2nd/10_Wiki/Topics/Other/Memetics.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-memetics	Memetics	10_Wiki/Topics	verified	self	Meme Theory Cultural Evolution Dawkins Memetics	none	A	0.85	applied	memetics cultural-evolution evolutionary-theory information-theory		2026-05-10	pending	language framework conceptual evolutionary-theory

Memetics

매 한 줄

"매 cultural unit propagates 의 selection-replication-mutation 의 동일 logic". Dawkins 1976 The Selfish Gene 의 마지막 chapter 의 introduced — meme 의 cultural analog 의 gene. 매 modern state (2026) 의 contested 으로 남음 — academic 의 quasi-discipline 으로 fade 했지만 social media virality / LLM training data analysis 에서 매 explanatory framework 으로 revival.

매 핵심

매 정의

• Meme: 매 cultural information unit (idea, behavior, style) 의 host-to-host transmission 가능.
• Replicator: 매 self-copying entity — gene 의 cultural counterpart.
• Memeplex: 매 co-replicating memes 의 cluster (e.g., religion = belief + ritual + identity meme bundle).

매 Darwinian 3-step

• Variation: 매 transmission 의 mutations (mishearing, reinterpretation).
• Selection: 매 attention / memory / social reward 의 differential survival pressure.
• Heredity: 매 high-fidelity copying 의 (vs. paraphrase) winning long-term.

매 응용

1. Internet virality — 매 K-factor (replication rate) 의 explicit modeling.
2. LLM training corpus — 매 dominant memes 의 over-representation 의 model bias.
3. Disinformation analysis — 매 hostile memeplex 의 spread dynamics.

💻 패턴

매 K-factor 의 simulation (epidemiological meme spread)

import numpy as np
import matplotlib.pyplot as plt

def simulate_meme_spread(N=10000, beta=0.3, gamma=0.1, days=60, seed=42):
    """SIR-style meme spread. beta = transmission rate, gamma = forget rate."""
    rng = np.random.default_rng(seed)
    S, I, R = N - 1, 1, 0
    history = []
    for day in range(days):
        new_inf = rng.binomial(S, 1 - np.exp(-beta * I / N))
        new_rec = rng.binomial(I, gamma)
        S -= new_inf
        I += new_inf - new_rec
        R += new_rec
        history.append((day, S, I, R))
    return history

hist = simulate_meme_spread()
peak_day = max(hist, key=lambda x: x[2])
print(f"Peak adoption day {peak_day[0]}, infected={peak_day[2]}")

매 meme fitness 의 measurement (engagement-weighted)

def meme_fitness(impressions: int, shares: int, completion_rate: float, novelty: float) -> float:
    """Composite fitness — higher means stronger replicator."""
    if impressions == 0:
        return 0.0
    share_rate = shares / impressions
    return share_rate * completion_rate * (1 + 0.5 * novelty)

# Example: TikTok clip
print(meme_fitness(1_000_000, 50_000, 0.78, 0.6))  # → ~0.0507

매 memeplex 의 cluster detection (LLM corpus analysis)

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans

def find_memeplexes(documents: list[str], k: int = 8):
    """Identify co-occurring meme clusters in a corpus."""
    vec = TfidfVectorizer(max_features=5000, ngram_range=(1, 3), stop_words="english")
    X = vec.fit_transform(documents)
    km = KMeans(n_clusters=k, random_state=42, n_init=10).fit(X)

    terms = vec.get_feature_names_out()
    for cluster_id in range(k):
        center = km.cluster_centers_[cluster_id]
        top = center.argsort()[-10:][::-1]
        print(f"Memeplex {cluster_id}: {[terms[i] for i in top]}")

# usage: find_memeplexes(reddit_posts, k=12)

매 mutation rate 의 quantification (paraphrase distance)

from sentence_transformers import SentenceTransformer
import numpy as np

model = SentenceTransformer("all-MiniLM-L6-v2")

def transmission_fidelity(original: str, retransmissions: list[str]) -> float:
    """1.0 = perfect copy, 0.0 = unrelated."""
    orig_vec = model.encode(original, normalize_embeddings=True)
    re_vecs = model.encode(retransmissions, normalize_embeddings=True)
    sims = re_vecs @ orig_vec
    return float(np.mean(sims))

매 selfish-meme 의 detector (cost-to-host)

def selfish_meme_score(replication_rate: float, host_wellbeing_delta: float) -> float:
    """High when meme spreads strongly while harming hosts (e.g., conspiracy theories)."""
    return replication_rate / (1 + max(0, host_wellbeing_delta))

# Healthy meme (positive impact, low spread): 0.5
print(selfish_meme_score(replication_rate=0.5, host_wellbeing_delta=0.5))   # 0.33
# Selfish meme (negative impact, high spread): high score
print(selfish_meme_score(replication_rate=2.0, host_wellbeing_delta=-0.8))  # 2.0

매 결정 기준

상황	Approach
매 single viral content 의 spread modeling	SIR with measured beta/gamma
매 long-term cultural change (years+)	Multi-meme co-evolution + selection landscape
매 LLM training bias 분석	Memeplex cluster detection on corpus
매 disinformation campaign 의 detection	Selfish-meme scoring + network propagation graph

기본값: 매 SIR-style modeling 의 first pass — 매 quantitative grip 후 refinement.

🔗 Graph

• 부모: Cultural Evolution
• 변형: Entropy in Information Theory
• Adjacent: Behavioral Economics

🤖 LLM 활용

언제: 매 viral content design / disinformation defense / training corpus 의 bias diagnosis. 언제 X: 매 individual cognition modeling — meme 의 statistical-population concept 의 individual prediction 의 부적합.

❌ 안티패턴

• 매 "meme = funny image": 매 internet vernacular 의 academic concept 의 confuse.
• 매 over-Darwinizing culture: 매 every cultural change 의 selection 의 attribute — many are random drift / institutional choice.
• 매 ignoring transmission medium: 매 medium 의 selection pressure 의 dominant — TV vs Twitter vs TikTok 의 different memeplex 의 favor.

🧪 검증 / 중복

• Verified (Dawkins The Selfish Gene 1976; Blackmore The Meme Machine 1999; Boyd & Richerson Culture and the Evolutionary Process 1985).
• 신뢰도 A (foundational) — but applied predictions 의 신뢰도 B.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — memetics 의 core theory + simulation/cluster patterns 추가