Longterm Wiki

Models Style Guide

Models Style Guide

This guide defines the standards for analytical models in LongtermWiki. Models should maximize information density while remaining accessible.

Prerequisite: All model pages must follow the Common Writing Principles — epistemic honesty, language neutrality, and analytical tone. The objectivity rating dimension measures this. For model pages with cost-effectiveness estimates, this is especially critical: always use ranges, show deflators, and include "Why These Numbers Might Be Wrong" sections.

Core Principles

  1. Density over brevity - Pack substantive content into every section. A 500-word model with tables and equations beats a 200-word model with bullets.
  2. Quantify everything possible - Probabilities, timelines, costs, thresholds. Vague claims waste reader attention.
  3. Show structure visually - Tables, diagrams, and equations communicate relationships faster than prose.
  4. Paragraphs over bullets - Bullets fragment thinking. Use them only for truly discrete items.

Required Sections

Every model should include:

1. Overview (2-3 paragraphs)

State the model's purpose, central question, and key insight. No bullets here—write flowing prose that orients the reader.

Bad:

## Overview
- This model looks at X
- Key question: Y
- Main finding: Z

Good:

## Overview

This model analyzes [phenomenon] by decomposing it into [components]. The central question: **[specific question with stakes]?**

The key insight is that [non-obvious conclusion]. This matters because [implication for AI safety/policy].

2. Conceptual Framework

Explain the model's structure. Include at least one of:

  • A Mermaid diagram showing relationships
  • A mathematical formulation
  • A typology table

3. Quantitative Analysis

The heart of the model. Must include:

  • Parameter tables with estimates and uncertainty ranges
  • Scenario analysis with probability-weighted outcomes
  • Sensitivity analysis showing which inputs matter most

4. Case Studies or Applications

Concrete examples showing the model applied. Tables comparing cases are ideal.

5. Limitations

Explicit acknowledgment of model weaknesses. Be specific about what the model ignores or gets wrong.

6. Related Models

Links to complementary models in the knowledge base.

Formatting Standards

Tables: Use Extensively

Tables compress information. Use them for:

  • Parameter estimates with ranges
  • Scenario comparisons
  • Timeline projections
  • Cost/benefit analyses
  • Threshold indicators

Minimum table requirements:

  • At least 3 columns (simple key-value pairs waste table format)
  • At least 4 rows of data
  • Header row clearly labeled
  • Include units and uncertainty ranges where applicable

Example - Parameter Table:

| Parameter | Best Estimate | Range | Confidence | Source |
|-----------|--------------|-------|------------|--------|
| P(misalignment) | 15% | 5-40% | Low | Expert surveys |
| Time to AGI | 2028 | 2025-2040 | Medium | Metaculus |
| Safety tax | 20% | 10-50% | Medium | Lab estimates |

Example - Scenario Table:

| Scenario | Probability | Outcome | Key Drivers |
|----------|-------------|---------|-------------|
| Coordinated slowdown | 15% | Low risk | International agreement, major incident |
| Competitive race | 45% | Medium-high risk | US-China tension, commercial pressure |
| Unilateral breakout | 25% | Very high risk | Capability surprise, regulatory failure |
| Managed transition | 15% | Low risk | Technical breakthrough in alignment |

Diagrams: Mermaid Required

Every model should have at least one diagram. Use Mermaid for:

Flowcharts - Causal chains and decision trees:

```mermaid
flowchart TD
    A[Capability Advance] --> B{Safety Research<br/>Keeps Pace?}
    B -->|Yes| C[Managed Development]
    B -->|No| D[Risk Gap Widens]
    D --> E{Incident Occurs?}
    E -->|Yes| F[Reactive Regulation]
    E -->|No| G[Continued Drift]
    G --> D
```​

State Diagrams - Phase transitions:

```mermaid
stateDiagram-v2
    [*] --> Reversible
    Reversible --> CostlyReversible: Deployment
    CostlyReversible --> PracticallyIrreversible: Lock-in
    PracticallyIrreversible --> AbsolutelyIrreversible: Existential threshold
    AbsolutelyIrreversible --> [*]
```​

Quadrant Charts - 2x2 analysis:

```mermaid
quadrantChart
    title Risk vs Tractability
    x-axis Low Tractability --> High Tractability
    y-axis Low Risk --> High Risk
    quadrant-1 Urgent Priority
    quadrant-2 Monitor Carefully
    quadrant-3 Opportunistic
    quadrant-4 Core Focus
    Misalignment: [0.3, 0.8]
    Misuse: [0.7, 0.6]
    Accidents: [0.8, 0.4]
    Structural: [0.4, 0.5]
```​

Sequence Diagrams - Interaction dynamics:

```mermaid
sequenceDiagram
    participant Lab A
    participant Lab B
    participant Regulator
    Lab A->>Lab B: Capability announcement
    Lab B->>Lab B: Accelerate timeline
    Lab B->>Lab A: Counter-announcement
    Regulator->>Lab A: Request safety assessment
    Lab A->>Regulator: Delayed response
    Note over Lab A,Lab B: Racing dynamics intensify
```​

Equations: Show Your Math

Include mathematical formulations where applicable. Use LaTeX:

Inline math for simple expressions: $P(X|Y) = 0.3$

Display math for key equations:

$$
R(t) = R_0 \cdot e^{\alpha t} \cdot (1 + \beta D)
$$

Where:
- $R_0$ = Base reversal cost at deployment
- $\alpha$ = Growth rate (0.1-0.5 per year)
- $t$ = Time since deployment
- $\beta$ = Dependency multiplier
- $D$ = Dependency depth (0 to 1)

Always include:

  • Variable definitions immediately after the equation
  • Realistic parameter ranges
  • Intuition for what the equation captures

Prose: Paragraphs Over Bullets

Bullets should be rare. They're appropriate for:

  • Truly discrete, unordered items (e.g., list of examples)
  • Quick reference lists at end of sections
  • Items that will be expanded in subsequent sections

Bad - Bullet brain:

## Racing Dynamics

- Labs compete for capabilities
- Safety work slows deployment
- First-mover advantages exist
- Coordination is difficult
- Racing creates risk

Good - Dense paragraphs:

## Racing Dynamics

Racing dynamics emerge when multiple actors pursue the same capability under competitive pressure. In AI development, labs face a structural tension: safety work requires time and resources that slow deployment, but first-mover advantages in capabilities—talent attraction, data access, revenue, and strategic positioning—create intense pressure to move fast. This produces a classic collective action problem where individually rational choices generate collectively irrational outcomes.

The severity of racing depends on three factors: the perceived magnitude of first-mover advantages, the credibility of competitors' timelines, and the availability of coordination mechanisms. When labs believe winner-take-all dynamics apply, racing pressure intensifies regardless of stated safety commitments.

Information Density Checklist

Before submitting a model, verify:

  • Tables: At least 2 substantive tables (4+ rows, 3+ columns each)
  • Diagram: At least 1 Mermaid diagram showing relationships
  • Equations: Mathematical formulation where applicable
  • Numbers: Probabilities, timelines, or thresholds quantified with ranges
  • Scenarios: Multiple scenarios analyzed with probability weights
  • Paragraphs: Less than 30% of content in bullet points
  • Length: Minimum 800 words of substantive content
  • Sources: Key claims attributed to sources

Anti-Patterns to Avoid

1. Bullet Lists as Primary Content

Bullets should support paragraphs, not replace them.

2. Vague Qualitative Claims

"Risk is high" → "Risk estimated at 15-30% (median 22%) based on expert surveys"

3. Missing Uncertainty

Always include ranges, not point estimates alone.

4. Tables Without Context

Tables need introductory sentences explaining what they show and key takeaways.

5. Diagrams Without Explanation

Every diagram needs a paragraph explaining what it illustrates and key insights.

6. Orphan Sections

Short sections (< 100 words) should be merged or expanded.

7. Insider Language and False Certainty

"EA organizations should fund this" → Describe specific orgs, use analytical framing. "True Cost: $500K" → "Est. cost: $300K-1M". See Common Writing Principles.

Example Model Structure

---
title: [Descriptive Model Name]
description: [One sentence capturing the model's purpose]
ratings:
  novelty: [0-10]
  rigor: [0-10]
  actionability: [0-10]
  completeness: [0-10]
---

## Overview
[2-3 paragraphs: purpose, central question, key insight]

## Conceptual Framework
[Structure explanation with diagram]

```mermaid
[diagram here]
```​

## Core Model
### Mathematical Formulation
$$[key equation]$$

[Variable definitions and intuition]

### Parameter Estimates
| Parameter | Estimate | Range | Confidence |
|-----------|----------|-------|------------|
| ... | ... | ... | ... |

## Analysis
### Scenario Analysis
| Scenario | P(scenario) | Outcome | Drivers |
|----------|-------------|---------|---------|
| ... | ... | ... | ... |

[Paragraph discussing scenarios]

### Sensitivity Analysis
[Which parameters matter most and why]

## Case Studies
### Case 1: [Name]
[Application of model to concrete example]

### Case 2: [Name]
[Another application]

## Implications
[What this model suggests for policy/research/action]

## Limitations
[Explicit weaknesses - be specific]

## Related Models
- [Link 1] - [relationship]
- [Link 2] - [relationship]

## Sources
[Key references]

Rating Criteria

Models are rated on seven dimensions (0-10 scale, harsh - 7+ is exceptional):

Focus: Does it answer what the title promises?

Novelty: How original is the framing or analysis?

  • 3-4: Useful synthesis or modest extensions
  • 5-6: Genuine new framing or connections
  • 7+: Novel framework that changes how we think about the problem

Rigor: How well-supported by evidence and logic?

  • 3-4: Reasonable extrapolation, some gaps
  • 5-6: Most claims sourced with some quantification
  • 7+: Strong empirical grounding or formal derivation

Completeness: How thoroughly developed?

  • 3-4: Core model sketched, notable gaps
  • 5-6: Core model complete, some gaps
  • 7+: Comprehensive treatment with edge cases addressed

Objectivity: Epistemic honesty, neutral language, analytical tone?

  • 3-4: Estimates without ranges; insider language; one-sided framing
  • 5-6: Mostly hedged and neutral; some uncertainty noted
  • 7+: All estimates use ranges with caveats; fully accessible; red-teams own conclusions

Concreteness: Specific numbers, examples, recommendations?

Actionability: Does it suggest concrete interventions?

  • 3-4: Identifies general areas of concern
  • 5-6: Identifies leverage points with some specifics
  • 7+: Specific, implementable recommendations with priorities