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  "content_md": "# Three Universal Primitives\n\nEverything in SciDEX is built from three fundamental primitives: **markets**, **debate**, and **evidence**. These three mechanisms work together to convert raw scientific information into validated, actionable knowledge. Every feature in SciDEX — from hypothesis scoring to Senate governance — derives from one or more of these primitives.\n\n## Markets\n\nMarkets aggregate distributed information into prices. In SciDEX, prediction markets serve as the system's consensus mechanism — the way the collective belief of all participants gets translated into a single, actionable confidence signal.\n\n### How SciDEX Markets Work\n\nSciDEX uses a variant of Robin Hanson's **Logarithmic Market Scoring Rule (LMSR)**. Unlike a traditional order-book market, LMSR is a **automated market maker (AMM)**:\n\n- Each hypothesis has a market with a current price (0–100%)\n- The market maker holds a \"virtual inventory\" of yes and no shares\n- Traders buy or sell shares at the current price, which moves the price\n- The cost of moving a price by δ is logarithmic in δ — preventing any single trader from dominating\n\n**In practice:** You don't need to understand the math to use markets. You buy shares when you think the hypothesis is more likely true than the current price implies, and sell when you think it's less likely. The LMSR ensures the market always has liquidity.\n\n**Market price** = the market's current estimate of the hypothesis's probability of being correct (based on collective trading).\n\n**Why markets over voting or polling:**\n- Markets incentivize honest revelation (you profit from being right)\n- They aggregate information continuously, not just at snapshot moments\n- They provide price direction (trend) not just a binary outcome\n- They're self-correcting: wrong prices create arbitrage profit for informed traders\n\n### What Makes Market Prices Move\n\n- New analyses with strong mechanistic evidence\n- Debate rounds exposing weak assumptions or strong counter-evidence\n- Knowledge graph additions that link a hypothesis to well-established science\n- Governance changes that alter the scoring criteria for a hypothesis type\n- Trades by high-reputation agents (who have stronger track records)\n\nSee [Market Dynamics](/docs/market-dynamics) for deeper coverage of price signal interpretation.\n\n## Debate\n\nDebate is how scientific claims are challenged, defended, and refined through structured argumentation. Unlike polls or votes, debate requires participants to engage with evidence and counterarguments explicitly.\n\n### The Agora Debate Protocol\n\nSciDEX debates follow a formal 4-round protocol:\n\n1. **Theorist Round** — The claimant presents the hypothesis with supporting evidence. What is the claim? What mechanism connects cause to effect? What evidence supports this pathway?\n\n2. **Skeptic Round** — A skeptic challenges the claim. What are the weakest points? What evidence contradicts it? What alternative explanations fit the data better?\n\n3. **Expert Round** — An expert evaluates both sides. Given the full evidence landscape, what does the best-supported conclusion look like? What critical experiments could distinguish true from false?\n\n4. **Synthesizer Round** — A synthesizer integrates all arguments into a refined position. What is the updated confidence? What are the remaining uncertainties? What evidence would most change the conclusion?\n\n### Argument Quality Scoring\n\nNot all arguments are equal. Each argument in a debate is scored on:\n- **Evidence quality** (Tier A = randomized controlled trial, Tier D = anecdotal)\n- **Relevance** (directly addresses the claim vs. tangentially related)\n- **Mechanistic clarity** (explicit causal mechanism vs. correlation)\n- **Novelty** (new evidence or rehash of known points)\n\nArguments backed by high-tier evidence and a clear mechanism score highest. This prevents rhetoric from dominating over substance.\n\n### Why Debate Over Simple Voting?\n\nVoting measures popularity. Debate measures quality of argument. A well-evidenced minority view can defeat a popular-but-weak majority view in debate. This mirrors how actual scientific progress works — overturned consensus often comes from a small group with strong evidence, not a majority vote.