QURI (Quantified Uncertainty Research Institute)

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Organization Details

Overview

The Quantified Uncertainty Research Institute (QURI) is a nonprofit research organization focused on developing tools and methodologies for probabilistic reasoning and forecasting. Founded in 2019 by Ozzie Gooen, QURI aims to make uncertainty quantification more accessible and rigorous, particularly for decisions affecting the long-term future of humanity. The organization evolved from Gooen's earlier work on Guesstimate, a spreadsheet tool for Monte Carlo simulations that demonstrated strong demand for accessible uncertainty tools in the effective altruism community.

QURI's flagship project is Squiggle, a domain-specific programming language designed specifically for probabilistic estimation. Unlike general-purpose languages, Squiggle provides first-class support for probability distributions, enabling analysts to express complex uncertainties naturally. The language powers Fermi estimates, cost-effectiveness analyses, and forecasting models used throughout the effective altruism and rationalist communities. QURI's experience building tools like Squiggle and Guesstimate has revealed a significant challenge: even highly skilled domain experts frequently struggle with basic programming requirements and often make errors in their probabilistic models.

The organization has expanded beyond Squiggle to encompass a suite of epistemic tools: Squiggle Hub for collaborative model sharing (hosting 17,000+ Guesstimate models), Metaforecast for aggregating predictions across 10+ platforms, SquiggleAI for LLM-assisted model generation, and RoastMyPostProjectRoastMyPostRoastMyPost is an LLM tool (Claude Sonnet 4.5 + Perplexity) that evaluates written content through multiple specialized AI agents—fact-checking, logical fallacy detection, math verification, and mo...Quality: 35/100 for AI-powered blog post evaluation. Together, these tools form an ecosystem aimed at improving quantitative reasoning in high-stakes domains.

Organizations in the effective altruism and rationalist communities regularly rely on cost-effectiveness analyses and Fermi estimates to guide their decisions. QURI's mission is to make these probabilistic tools more accessible and reliable for altruistic causes, bridging the gap between sophisticated quantitative methods and practical decision-making.

Founder: Ozzie Gooen

Ozzie Gooen is the founder and Executive Director of QURI, with a background spanning engineering, effective altruism, and forecasting research.

Background and Career

Gooen discovered effective altruism in college after learning he "sounded like a utilitarian." His path from engineering to forecasting research reflects a consistent interest in applying quantitative methods to important decisions. At FHI, he focused on forecasting infrastructure research, exploring how prediction marketsApproachPrediction Markets (AI Forecasting)Prediction markets achieve Brier scores of 0.16-0.24 (15-25% better than polls) by aggregating dispersed information through financial incentives, with platforms handling $1-3B annually. For AI saf...Quality: 56/100 and aggregation methods could improve institutional decision-making.

Beyond QURI, Gooen serves on the boards of Rethink Charity and Rethink Priorities. He has contributed to discussions on AI governanceParameterAI GovernanceThis page contains only component imports with no actual content - it displays dynamically loaded data from an external source that cannot be evaluated., forecasting methodology, and quantitative cause prioritization through extensive writing on the EA Forum and LessWrong.

Notable Contributions

• Guesstimate: Created the first widely-used Monte Carlo spreadsheet, which runs 5,000 simulations per model and has accumulated 17,000+ public models
• Forecasting Research: Developed proposals for prediction market improvements and forecast aggregation methods
• QURI Tools Ecosystem: Oversaw development of Squiggle, Metaforecast, SquiggleAI, and RoastMyPostProjectRoastMyPostRoastMyPost is an LLM tool (Claude Sonnet 4.5 + Perplexity) that evaluates written content through multiple specialized AI agents—fact-checking, logical fallacy detection, math verification, and mo...Quality: 35/100
• EA Community: Helped build early EA infrastructure through .impact and ongoing community engagement

Core Projects

Squiggle Language

Squiggle is QURI's primary project—a domain-specific programming language for probabilistic estimation that runs in the browser via JavaScript. It can be useful to think of Squiggle as similar to SQL, Excel, or probabilistic programming languages like WebPPL: there are simple ways to declare variables and write functions, but don't expect classes, inheritance, or monads.

