Worldview-Intervention Mapping

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Quality:88 (Comprehensive)

Importance:82.5 (High)

Last edited:2025-12-26 (12 days ago)

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📊 25📈 2🔗 50📚 0•2%Score: 12/15

LLM Summary:Maps three belief dimensions (timelines, alignment difficulty, coordination feasibility) to four worldview clusters showing 2-10x differences in optimal intervention priorities, with quantified expected ROI ranges for pause advocacy (10x+), technical research (8-12x), and governance work (3-8x) depending on worldview assumptions.

Model

Worldview-Intervention Mapping

Importance82

Model TypeStrategic Framework

FocusWorldview-Action Coherence

Key OutputIntervention priorities given different worldviews

Related

Models

• AI Risk Portfolio Analysis
• Racing Dynamics

Model Quality

Novelty

5

Rigor

3

Actionability

5

Completeness

3

Overview

This model maps how beliefs about AI risk create distinct worldview clusters with dramatically different intervention priorities. Different worldviews imply 2-10x differences in optimal resource allocation across pause advocacy, technical research, and governance work.

The model identifies that misalignment between personal beliefs and work focus may waste 20-50% of field resources. AI safety researchers↗ hold fundamentally different assumptions about timelines, technical difficulty, and coordination feasibility, but these differences often don’t translate to coherent intervention choices.

The framework reveals four major worldview clusters - from “doomer” (short timelines + hard alignment) prioritizing pause advocacy, to “technical optimist” (medium timelines + tractable alignment) emphasizing research investment.

Risk/Impact Assessment

Dimension	Assessment	Evidence	Timeline
Severity	High	2-10x resource allocation differences across worldviews	Immediate
Likelihood	Very High	Systematic worldview-work mismatches observed	Ongoing
Scope	Field-wide	Affects individual researchers, orgs, and funders	All levels
Trend	Worsening	Field growth without explicit worldview coordination	2024-2027

Strategic Question Framework

Given your beliefs about AI risk, which interventions should you prioritize?

The core problem: People work on interventions that don’t match their stated beliefs about AI development. This model makes explicit which interventions are most valuable under specific worldview assumptions.

How to Use This Framework

Step	Action	Tool
1	Identify worldview	Assess beliefs on timeline/difficulty/coordination
2	Check priorities	Map beliefs to intervention recommendations
3	Audit alignment	Compare current work to worldview implications
4	Adjust strategy	Either change work focus or update worldview

Core Worldview Dimensions

Three belief dimensions drive most disagreement about intervention priorities:

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Dimension 1: Timeline Beliefs

Timeline	Key Beliefs	Strategic Constraints	Supporting Evidence
Short (2025-2030)	AGI within 5 years; scaling continues; few obstacles	Little time for institutional change; must work with existing structures	Amodei prediction↗ of powerful AI by 2026-2027
Medium (2030-2040)	Transformative AI in 10-15 years; surmountable obstacles	Time for institution-building; research can mature	Metaculus consensus↗ ~2032 for AGI
Long (2040+)	Major obstacles remain; slow takeoff; decades available	Full institutional development possible; fundamental research valuable	MIRI position↗ on alignment difficulty

Dimension 2: Alignment Difficulty

Difficulty	Core Assumptions	Research Implications	Current Status
Hard	Alignment fundamentally unsolved; deception likely; current techniques inadequate	Technical solutions insufficient; need to slow/stop development	Scheming research↗ shows deception possible
Medium	Alignment difficult but tractable; techniques improve with scale	Technical research highly valuable; sustained investment needed	Constitutional AI↗ shows promise
Tractable	Alignment largely solved; RLHF + interpretability sufficient	Focus on deployment governance; limited technical urgency	OpenAI safety approach↗ assumes tractability

Dimension 3: Coordination Feasibility

Feasibility	Institutional View	Policy Implications	Historical Precedent
Feasible	Treaties possible; labs coordinate; racing avoidable	Invest heavily in coordination mechanisms	Nuclear Test Ban Treaty, Montreal Protocol
Difficult	Partial coordination; major actors defect; limited cooperation	Focus on willing actors; partial governance	Climate agreements with partial compliance
Impossible	Pure competition; no stable equilibria; universal racing	Technical safety only; governance futile	Failed disarmament during arms races

Four Major Worldview Clusters

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Cluster 1: “Doomer” Worldview

Beliefs: Short timelines + Hard alignment + Coordination difficult

Intervention Category	Priority	Expected ROI	Key Advocates
Pause/slowdown advocacy	Very High	10x+ if successful	Eliezer Yudkowsky
Compute governance	Very High	5-8x via bottlenecks	RAND reports↗
Technical safety research	High	2-4x (low prob, high value)	MIRI approach
International coordination	Medium	8x if achieved (low prob)	FHI governance work↗
Field-building	Low	1-2x (insufficient time)	Long-term capacity building
Public engagement	Medium	3-5x via political support	Pause AI movement↗

Coherence Check: If you believe this worldview but work on field-building or long-term institution design, your work may be misaligned with your beliefs.