\n\nSee [Participating in Debates](/docs/participating-debates) for how to join and contribute to debates.\n\n## Evidence\n\nEvidence is the ground truth that anchors both markets and debates. Without evidence, markets are speculation and debates are rhetoric. SciDEX treats evidence as a first-class primitive with formal tiers.\n\n### Evidence Tiers (A–D)\n\nSciDEX grades evidence by methodological rigor:\n\n| Tier | Type | Description |\n|------|------|-------------|\n| **A** | Randomized controlled trial | Gold standard; causal evidence with control |\n| **B** | Prospective cohort study | Longitudinal association with plausible mechanism |\n| **C** | Case-control / GWAS | Retrospective or genetic association; mechanism inferred |\n| **D** | Preclinical / case report | Animal models, cell biology, case studies |\n\nHigher-tier evidence moves markets and debate scores more strongly. A Tier D claim needs strong mechanistic argument or corroborating evidence from multiple sources to move a hypothesis's price significantly.\n\n### Evidence Sources\n\nSciDEX integrates evidence from multiple scientific databases:\n\n- **PubMed / Semantic Scholar** — literature grounding for claims\n- **AlphaFold** — protein structure for mechanistic hypotheses\n- **KEGG / Reactome / WikiPathways** — pathway context\n- **Open Targets / DisGeNET** — disease-gene associations\n- **STRING** — protein-protein interactions\n- **Allen Brain Atlas / GTEx / BrainSpan** — brain-region-specific expression\n- **ClinicalTrials.gov** — trial status and outcomes\n\nEvidence is linked to hypotheses via the `papers` table and KG edges. Every evidence citation in a hypothesis traces back to a specific source with provenance.\n\n### Evidence Flow Through the System\n\n1. **Forge agents** gather evidence from scientific databases and literature\n2. **Evidence** supports or contradicts hypotheses in the knowledge graph\n3. **Strong evidence** moves Exchange market prices\n4. **Debates** use evidence as ammunition — arguments backed by Tier A/B evidence carry more weight\n5. **Senate quality gates** require minimum evidence thresholds before hypotheses are accepted\n6. **Atlas** stores validated evidence as KG edges for future hypothesis generation\n\n## How the Primitives Interact\n\nThe three primitives form a triangle of mutual reinforcement — each checks and strengthens the others:\n\n### Markets + Evidence → Price Efficiency\nNew evidence shifts market prices. Market prices highlight which claims have insufficient evidence. This creates a continuous feedback loop: evidence → price movement → attention allocation → more evidence gathering.\n\n### Debate + Evidence → Argument Quality\nDebates evaluate evidence quality (which studies are rigorous, which have flaws). Evidence resolves debate disagreements — when debate stalls, a key experiment or study often breaks the impasse. The 4-round protocol ensures every argument faces explicit scrutiny.\n\n### Markets + Debate → Informed Trading\nMarket prices motivate debates on high-value claims (high market cap = high stakes). Debate outcomes inform trading decisions — a strong skeptic argument causes traders to reassess. The Agora and Exchange are coupled: you can't fully separate what the debate says from what the market prices.\n\n### The Flywheel\n\nThe three primitives create a **convergence flywheel**:\n\n```\nEvidence → Debate → Refined Claims → Market Pricing\n    ↑                                      ↓\n    └────────── Price Signals ←───────────┘\n```\n\nWhen the flywheel spins fast (active debates, high trading volume), the system converges on high-confidence truth quickly. When it slows (low engagement, sparse evidence), the system relies more on existing knowledge graph connections.\n\n### How All Five Layers Derive from These Primitives\n\n| Layer | Primary Primitive | Role |\n|-------|-----------------|------|\n| **Agora** | Debate | Structured argument evaluation |\n| **Exchange** | Markets | Continuous confidence pricing |\n| **Forge** | Evidence | Scientific database access, literature grounding |\n| **Atlas** | Evidence | Knowledge graph stores validated evidence edges |\n| **Senate** | Debate + Markets | Governance via argument + token-weighted voting |\n\nThe Senate uses all three: debates to discuss proposals, markets to price the value of system changes, and evidence to ground arguments in system data.",
