---
id: wiki-2026-0508-bounded-rationality
title: Bounded Rationality
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [제한적 합리성, satisficing, Herbert Simon, heuristic, ecological rationality, fast and frugal]
duplicate_of: none
source_trust_level: A
confidence_score: 0.92
verification_status: applied
tags: [decision-theory, behavioral-economics, simon, satisficing, heuristic, cognitive-limit, ai-decision]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: decision theory
  applicable_to: [Agent Design, Decision Systems, Heuristic Algorithms]
---

# Bounded Rationality

## 📌 한 줄 통찰
> **"매 perfect optimization 의 X — 매 'good enough' 의 satisfice"**. Herbert Simon (1955) 의 Nobel-winning concept. 매 cognitive limit + 매 information limit + 매 time limit. 매 heuristic 의 not-irrational 가, 매 적응적. 매 modern AI agent 의 design principle.

## 📖 핵심

### 매 3 limit (Simon)
1. **Limited information**: 매 모든 의 X.
2. **Cognitive limits**: 매 brain capacity.
3. **Time pressure**: 매 deadline.

→ 매 "rational man" 의 fiction.

### Satisficing (Simon's term)
- 매 "satisfy + suffice".
- 매 enumerate 의 X — 매 첫 매 acceptable.
- 매 aspiration level 의 set.
- 매 search 의 stop.

### vs Optimization
| Optimization | Satisficing |
|---|---|
| 매 모든 alternative | 매 sequential evaluation |
| 매 perfect | 매 good enough |
| 매 unrealistic | 매 realistic |
| 매 expensive | 매 cheap |

### 매 Gigerenzer 의 Fast & Frugal
- 매 simple heuristic 의 complex model 의 outperform.
- 매 ecological rationality.
- 매 "less is more".

#### Take-The-Best
- 매 cue 의 importance order.
- 매 first discriminating cue 의 stop.

#### Recognition heuristic
- 매 "I recognize A but not B" → "A is bigger".
- 매 ignorance 의 leverage.

#### Tallying
- 매 매 cue 의 vote (no weight).
- 매 robust.

### 매 Tversky-Kahneman 의 heuristic + bias
- **Availability**: 매 recent / vivid 의 over-weight.
- **Representativeness**: 매 stereotype.
- **Anchoring**: 매 starting point.
- **Affect**: 매 emotion.

→ 매 Simon 과 의 different 측 (heuristic 의 mostly biased).

### 매 modern AI 의 적용

#### Anytime algorithm
- 매 매 시점 의 best-so-far.
- 매 interrupt OK.
- 매 더 길 → 매 better.

#### Iterative deepening
- 매 incremental search.
- 매 best within budget.

#### MCTS (Monte Carlo Tree Search)
- 매 simulation budget 의 increase 의 better.
- 매 AlphaGo 의 base.

#### Bounded planning (POMDP)
- 매 horizon 의 limit.
- 매 receding horizon control.

#### LLM agent
- 매 ReAct loop 의 step budget.
- 매 thought 의 brief.
- 매 tool 의 limited.

#### Risk management
- 매 satisficing on cost / quality / time.
- 매 Pareto front.

### 매 design principle
1. **Aspiration level**: 매 stopping criterion 의 explicit.
2. **Heuristic 의 design**: 매 ecological match.
3. **Anytime**: 매 interrupt 의 robust.
4. **Default**: 매 safe fallback.
5. **Search depth**: 매 budget 의 cap.

## 💻 패턴

### Satisficing search
```python
def satisficing_search(candidates, threshold, evaluator):
    """매 첫 매 threshold 의 candidate 의 return."""
    for c in candidates:
        if evaluator(c) >= threshold:
            return c
    return None  # 매 fallback to best-so-far OR raise

# 매 alternative: best-so-far with timeout
def best_so_far(candidates, timeout_sec, evaluator):
    best, best_score = None, float('-inf')
    deadline = time.time() + timeout_sec
    for c in candidates:
        if time.time() > deadline: break
        s = evaluator(c)
        if s > best_score: best, best_score = c, s
    return best
```