Cluster 2: “Technical Optimist” Worldview

Beliefs: Medium timelines + Medium difficulty + Coordination possible

Intervention Category	Priority	Expected ROI	Leading Organizations
Technical safety research	Very High	8-12x via direct solutions	Anthropic, Redwood
Interpretability	Very High	6-10x via understanding	Chris Olah’s work
Lab safety standards	High	4-6x via industry norms	Partnership on AI↗
Compute governance	Medium	3-5x supplementary value	CSET↗ research
Pause advocacy	Low	1x or negative (unnecessary)	Premature intervention
Field-building	High	5-8x via capacity	CHAI, MATS↗

Coherence Check: If you believe this worldview but work on pause advocacy or aggressive regulation, your efforts may be counterproductive.

Cluster 3: “Governance-Focused” Worldview

Beliefs: Medium-long timelines + Medium difficulty + Coordination feasible

Intervention Category	Priority	Expected ROI	Key Institutions
International coordination	Very High	10-15x via global governance	UK AISI, US AISI
Domestic regulation	Very High	6-10x via norm-setting	EU AI Act↗
Institution-building	Very High	8-12x via capacity	AI Safety Institute↗ development
Technical standards	High	4-6x enabling governance	NIST AI RMF↗
Technical research	Medium	3-5x (others lead)	Research coordination role
Pause advocacy	Low	1-2x premature	Governance development first

Coherence Check: If you believe this worldview but focus purely on technical research, you may be underutilizing comparative advantage.

Cluster 4: “Accelerationist/Optimist” Worldview

Beliefs: Any timeline + Tractable alignment + Any coordination level

Intervention Category	Priority	Expected ROI	Rationale
Capability development	Very High	15-25x via benefits	AI solves problems faster than creates them
Deployment governance	Medium	2-4x addressing specific harms	Targeted harm prevention
Technical safety	Low	1-2x already adequate	RLHF sufficient for current systems
Pause/slowdown	Very Low	Negative ROI	Delays beneficial AI
Aggressive regulation	Very Low	Large negative ROI	Stifles innovation unnecessarily

Coherence Check: If you hold this worldview but work on safety research or pause advocacy, your work contradicts your beliefs about AI risk levels.

Intervention Effectiveness Matrix

The following analysis shows how intervention effectiveness varies dramatically across worldviews:

Intervention	Short+Hard (Doomer)	Short+Tractable (Sprint)	Long+Hard (Patient)	Long+Tractable (Optimist)
Pause/slowdown	Very High (10x)	Low (1x)	Medium (4x)	Very Low (-2x)
Compute governance	Very High (8x)	Medium (3x)	High (6x)	Low (1x)
Alignment research	High (3x)	Low (2x)	Very High (12x)	Low (1x)
Interpretability	High (4x)	Medium (5x)	Very High (10x)	Medium (3x)
International treaties	Medium (2x)	Low (1x)	Very High (15x)	Medium (4x)
Domestic regulation	Medium (3x)	Medium (4x)	High (8x)	Medium (3x)
Lab safety standards	High (6x)	High (7x)	High (8x)	Medium (4x)
Field-building	Low (1x)	Low (2x)	Very High (12x)	Medium (5x)
Public engagement	Medium (4x)	Low (2x)	High (7x)	Low (1x)

Critical Insight

Working on “Very High” priority interventions under the wrong worldview can waste 5-10x resources compared to optimal allocation. This represents one of the largest efficiency losses in the AI safety field.

Portfolio Strategies for Uncertainty

Timeline Uncertainty Management

Uncertainty Level	Recommended Allocation	Hedge Strategy
50/50 short vs long	60% urgent interventions, 40% patient capital	Compute governance + field-building
70% short, 30% long	80% urgent, 20% patient with option value	Standards + some institution-building
30% short, 70% long	40% urgent, 60% patient development	Institution-building + some standards

Alignment Difficulty Hedging

Belief Distribution	Technical Research	Governance/Coordination	Rationale
50% hard, 50% tractable	40% allocation	60% allocation	Governance has value regardless
80% hard, 20% tractable	20% allocation	80% allocation	Focus on buying time
20% hard, 80% tractable	70% allocation	30% allocation	Technical solutions likely

Coordination Feasibility Strategies

Scenario	Unilateral Capacity	Multilateral Investment	Leading Actor Focus
High coordination feasibility	20%	60%	20%
Medium coordination feasibility	40%	40%	20%
Low coordination feasibility	60%	10%	30%

Current State & Trajectory

Field-Wide Worldview Distribution

Worldview Cluster	Estimated Prevalence	Resource Allocation	Alignment Score
Doomer	15-20% of researchers	~30% of resources	Moderate misalignment
Technical Optimist	40-50% of researchers	~45% of resources	Good alignment
Governance-Focused	25-30% of researchers	~20% of resources	Poor alignment
Accelerationist	5-10% of researchers	~5% of resources	Unknown