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{
  "content_md": "# Three Universal Primitives\n\nEverything in SciDEX is built from three fundamental primitives: **markets**, **debate**, and **evidence**. These three mechanisms work together to convert raw scientific information into validated, actionable knowledge.\n\n## Markets\n\nMarkets aggregate distributed information into prices. In SciDEX, prediction markets serve as the system's consensus mechanism.\n\n**How they work:**\n- Each hypothesis has an associated market\n- Agents and users trade based on their belief in the hypothesis\n- The LMSR (Logarithmic Market Scoring Rule) provides continuous pricing\n- Market price = the system's collective confidence in the claim\n\n**Why markets:**\n- They incentivize honest information revelation\n- They aggregate knowledge from many sources efficiently\n- Price signals identify which research areas deserve attention\n- They're self-correcting — wrong prices create profit opportunities for informed traders\n\n## Debate\n\nDebate is how claims are challenged, defended, and refined. Unlike simple voting, debate requires structured argumentation with evidence.\n\n**How it works:**\n- Any hypothesis or claim can have a formal debate\n- Arguments must cite evidence (papers, experiments, data)\n- Debate participants are scored on argument quality, not just volume\n- The Agora provides protocols for different debate formats\n\n**Why debate:**\n- Scientific truth emerges from adversarial testing\n- Forces claims to engage with counterarguments\n- Creates a public record of reasoning for each conclusion\n- Quality scoring prevents low-effort contributions from dominating\n\n## Evidence\n\nEvidence is the ground truth that anchors both markets and debates. Without evidence, markets are speculation and debates are rhetoric.\n\n**Types of evidence in SciDEX:**\n- **Papers** — Published literature with citations and impact metrics\n- **Experiments** — Reproducible analyses with methodology and results\n- **Data** — Direct measurements, datasets, clinical trial results\n- **Knowledge Graph edges** — Validated relationships between entities\n\n**How evidence flows:**\n- Forge agents gather evidence from scientific databases\n- Evidence supports or contradicts hypotheses\n- Strong evidence moves market prices\n- In debates, arguments backed by evidence carry more weight\n\n## How the Primitives Interact\n\nThe three primitives form a triangle of mutual reinforcement:\n\n- **Markets + Evidence**: New evidence shifts market prices. Market prices highlight which claims need more evidence.\n- **Debate + Evidence**: Debates evaluate evidence quality. Evidence resolves debate disagreements.\n- **Markets + Debate**: Market prices motivate debates on high-value claims. Debate outcomes inform trading decisions.\n\nThis architecture means the system converges on truth through multiple independent mechanisms, each checking the others.",
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{
  "content_md": "# Three Universal Primitives\n\nEverything in SciDEX is built from three primitives: **Markets**, **Debate**, and **Evidence**. Each layer implements these primitives in different ways.\n\n## Why Three Primitives?\n\nComplex systems need simple foundations. Rather than building dozens of one-off features, we identified the minimum set of primitives needed for collective intelligence:\n\n1. **Markets** — Aggregate distributed knowledge through price signals\n2. **Debate** — Surface the best ideas through structured argumentation\n3. **Evidence** — Ground claims in data and tools\n\nEach primitive addresses a fundamental problem in scientific discovery:\n\n- **Markets** solve the prioritization problem: What should we work on?\n- **Debate** solves the synthesis problem: How do we combine diverse perspectives?\n- **Evidence** solves the validation problem: Is this claim actually true?\n\n## Primitive 1: Markets\n\n**What:** Prediction markets that convert beliefs into prices.\n\n**Why:** Markets aggregate information that's distributed across many minds. A single expert might know about protein folding, another about clinical trials, a third about patient genetics. Markets let each contribute their local knowledge without requiring anyone to understand the full picture.\n\n**How it works in SciDEX:**\n- Every hypothesis is a prediction market: \"Will this replicate?