[bidder] = welfare_without - (welfare_with - bids[bidder])
+    return payments
+```
+
+### Reputation system (eBay-style)
+```python
+class Reputation:
+    def __init__(self):
+        self.history = []
+    
+    def add_review(self, score, weight=1):
+        self.history.append((score, weight, datetime.now()))
+    
+    def score(self, decay_days=365):
+        weighted = []
+        for s, w, t in self.history:
+            age = (datetime.now() - t).days
+            decay = 0.5 ** (age / decay_days)
+            weighted.append((s * w * decay, w * decay))
+        if not weighted: return None
+        return sum(s for s, _ in weighted) / sum(w for _, w in weighted)
+```
+
+### Quality score (search ad)
+```python
+def ad_rank(bid, expected_ctr, ad_quality, landing_quality):
+    """매 Google AdWords-style."""
+    return bid * (expected_ctr * ad_quality * landing_quality)
+```
+
+### Moral hazard (deductible)
+```python
+def optimal_deductible(p_loss, loss, risk_aversion, monitoring_cost):
+    """매 trade-off: 매 risk-share vs incentive."""
+    # 매 higher deductible → 매 less moral hazard
+    return min(loss, monitoring_cost / risk_aversion / p_loss)
+```
+
+### Cheap talk (Crawford-Sobel)
+```python
+def cheap_talk_eq(preferences_aligned):
+    """매 sender / receiver 의 align 매 babbling X."""
+    if preferences_aligned > 0.7:
+        return 'full_revelation'
+    if preferences_aligned > 0.3:
+        return 'partial_pooling'
+    return 'babbling_eq'
+```
+
+### LLM as expert with skin in game
+```python
+def llm_with_skin(llm, claim, stakes):
+    """매 hallucination cost 의 internalize."""
+    confidence = llm.estimate_confidence(claim)
+    if confidence * stakes > THRESHOLD:
+        return claim
+    return f"I'm not confident enough — uncertainty {1 - confidence:.2f}"
+```
+
+### Information cascade (herd)
+```python
+def cascade_decision(public_signals, private_signal):
+    """매 Bikhchandani 1992."""
+    public_majority = sum(public_signals) / len(public_signals) > 0.5
+    if abs(sum(public_signals) - len(public_signals) / 2) > 2:
+        return public_majority  # 매 follow herd
+    return private_signal  # 매 own info dominates
+```
+
+### Provenance / certificate (C2PA economics)
+```python
+def value_of_provenance(verified_chain, market_premium=0.1):
+    """매 verified content 의 market premium."""
+    if verified_chain.is_complete():
+        return market_premium
+    return 0
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| Hidden type | Screening menu |
+| Hidden action | Incentive / monitor |
+| Multi-bidder | VCG / Vickrey |
+| Reputation matters | Persistent ID + reviews |
+| Cheap talk | Verifiable claim |
+| AI output trust | Source attribution + cert |
+
+**기본값**: 매 mechanism design 의 incentive-compatible + 매 truthful elicitation + 매 reputation persistence + 매 verifiable signal.
+
+## 🔗 Graph
+- 변형: [[Signaling]]
+- Adjacent: [[Behavioral-Economics]] · [[Information_Theory|Information-Theory]]
+
+## 🤖 LLM 활용
+**언제**: 매 marketplace design. 매 incentive system. 매 AI trust.
+**언제 X**: 매 perfect-info commodity.
+
+## ❌ 안티패턴
+- **Ignore information asymmetry**: 매 lemons collapse.
+- **Untruthful auction (1st price hide info)**: 매 strategic gaming.
+- **No reputation persistence**: 매 short-term cheating.
+- **Cheap talk 의 trust**: 매 babbling.
+- **No skin-in-game for AI**: 매 hallucinate freely.
+
+## 🧪 검증 / 중복
+- Verified (Akerlof 1970, Spence 1973, Stiglitz 1976, Myerson Mechanism Design).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-04-20 | Auto-reinforced |
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — asymmetric + 매 lemons / signaling / VCG / reputation / cheap talk code |