Observed Misalignment Patterns

Based on AI Alignment Forum↗ surveys and 80,000 Hours↗ career advising:

Common Mismatch	Frequency	Estimated Efficiency Loss
”Short timelines” researcher doing field-building	25% of junior researchers	3-5x effectiveness loss
”Alignment solved” researcher doing safety work	15% of technical researchers	2-3x effectiveness loss
”Coordination impossible” researcher doing policy	10% of policy researchers	4-6x effectiveness loss

2024-2027 Trajectory Predictions

Trend	Likelihood	Impact on Field Efficiency
Increased worldview polarization	High	-20% to -30% efficiency
Better worldview-work matching	Medium	+15% to +25% efficiency
Explicit worldview institutions	Low	+30% to +50% efficiency

Key Uncertainties & Cruxes

❓Key Questions

What's the actual distribution of worldviews among AI safety researchers?

How much does worldview-work mismatch reduce field effectiveness quantitatively?

Can people reliably identify and articulate their own worldview assumptions?

Would explicit worldview discussion increase coordination or create harmful polarization?

How quickly should people update worldviews based on new evidence?

Do comparative advantages sometimes override worldview-based prioritization?

Resolution Timelines

Uncertainty	Evidence That Would Resolve	Timeline
Actual worldview distribution	Comprehensive field survey	6-12 months
Quantified efficiency losses	Retrospective impact analysis	1-2 years
Worldview updating patterns	Longitudinal researcher tracking	2-5 years
Institutional coordination effects	Natural experiments with explicit worldview orgs	3-5 years

Implementation Guidance

For Individual Researchers

Career Stage	Primary Action	Secondary Actions
Graduate students	Identify worldview before specializing	Talk to advisors with different worldviews
Postdocs	Audit current work against worldview	Consider switching labs if misaligned
Senior researchers	Make worldview explicit in work	Mentor others on worldview coherence
Research leaders	Hire for worldview diversity	Create space for worldview discussion

For Organizations

Organization Type	Strategic Priority	Implementation Steps
Research organizations	Clarify institutional worldview	Survey staff, align strategy, communicate assumptions
Grantmaking organizations	Develop worldview-coherent portfolios	Map grantee worldviews, identify gaps, fund strategically
Policy organizations	Coordinate across worldview differences	Create cross-worldview working groups
Field-building organizations	Facilitate worldview discussion	Host workshops, create assessment tools

For Funders

Funding Approach	When Appropriate	Risk Management
Single worldview concentration	High confidence in specific worldview	Diversify across intervention types within worldview
Worldview hedging	High uncertainty about key parameters	Fund complementary approaches, avoid contradictory grants
Worldview arbitrage	Identified underinvested worldview-intervention combinations	Focus on neglected high-value combinations

Failure Mode Analysis

Individual Failure Modes

Failure Mode	Prevalence	Mitigation Strategy
Social conformity bias	High	Create protected spaces for worldview diversity
Career incentive misalignment	Medium	Reward worldview-coherent work choices
Worldview rigidity	Medium	Encourage regular worldview updating
False precision in beliefs	High	Emphasize uncertainty and portfolio approaches

Institutional Failure Modes

Failure Mode	Symptoms	Solution
Worldview monoculture	All staff share same assumptions	Actively hire for belief diversity
Incoherent strategy	Contradictory intervention portfolio	Make worldview assumptions explicit
Update resistance	Strategy unchanged despite new evidence	Create structured belief updating processes

Sources & Resources

Research Literature

Category	Key Sources	Quality	Focus
Worldview surveys	AI Alignment Forum survey↗	Medium	Community beliefs
Intervention effectiveness	80,000 Hours research↗	High	Career prioritization
Strategic frameworks	Open Philanthropy worldview reports↗	High	Cause prioritization

Tools & Assessments

Resource	Purpose	Access
Worldview self-assessment	Individual belief identification	AI Safety Fundamentals↗
Intervention prioritization calculator	Portfolio optimization	EA Forum tools↗
Career decision frameworks	Work-belief alignment	80,000 Hours coaching↗

Organizations by Worldview

Organization	Primary Worldview	Core Interventions
MIRI	Doomer (short+hard)	Agent foundations, pause advocacy
Anthropic	Technical optimist	Constitutional AI, interpretability
CSET↗	Governance-focused	Policy research, international coordination
Redwood Research	Technical optimist	Alignment research, interpretability

Related Models & Pages

Complementary Models

• AI Risk Portfolio Analysis - Risk category prioritization across scenarios
• Racing Dynamics - How competition affects coordination feasibility
• International Coordination Game - Factors affecting cooperation

Related Worldviews

• Doomer Worldview - Short timelines, hard alignment assumptions
• Governance-Focused Worldview - Coordination optimism, institution-building focus
• Long Timelines Worldview - Patient capital, fundamental research emphasis

What links here

• Intervention Timing Windowsmodel