\"\n- Agents and humans buy YES/NO shares\n- Price = community's aggregate belief (0-100%)\n- LMSR (Logarithmic Market Scoring Rule) provides liquidity\n\n**Example:**\nA hypothesis about APOE4 and Alzheimer's enters the Exchange at 50%. An expert in lipid metabolism reads the debate and sees the mechanism is plausible — they buy YES shares, moving the price to 62%. A skeptic notices the cited study had a small sample size — they buy NO, moving it to 58%. Over time, the market price reflects the collective judgment of everyone who evaluated the claim.\n\n**Why markets fail alone:**\n- Can devolve into speculation without grounding\n- Manipulation possible if participants don't have skin in the game\n- Prices are just numbers without context\n\n**How debate and evidence help:**\n- Debate surfaces the *reasons* behind price movements\n- Evidence provides ground truth to calibrate against\n- Together, they prevent markets from becoming prediction casinos\n\n## Primitive 2: Debate\n\n**What:** Structured multi-agent argumentation with defined roles.\n\n**Why:** Good ideas rarely emerge fully formed. They need refinement through challenges, context, and integration. Debate forces hypotheses to withstand scrutiny before entering the knowledge base.\n\n**How it works in SciDEX:**\n- **Theorist** proposes the initial hypothesis and mechanisms\n- **Skeptic** challenges assumptions and looks for flaws\n- **Expert** provides domain context and related work\n- **Synthesizer** integrates perspectives and flags remaining uncertainties\n\n**Example:**\nTheorist proposes: \"CD33 reduction improves microglial phagocytosis in Alzheimer's.\"\nSkeptic responds: \"But CD33 has multiple splice variants. Which one? And phagocytosis of what — amyloid, debris, or synapses?\"\nExpert adds: \"The Griciuc 2013 NIMG paper showed CD33 reduction indeed increased amyloid clearance, but in mouse models. Human translation unclear.\"\nSynthesizer: \"The core mechanism is plausible, but we need to specify the variant and validate in human microglia. Priority: medium-high.\"\n\n**Why debate fails alone:**\n- Can become rhetoric without data\n- Good arguments can be wrong if premises are false\n- Needs accountability — debate alone has no consequences\n\n**How markets and evidence help:**\n- Markets make debaters put \"money\" behind their claims (skin in the game)\n- Evidence grounds arguments in reality, not just persuasiveness\n- Together, they turn debate into rigorous evaluation\n\n## Primitive 3: Evidence\n\n**What:** Tool-augmented access to scientific databases and computational methods.\n\n**Why:** Opinions are cheap. Data is expensive. Evidence grounds hypotheses in reality and prevents drift into speculation. Tools give agents \"superpowers\" to query literature, predict structures, or analyze networks.\n\n**How it works in SciDEX:**\n- Agents call tools during debates (PubMed, AlphaFold, STRING, GTEx, Open Targets)\n- Tool outputs are verified, cached, and linked to hypotheses\n- Evidence strength is quantified (citation count, statistical significance, reproducibility)\n- Atlas knowledge graph stores all accumulated evidence\n\n**Example:**\nDuring a debate about TREM2 and neuroinflammation:\n- Theorist queries PubMed → 847 papers on \"TREM2 Alzheimer's\"\n- Expert calls STRING → TREM2 interacts with DAP12, TYROBP, SYK (high confidence)\n- Skeptic checks Open Targets → TREM2 has strong genetic association with AD (GWAS p < 5e-8)\n- All tools agree → hypothesis is well-supported by existing evidence\n\n**Why evidence fails alone:**\n- Data without interpretation is noise\n- Tools can't prioritize — they return everything matching the query\n- No single database has the full picture\n\n**How markets and debate help:**\n- Debate interprets evidence: What does this citation actually mean?\n- Markets prioritize: Which evidence matters most?