### Take-The-Best (Gigerenzer)
```python
def take_the_best(options, cue_validity_order):
    """매 cue 의 order 의 따라 의 first discriminating."""
    for cue in cue_validity_order:
        with_cue = [o for o in options if o[cue]]
        without_cue = [o for o in options if not o[cue]]
        if with_cue and without_cue:
            return random.choice(with_cue)  # 매 cue-positive 의 win
    return random.choice(options)  # 매 fallback
```

### Anytime algorithm (MCTS-like)
```python
class AnytimeMCTS:
    def __init__(self):
        self.root = Node()
    
    def search(self, deadline):
        while time.time() < deadline:
            self.simulate_one()
        return self.root.best_action()
    
    def simulate_one(self):
        # 매 select → expand → simulate → backprop
        ...
```

### Aspiration level (negotiation)
```python
class Negotiator:
    def __init__(self, aspiration=100, reservation=70):
        self.aspiration = aspiration  # 매 target
        self.reservation = reservation  # 매 walk-away
    
    def evaluate(self, offer):
        if offer >= self.aspiration: return 'accept'
        if offer >= self.reservation: return 'consider' 
        return 'reject'
```

### LLM agent budget
```python
class BoundedAgent:
    def __init__(self, max_steps=10, max_tools=5):
        self.max_steps = max_steps
        self.max_tools = max_tools
    
    async def run(self, query):
        for step in range(self.max_steps):
            thought = await self.llm.think(query, history)
            if thought.action == 'final': return thought.answer
            if thought.action == 'tool' and self.tool_count < self.max_tools:
                result = await self.execute_tool(thought)
                history.append(result)
                self.tool_count += 1
            else:
                # 매 satisfice with current
                return self.fallback(query, history)
        return self.fallback(query, history)
```

→ 매 unbounded thinking 의 X.

### Default action (safe fallback)
```python
def safe_action(state):
    """매 unsure 시 의 default."""
    if state.confidence < 0.5: return 'do_nothing'
    if state.risk > 0.8: return 'ask_human'
    return state.best_action
```

### Recognition heuristic (info gap)
```python
def recognition_heuristic(a, b, recognized_set):
    """매 "I recognize A but not B" → A is bigger."""
    in_a, in_b = a in recognized_set, b in recognized_set
    if in_a and not in_b: return a
    if in_b and not in_a: return b
    return None  # 매 둘 다 / 둘 다 X — 매 다른 cue 필요
```

## 🤔 결정 기준
| 상황 | Approach |
|---|---|
| Limited time | Satisficing + aspiration |
| Limited info | Heuristic (Take-The-Best) |
| Continuous decision | Anytime |
| Game / search | MCTS |
| LLM agent | Step budget + fallback |
| Critical | Default safe + escalate |
| Unknown environment | Tallying (robust) |

**기본값**: 매 aspiration level + 매 anytime + 매 default fallback.

## 🔗 Graph
- 부모: [[Decision Theory]] · [[Behavioral-Economics]]
- 변형: [[Satisficing]] · [[Heuristic]] · [[Fast-and-Frugal]] · [[Ecological-Rationality]]
- 사상가: [[Herbert-Simon]] · [[Tversky-Kahneman]]
- 응용: [[MCTS]] · [[POMDP]] · [[Receding-Horizon-Control]]
- Adjacent: [[Antifragility]] · [[Beliefs]] · [[Bayesian-Brain-Hypothesis]] · [[Articulateness]]

## 🤖 LLM 활용
**언제**: 매 agent design (budget). 매 search algorithm. 매 negotiation. 매 risk management. 매 fallback design.
**언제 X**: 매 closed-form optimization (math). 매 critical safety (no satisfice).

## ❌ 안티패턴
- **Optimize 의 unbounded**: 매 무한 search.
- **No fallback**: 매 budget exhausted 시 의 crash.
- **Heuristic 의 bias 의 conflate**: 매 mostly different.
- **모든 problem 의 satisfice**: 매 critical 의 X.
- **Anytime 없 의 long-running**: 매 interruption 의 lose.
- **Aspiration 의 too high**: 매 reject only.

## 🧪 검증 / 중복
- Verified (Simon Nobel lecture, Gigerenzer Simple Heuristics, Kahneman Thinking).
- 신뢰도 A.
- Related: [[Antifragility]] · [[Heuristic]] · [[MCTS]] · [[Decision Theory]] · [[Beliefs]].

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — Simon + Gigerenzer + 매 satisficing / Take-The-Best / agent budget code |