Scaled Swarm Predict

sqlite3 /var/lib/osmoda/swarm/sim_$(date +%s).db "
CREATE TABLE personas (
  id INTEGER PRIMARY KEY,
  name TEXT NOT NULL,
  age INTEGER,
  gender TEXT,
  location TEXT,
  occupation TEXT,
  political_lean TEXT,
  income_bracket TEXT,
  education TEXT,
  interests TEXT,
  personality TEXT,
  initial_position TEXT,
  bio TEXT
);

CREATE TABLE posts (
  id INTEGER PRIMARY KEY AUTOINCREMENT,
  round INTEGER NOT NULL,
  persona_id INTEGER NOT NULL,
  reply_to INTEGER,
  content TEXT NOT NULL,
  position TEXT,
  sentiment REAL,
  likes INTEGER DEFAULT 0,
  retweets INTEGER DEFAULT 0,
  FOREIGN KEY (persona_id) REFERENCES personas(id),
  FOREIGN KEY (reply_to) REFERENCES posts(id)
);

CREATE TABLE position_shifts (
  persona_id INTEGER NOT NULL,
  round INTEGER NOT NULL,
  position_before TEXT,
  position_after TEXT,
  reason TEXT,
  PRIMARY KEY (persona_id, round),
  FOREIGN KEY (persona_id) REFERENCES personas(id)
);

CREATE TABLE analysis (
  round INTEGER PRIMARY KEY,
  summary TEXT,
  top_arguments_for TEXT,
  top_arguments_against TEXT,
  consensus_pct REAL,
  viral_posts TEXT
);
"

Age:        18-24 (15%), 25-34 (20%), 35-44 (18%), 45-54 (16%), 55-64 (15%), 65+ (16%)
Gender:     Male (49%), Female (50%), Non-binary (1%)
Education:  High school (27%), Some college (20%), Bachelor's (22%), Graduate (13%), Trade/other (18%)
Political:  Liberal (25%), Moderate-left (15%), Center (20%), Moderate-right (15%), Conservative (25%)
Income:     <$30k (20%), $30-60k (25%), $60-100k (25%), $100-150k (18%), $150k+ (12%)

Generate [N] realistic social media personas for a [Twitter/Reddit] simulation about "[topic]".

Demographics for this batch: [specify age/gender/political/income distribution for this batch]

For each persona, output JSON:
{
  "name": "realistic full name matching demographics",
  "age": N,
  "gender": "...",
  "location": "city, state",
  "occupation": "specific job title",
  "political_lean": "liberal|moderate-left|center|moderate-right|conservative",
  "income_bracket": "$X-Y",
  "education": "specific degree or level",
  "interests": "3-5 specific interests",
  "personality": "one sentence: how they argue online (aggressive, thoughtful, sarcastic, lurker-who-snaps, etc.)",
  "initial_position": "support|oppose|undecided|unaware",
  "bio": "their Twitter/Reddit bio, 1-2 sentences, authentic voice"
}

Make them feel REAL — not stereotypes. A conservative plumber can care about climate.
A liberal professor can oppose regulation. Surprise me with 20% of them.

sqlite3 $DB_PATH "
SELECT political_lean, COUNT(*), ROUND(COUNT(*)*100.0/(SELECT COUNT(*) FROM personas),1) as pct
FROM personas GROUP BY political_lean ORDER BY pct DESC;
"

You are simulating a [Twitter/Reddit] board. [N] users are reacting to this:

"[TOPIC/ANNOUNCEMENT — paste seed data summary here]"

Here are the users (JSON array of all personas).

Generate a [Twitter post (max 280 chars) / Reddit comment (2-4 sentences)] for EACH persona.
Stay in character — match their age, education, politics, personality, and online voice.

For each persona output:
- persona_id: [N]
- content: "[their post]"
- position: "support|oppose|mixed|joke|question"
- sentiment: [-1.0 to 1.0]

Some personas should:
- Post memes or jokes (15%)
- Ask genuine questions (10%)
- Share personal anecdotes (10%)
- Make strong arguments for/against (40%)
- Just react emotionally (15%)
- Stay silent this round (10% — not everyone posts immediately)

Do NOT make everyone articulate. Some posts should be typos, slang, ALL CAPS, one-word reactions.

Round 1 posts are below. Now generate Round 2: replies and reactions.

[Paste Round 1 posts — all of them, with persona names and IDs]

Rules for Round 2:
- Each persona reads 5-10 random posts from Round 1
- They reply to 1-3 posts (set reply_to = that post's ID)
- They may also make a new top-level post
- 20% of personas shift position based on arguments they read
- Record any position shift in their response

For position shifts, also output:
- position_before
- position_after
- reason: "one sentence — what convinced them"

Viral dynamics: The top 3 posts from Round 1 (by argument strength) get more replies.
Controversial posts get more replies than consensus posts.

Rounds 1-2 are below. Generate Round 3: the debate intensifies.

[Paste all posts from rounds 1-2 with context]

Round 3 dynamics:
- Tribal clusters form: personas start agreeing with similar demographics
- Counter-arguments appear: personas cite specific earlier posts to rebut
- Information spreads: facts from seed data appear in arguments (with varying accuracy)
- 10% of personas get heated (personal attacks, sarcasm, ratio attempts)
- 15% more position shifts
- Lurkers from Round 1 now engage (the silent 10%)
- 5% of personas disengage ("this is pointless" / "touch grass")

Full thread so far (Rounds 1-3). Generate Round 4: positions solidify.