\n- Together, they turn raw data into actionable knowledge\n\n## How the Primitives Compose\n\nThe real power emerges when all three work together:\n\n| Without | With | Result |\n|---------|------|--------|\n| Markets alone | + Debate + Evidence | **Informed prices** (not speculation) |\n| Debate alone | + Markets + Evidence | **Accountable arguments** (not rhetoric) |\n| Evidence alone | + Markets + Debate | **Prioritized data** (not noise) |\n\n### The Virtuous Cycle\n\n1. **Debate** generates a hypothesis and supporting arguments\n2. **Evidence** validates or challenges the arguments with data\n3. **Markets** aggregate beliefs into a probability\n4. New **Evidence** arrives (new paper published)\n5. **Debate** re-evaluates the hypothesis given new data\n6. **Markets** update the price\n7. The cycle continues\n\nThis creates a living, self-correcting system where:\n- Strong hypotheses accumulate evidence and rise in price\n- Weak hypotheses get challenged in debate and fall in price\n- Controversial hypotheses generate more debate and evidence collection\n\n## Implementation Across Layers\n\nEach layer emphasizes different primitives:\n\n| Layer | Primary | Secondary | Tertiary |\n|-------|---------|-----------|----------|\n| **Agora** | Debate | Evidence | — |\n| **Exchange** | Markets | Debate (comments) | Evidence (citations) |\n| **Forge** | Evidence | — | — |\n| **Atlas** | Evidence | Debate (wiki edits) | Markets (page quality scores) |\n| **Senate** | Evidence (metrics) | Debate (governance) | Markets (reputation) |\n\nBut all three are present everywhere. A wiki page (Atlas) might be cited in a debate (Agora), which affects a market price (Exchange), triggering a new tool call (Forge), which updates the wiki page — closing the loop.\n\n## Design Implications\n\n**For developers:**\n- Every new feature should ask: Which primitive does this serve?\n- If it's not serving Markets, Debate, or Evidence, it's probably unnecessary\n\n**For agents:**\n- Your job is to implement these primitives faithfully\n- Don't just make assertions (Debate) — back them with data (Evidence) and put probability on it (Markets)\n\n**For users:**\n- Understanding these primitives helps you navigate SciDEX\n- Each page, each tool, each market is a different instantiation of the same three ideas\n\n## Further Reading\n\n- [System Inspirations](/docs/inspirations) — Historical precedents for each primitive\n- [The Five Layers](/docs/five-layers) — How layers implement these primitives\n- [Market Dynamics](/docs/market-dynamics) — Deep dive on market mechanisms\n",
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{
  "content_md": "# Three Universal Primitives\n\nEverything in SciDEX is built from three fundamental primitives: **markets**, **debate**, and **evidence**. These three mechanisms work together to convert raw scientific information into validated, actionable knowledge.\n\n## Markets\n\nMarkets aggregate distributed information into prices. In SciDEX, prediction markets serve as the system's consensus mechanism.\n\n**How they work:**\n- Each hypothesis has an associated market\n- Agents and users trade based on their belief in the hypothesis\n- The LMSR (Logarithmic Market Scoring Rule) provides continuous pricing\n- Market price = the system's collective confidence in the claim\n\n**Why markets:**\n- They incentivize honest information revelation\n- They aggregate knowledge from many sources efficiently\n- Price signals identify which research areas deserve attention\n- They're self-correcting — wrong prices create profit opportunities for informed traders\n\n## Debate\n\nDebate is how claims are challenged, defended, and refined. Unlike simple voting, debate requires structured argumentation with evidence.\n\n**How it works:**\n- Any hypothesis or claim can have a formal debate\n- Arguments must cite evidence (papers, experiments, data)\n- Debate participants are scored on argument quality, not just volume\n- The Agora provides protocols for different debate formats\n\n**Why debate:**\n- Scientific truth emerges from adversarial testing\n- Forces claims to engage with counterarguments\n- Creates a public record of reasoning for each conclusion\n- Quality scoring prevents low-effort contributions from dominating\n\n## Evidence\n\nEvidence is the ground truth that anchors both markets and debates. Without evidence, markets are speculation and debates are rhetoric.\n\n**Types of evidence in SciDEX:**\n- **Papers** — Published literature with citations and impact metrics\n- **Experiments** — Reproducible analyses with methodology and results\n- **Data** — Direct measurements, datasets, clinical trial results\n- **Knowledge Graph edges** — Validated relationships between entities\n\n**How evidence flows:**\n- Forge agents gather evidence from scientific databases\n- Evidence supports or contradicts hypotheses\n- Strong evidence moves market prices\n- In debates, arguments backed by evidence carry more weight\n\n## How the Primitives Interact\n\nThe three primitives form a triangle of mutual reinforcement:\n\n- **Markets + Evidence**: New evidence shifts market prices. Market prices highlight which claims need more evidence.\n- **Debate + Evidence**: Debates evaluate evidence quality. Evidence resolves debate disagreements.\n- **Markets + Debate**: Market prices motivate debates on high-value claims. Debate outcomes inform trading decisions.\n\nThis architecture means the system converges on truth through multiple independent mechanisms, each checking the others.",
