Factor Diagram Naming: Research Report

Executive Summary

Finding	Key Insight	Recommendation
"Cause-effect" is misleading	Our diagrams aren't strictly causal—they include uncertainties, considerations, and influence relationships	Rename to something more accurate
Multiple established frameworks exist	Influence diagrams, driver diagrams, argument maps, crux maps all map similar territory	Borrow terminology strategically
Uncertainty types matter	Aleatoric (inherent randomness) vs epistemic (knowledge gaps) distinction is well-established	Consider incorporating uncertainty typology
"Crux" has traction in target audience	Rationalist/EA community already uses "crux" terminology from CFAR	Strong candidate for naming
Recommended name	"Crux Map" or "Factor Map"	Balances clarity, accuracy, and audience fit

Background

The Problem

Our current "cause-effect diagrams" visualize factors that influence outcomes, including uncertainties, considerations, and decision-relevant relationships. The name "cause-effect" implies strict causality that doesn't match what we're actually modeling.

This report surveys similar conceptual frameworks across multiple fields to identify appropriate terminology for LongtermWiki's factor visualization system. We examined:

Decision analysis: Influence diagrams, decision trees, tornado diagrams
Systems thinking: Causal loop diagrams, stock-flow models
Philosophy/Argumentation: Argument maps, IBIS, crucial considerations
Rationalist community: Crux mapping, double crux
Quality improvement: Driver diagrams, theory of change
Knowledge representation: Concept maps, knowledge graphs

Framework Comparison

1. Influence Diagrams (Decision Analysis)

Origin: Introduced by Howard and Matheson in 1981, building on Howard Raiffa's decision analysis work.

Definition: A graph representing a Bayesian decision problem with three node types:

Circles: Random variables (uncertainties)
Rectangles: Decision variables (choices)
Diamonds: Utility/value variables (outcomes)

Strength	Weakness
Distinguishes uncertainty from decisions	Technical/academic connotation
Well-established in decision theory	Implies quantitative Bayesian framework
Supports automatic model generation	Less familiar outside specialized fields

Relevance

Influence diagrams share our goal of mapping what affects outcomes, but they're specifically designed for quantitative decision analysis with probability distributions. Our diagrams are more qualitative and conceptual.

Sources: Decision Analysis Wikipedia, INFORMS Decision Analysis

2. Causal Loop Diagrams (Systems Thinking)

Origin: Emerged from System Dynamics practice, popularized by Jay Forrester and the MIT school.

Definition: A diagram showing causal relationships between variables, with emphasis on feedback loops:

Reinforcing loops (R): Change compounds (e.g., compound interest)
Balancing loops (B): Change counteracts (e.g., thermostat)
Polarity (+/-): Whether variables move together or opposite

Strength	Weakness
Handles feedback loops natively	Our diagrams are DAGs (no cycles)
Clear visual conventions	"Causal" in the name has same problem
Popular in organizational contexts	Emphasizes dynamics over static factors

Key Difference

CLDs are designed for systems with feedback loops. Our current implementation uses Dagre (DAG layout), which doesn't support cycles. If we wanted to support feedback, we'd need different rendering.

Sources: Causal Loop Diagram Wikipedia, Systems Thinker, Cascade Institute Handbook

3. Argument Maps & IBIS (Argumentation)

Origin: IBIS (Issue-Based Information System) invented by Werner Kunz and Horst Rittel in the 1960s for tackling "wicked problems."

Core Elements:

Issues: Questions to be addressed
Positions: Proposed answers/solutions
Arguments: Pro/con rationales supporting or opposing positions

Strength	Weakness
Designed for complex, ill-defined problems	Focused on argumentation, not factors
Captures disagreement structure	Three-type ontology is restrictive
Software tools exist (Compendium)	Less familiar than other terms

Insight

IBIS's focus on structuring discourse around "wicked problems" resonates with LongtermWiki's goal. The emphasis on capturing positions and arguments for/against is similar to how we model different views on factors.

Tools: Compendium, DRed, designVUE

Sources: IBIS Wikipedia, Kunz & Rittel 1970, Cognexus

4. Crucial Considerations (Philosophy)

Origin: Concept introduced by Nick Bostrom in 2007, elaborated in a 2014 talk.

Definition: "A consideration such that if it were taken into account, it would overturn the conclusions we would otherwise reach about how we should direct our efforts."

Key concepts:

Crucial consideration: Overturns conclusions
Crucial consideration component: Becomes crucial when combined with other considerations
Deliberation ladder: Sequence of successive reassessments

Strength	Weakness
Directly relevant to LongtermWiki's mission	"Consideration" is abstract
Familiar to EA/rationalist audience	More about ideas than structure
Captures the "changes your mind" aspect	Not specifically a diagram type

Key Quote

"If we have overlooked even just one such consideration, then all our best efforts might be for naught – or less. When headed the wrong way, the last thing needed is progress." — Nick Bostrom

Sources: Bostrom's website, EA Forum, Effective Altruism article

5. Crux Mapping (Rationalist Community)

Origin: Developed by CFAR (Center for Applied Rationality) as part of their curriculum.

Definitions:

Crux: A belief C that, if changed, would change your belief in B
Double crux: A shared crux where two disagreeing people would both change their minds

Victory condition: "Agreement on a shared causal model of the world—you've won when both you and your partner agree to the same if-then statements."

Strength	Weakness
Familiar to target audience	Jargon outside rationalist circles
Emphasizes "what would change your mind"	Originally about dyadic disagreement
"Crux" is memorable and specific	Some critique it as overly idealized

Perfect Fit

The crux concept directly aligns with LongtermWiki's goal of mapping key uncertainties that drive prioritization. "What would change your mind about this intervention?" is exactly what we're trying to capture.