Round 4 dynamics:
- Most personas have firm positions now
- Summary posts appear ("the real issue is...")
- Final position shifts (5-10% max — late deciders)
- Thread fatigue: 30% of personas stop posting
- Quality arguments bubble up, low-effort posts sink
- Some personas change the topic or go meta ("why are we even arguing about this")

24 hours later. The thread has cooled. Generate Round 5.

Round 5 dynamics:
- Only 30-40% of personas return
- Tone shifts: more reflective, less combative
- New information may have emerged (you can introduce one new fact)
- "I thought about it and..." posts (final position shifts)
- Someone writes a long "thread" summarizing both sides

BREAKING: [New information related to the topic]

How do personas react? Generate Round 6.
- 60% of personas react to the new info
- Position shifts may be dramatic
- Previous arguments get re-evaluated

# Final position distribution
sqlite3 $DB_PATH "
SELECT position, COUNT(*) as count,
  ROUND(COUNT(*)*100.0/(SELECT COUNT(*) FROM posts WHERE round=(SELECT MAX(round) FROM posts)),1) as pct
FROM posts WHERE round = (SELECT MAX(round) FROM posts)
GROUP BY position ORDER BY count DESC;
"

# Position shifts over time
sqlite3 $DB_PATH "
SELECT round, position_after, COUNT(*) as shifts
FROM position_shifts
GROUP BY round, position_after
ORDER BY round, shifts DESC;
"

# Most influential posts (most replies received)
sqlite3 $DB_PATH "
SELECT p.id, per.name, p.content, COUNT(r.id) as reply_count
FROM posts p
JOIN personas per ON p.persona_id = per.id
LEFT JOIN posts r ON r.reply_to = p.id
GROUP BY p.id
ORDER BY reply_count DESC
LIMIT 10;
"

# Sentiment by demographic
sqlite3 $DB_PATH "
SELECT per.political_lean, ROUND(AVG(p.sentiment),2) as avg_sentiment
FROM posts p JOIN personas per ON p.persona_id = per.id
WHERE p.round = (SELECT MAX(round) FROM posts)
GROUP BY per.political_lean;
"

# Age group sentiment
sqlite3 $DB_PATH "
SELECT
  CASE WHEN per.age < 25 THEN '18-24'
       WHEN per.age < 35 THEN '25-34'
       WHEN per.age < 45 THEN '35-44'
       WHEN per.age < 55 THEN '45-54'
       WHEN per.age < 65 THEN '55-64'
       ELSE '65+' END as age_group,
  ROUND(AVG(p.sentiment),2) as avg_sentiment,
  COUNT(DISTINCT per.id) as persona_count
FROM posts p JOIN personas per ON p.persona_id = per.id
WHERE p.round = (SELECT MAX(round) FROM posts)
GROUP BY age_group ORDER BY age_group;
"

Read the full simulation (all rounds, all posts, all position shifts).

Produce this analysis:

1. PREDICTION: What does this simulated population predict/prefer? (1 sentence)
2. CONFIDENCE: How strong is the consensus? (percentage + explanation)
3. FAULT LINES: Where does the population split? (demographics, not just opinion)
4. VIRAL ARGUMENTS: The 3 arguments that changed the most minds (quote them)
5. SURPRISING FINDINGS: What positions appeared that you didn't expect?
6. DEMOGRAPHIC BREAKDOWN: Support/oppose by age, gender, political lean, income
7. SIMULATION HEALTH: Did the personas feel real? Were any rounds shallow?

file_write({
  path: "/var/lib/osmoda/swarm/report_[timestamp].md",
  content: "[full report markdown]"
})

# Scaled Swarm Prediction Report

## Topic
[What was simulated]

## Population
- Size: [N] personas
- Platform: [Twitter/Reddit]
- Rounds: [N]
- Demographic match: [% accuracy to target distribution]

## Prediction
[One clear sentence: what the simulated population predicts/prefers]

## Confidence: [X%]
[Why this confidence level — based on consensus strength, not gut feeling]

## Position Distribution (Final Round)
| Position | Count | % |
|----------|-------|---|
| Support  | N     | X |
| Oppose   | N     | X |
| Mixed    | N     | X |
| Other    | N     | X |

## Demographic Breakdown
[Table: position by age group, political lean, gender, income]

## Key Fault Lines
1. [Demographic split #1 — e.g., "Under-35s support 3:1, over-55s oppose 2:1"]
2. [Demographic split #2]

## Most Influential Arguments
1. "[Quote]" — by [persona name/type], shifted [N] positions
2. "[Quote]"
3. "[Quote]"

## Surprising Findings
- [Thing that broke expectations]

## Position Shifts Over Time
| Round | Support→Oppose | Oppose→Support | Newly Engaged |
|-------|---------------|----------------|---------------|
| 1     | -             | -              | N             |
| 2     | N             | N              | N             |
| ...   |               |                |               |

## Simulation Health
- Persona authenticity: [HIGH/MED/LOW — did they feel real?]
- Debate quality: [Did arguments evolve or repeat?]
- Known bias: [Single model role-playing all personas — positions may cluster]

## Raw Data
Database: [path to SQLite file]

teach_knowledge_create({
  title: "Swarm prediction: [topic]",
  category: "prediction",
  content: "[report summary + prediction + confidence]",
  tags: ["scaled-swarm-predict", "[topic-tag]", "[platform]"]
})