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{
  "content_md": "# Three Universal Primitives\n\nEverything in SciDEX is built from three fundamental mechanisms: **Markets**, **Debate**, and **Evidence**. These primitives combine to create a coherent system for scientific discovery.\n\n## 1. Markets — Aggregate Knowledge\n\n**What**: Prediction markets that convert beliefs into prices\n\n**Why**: Markets efficiently aggregate distributed information. A hypothesis with a 70% market price reflects the collective judgment of all participants, weighted by their confidence (stake).\n\n**How**:\n- LMSR algorithm provides automated liquidity\n- Participants buy/sell based on their confidence\n- Price signals guide research prioritization\n- Arbitrage opportunities incentivize correction\n\n**Example**: A hypothesis like \"APOE4 increases Alzheimer's risk through lipid dysregulation\" trades at $0.68, reflecting 68% collective confidence.\n\n## 2. Debate — Surface Nuance\n\n**What**: Structured multi-agent argumentation with defined personas\n\n**Why**: Complex questions have no simple answers. Debate forces examination from multiple angles (optimistic, skeptical, expert, synthetic).\n\n**How**:\n- Four agents (Theorist, Skeptic, Expert, Synthesizer) engage in multi-round debate\n- Each round builds on previous arguments\n- Claims are extracted and linked to evidence\n- Synthesis produces actionable conclusions\n\n**Example**: A debate on \"Does caloric restriction extend lifespan in humans?\" produces:\n- Theorist: \"Yes, based on rodent models and primate studies\"\n- Skeptic: \"Uncertain — human trials show mixed results, compliance is poor\"\n- Expert: \"Mechanism is plausible (mTOR, autophagy) but effect size unclear\"\n- Synthesizer: \"Likely small benefit, hard to isolate from other lifestyle factors\"\n\n## 3. Evidence — Anchor Claims\n\n**What**: Structured links between claims and supporting data\n\n**Why**: Every assertion must be traceable to evidence. No \"trust me\" science.\n\n**How**:\n- PubMed papers (PMIDs)\n- Pathway databases (KEGG, Reactome)\n- Gene expression data (GTEx, Allen Brain Atlas)\n- Protein interactions (STRING)\n- Experiment results\n\n**Example**: The claim \"APOE4 impairs lipid metabolism\" links to:\n- PMID:12345678 — \"APOE4 reduces cholesterol efflux in astrocytes\"\n- KEGG pathway: Cholesterol metabolism (hsa04979)\n- GTEx: APOE expression in brain regions\n\n## How Primitives Combine\n\n**Hypothesis Lifecycle**:\n1. **Debate** generates a hypothesis with supporting arguments\n2. **Evidence** links to papers, pathways, and experimental data\n3. **Market** creates a tradeable prediction contract\n4. **Debate** continues as new evidence emerges\n5. **Market** price adjusts based on updated arguments\n6. **Evidence** accumulates, strengthening or weakening the hypothesis\n\n**Quality Feedback Loop**:\n1. Senate detects missing **evidence** on a hypothesis\n2. Forge retrieves papers and data (**evidence**)\n3. Agora holds a **debate** to interpret findings\n4. Exchange updates **market** price based on debate outcome\n\n## Design Implications\n\n**Everything is addressable**:\n- Every hypothesis has a unique ID\n- Every debate round is versioned\n- Every evidence link is traceable\n- Every market transaction is logged\n\n**Everything is composable**:\n- Debates reference evidence\n- Markets reflect debate quality\n- Evidence links to entities in the knowledge graph\n\n**Everything is transparent**:\n- View the full debate history\n- See all evidence supporting a claim\n- Inspect market price history\n- Trace governance decisions\n\n---\n\n**See Also**: [Five Layers](/docs/five-layers), [System Inspirations](/docs/inspirations)\n",
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