Sources: LessWrong Double Crux, CFAR Resources, Basic Double Crux Pattern

6. Driver Diagrams (Quality Improvement)

Origin: Widely used in healthcare quality improvement, particularly with Plan-Do-Study-Act (PDSA) cycles.

Structure:

Aim: What you're trying to achieve
Primary drivers: High-level factors that must be influenced
Secondary drivers: Specific factors/interventions
Change ideas: Concrete actions

Strength	Weakness
Clear hierarchical structure	Associated with healthcare/QI
"Driver" is intuitive	Implies more agency than we model
Links factors to interventions	May feel too operational

Sources: IHI Driver Diagram, AHRQ Key Driver Diagram

7. Theory of Change / Logic Models (Nonprofit)

Origin: Common in nonprofit and philanthropic contexts.

Key distinction:

Theory of Change: Explains why change will occur, works backward from impact
Logic Model: Describes what will happen, works forward from inputs

Strength	Weakness
Familiar to funders/nonprofits	Associated with program evaluation
Emphasizes causal pathways	Less about uncertainty
Links to impact measurement	May feel too outcome-focused

Sources: NPC Theory of Change, La Piana comparison, TOC Toolkit

8. Concept Maps vs Knowledge Graphs

Feature	Mind Map	Concept Map	Knowledge Graph
Structure	Tree (single center)	Network (multiple hubs)	Graph/Network
Relationships	Implicit	Labeled connections	Typed relationships
Origin	Tony Buzan (1974)	Joseph Novak (1970s)	Computer science

Our diagrams are most similar to concept maps: network structure with labeled relationships between nodes.

Sources: NN/g Cognitive Maps, Gloow comparison, Concept Map Wikipedia

Uncertainty Types

Why This Matters

Our diagrams include nodes with varying confidence levels. Understanding established uncertainty typologies helps us communicate what kinds of uncertainty we're representing.

Aleatoric vs Epistemic Uncertainty

Type	Definition	Can be reduced?	Example
Aleatoric	Inherent randomness in the system	No—only quantified	Coin flip outcome
Epistemic	Uncertainty due to lack of knowledge	Yes—with more information	"Will this policy pass?"

Key insight: "Epistemic uncertainty tends to be expressed using phrases like 'I am 90% sure' whereas aleatory uncertainty tends to be expressed using phrases like 'I think there's a 90% chance.'"

Our diagrams mostly represent epistemic uncertainty—things we don't know but could learn—rather than inherent randomness.

Sources: Springer Machine Learning, Berkeley Statistics, Towards Data Science

Naming Options Analysis

Based on this research, here are the main naming candidates:

Option 1: Crux Map

Dimension	Assessment
Accuracy	High—captures "what would change your mind"
Familiarity	High for EA/rationalist audience, low outside
Distinctiveness	High—not easily confused with other diagram types
Fit with LongtermWiki	Excellent—aligns with Crux Graph component

Recommended

"Crux Map" directly aligns with LongtermWiki's mission (mapping key uncertainties) and the target audience's vocabulary. The "crux" terminology is already established in the rationalist/EA community.

Option 2: Factor Map

Dimension	Assessment
Accuracy	High—we are mapping factors
Familiarity	Medium—generic but clear
Distinctiveness	Medium—could be confused with factor analysis
Fit with LongtermWiki	Good—neutral, accessible

Option 3: Influence Map

Dimension	Assessment
Accuracy	High—captures influence relationships
Familiarity	Medium—some association with decision analysis
Distinctiveness	Medium—could be confused with influence diagrams
Fit with LongtermWiki	Good—clear meaning

Option 4: Consideration Map

Dimension	Assessment
Accuracy	High—we're mapping considerations
Familiarity	Low—not established terminology
Distinctiveness	High—distinctive
Fit with LongtermWiki	Good—connects to "crucial considerations"

Option 5: Driver Map/Diagram

Dimension	Assessment
Accuracy	Medium—implies more agency
Familiarity	Medium—established in QI
Distinctiveness	Medium—associated with healthcare
Fit with LongtermWiki	Moderate—less about uncertainty

Option 6: Priority Map

Dimension	Assessment
Accuracy	Medium—we map factors, not just priorities
Familiarity	Low—not established
Distinctiveness	Medium
Fit with LongtermWiki	Moderate—captures decision-relevance

Option 7: Leverage Map

Dimension	Assessment
Accuracy	Medium—emphasizes tractability
Familiarity	Low—not established
Distinctiveness	High
Fit with LongtermWiki	Moderate—overemphasizes changeability

Recommendations

Primary Recommendation: Crux Map

Rationale:

Directly aligns with LongtermWiki's mission ("Crux Graph" is already a core component)
Familiar to the target audience (EA/rationalist community)
Captures the key insight: "what would change your mind?"
Distinctive—won't be confused with other diagram types
The word "crux" conveys importance and decision-relevance

Alternative: Factor Map

Use if: You want broader accessibility outside the rationalist community. "Factor" is neutral, clear, and doesn't require explaining jargon.

Naming for Node Scores

Our diagrams include four scoring dimensions. Consider renaming to clarify uncertainty types:

Current Name	Suggested Rename	Rationale
`novelty`	Keep as is	Clear meaning
`sensitivity`	Keep as is	Clear meaning
`changeability`	`tractability`	More common in EA
`certainty`	`epistemic confidence`	Distinguishes from aleatoric

Implementation Checklist

If renaming to "Crux Map":

Update cause-effect-diagrams.mdx → crux-maps.mdx
Update component name: CauseEffectGraph → CruxMap
Update YAML property: causeEffectGraph → cruxMap
Update style guide references
Update skill name: /cause-effect-diagram → /crux-map
Update documentation throughout