Design Philosophy

Squiggle is meant for intuitively-driven quantitative estimation rather than data analysis or data-driven statistical techniques. It's designed for situations where there is very little data available, and most variables will be intuitively estimated by domain experts. The syntax is forked from Guesstimate and Foretold, optimized for readable probabilistic expressions.

Core Features

Distribution Creation Syntax

Squiggle offers flexible distribution creation with multiple parameterization options:

// Normal distribution - multiple ways to specify
normal(10, 2)                    // mean, standard deviation
normal({mean: 10, stdev: 2})     // explicit parameters
normal({p5: 5, p95: 15})         // percentile-based
normal({p10: 6, p90: 14})        // different percentiles
normal({p25: 8, p75: 12})        // quartile-based

// Lognormal distribution - the workhorse for positive quantities
lognormal(2, 0.5)                // mu, sigma (log-scale parameters)
lognormal({mean: 10, stdev: 5})  // arithmetic mean/stdev
5 to 50                          // shorthand for lognormal({p5: 5, p95: 50})

// Beta distribution - for probabilities between 0 and 1
beta(2, 8)                       // alpha, beta parameters (~20% mean)
beta({mean: 0.3, stdev: 0.1})    // mean/stdev parameterization

// Other distributions
uniform(5, 15)                   // uniform between bounds
triangular(5, 10, 15)            // min, mode, max
exponential(0.1)                 // rate parameter

Squiggle Code Examples

Basic Cost-Effectiveness Model:

// Cost-effectiveness model for AI safety intervention
interventionCost = lognormal(1e6, 1.5) // \$1M median, high uncertainty
probabilityOfSuccess = beta(2, 8) // ~20% base rate
valueIfSuccessful = lognormal(1e12, 2) // High but uncertain value

expectedValue = probabilityOfSuccess * valueIfSuccessful
costEffectiveness = expectedValue / interventionCost

Function with Domain Constraints:

// Calculate expected value with bounded probability
calculateEV(probability: [0, 1], value) = {
  adjustedProb = probability * normal(1, 0.1) // Add estimation uncertainty
  truncate(adjustedProb, 0, 1) * value
}

// Use the function
projectValue = calculateEV(0.3, lognormal(1e9, 2))

Multi-Variable Fermi Estimate:

// Estimate: Number of piano tuners in Chicago
chicagoPopulation = 2.7M to 2.9M
householdsPerPerson = 0.35 to 0.45
pianoOwnershipRate = 0.02 to 0.05
tuningsPerYear = 0.5 to 2
hoursPerTuning = 1.5 to 2.5
workingHoursPerYear = 1800 to 2200

totalTunings = chicagoPopulation * householdsPerPerson *
               pianoOwnershipRate * tuningsPerYear
tunerCapacity = workingHoursPerYear / hoursPerTuning
numberOfTuners = totalTunings / tunerCapacity

Version History

Squiggle 0.10.0 Deep Dive

The January 2025 release of Squiggle 0.10.0 represented six months of development with significant architectural changes:

Web Workers: All Squiggle code now runs in a separate Web Worker thread by default, with results marshaled back asynchronously. This prevents UI freezes during complex calculations.

Type System: New compile-time type inference transforms AST to typed AST, enabling earlier error detection. The pipeline now includes semantic analysis for type checks.

Unit Type Annotations (experimental, contributed by Michael Dickens): Variables can be annotated with physical units like kilograms, dollars, or compound units like m/s^2.

UI Changes: The output viewer now defaults to collapsed variables. Use the @startOpen decorator to expand variables by default.

Strengths and Limitations

Strengths:

• Simple, readable syntax optimized for probabilistic math
• Fast for small-to-medium models
• Strong for rapid prototyping
• Optimized for numeric and symbolic approaches, not just Monte Carlo
• Embeddable in JavaScript applications
• Free and open-source (MIT license)

Limitations:

• Does not support Bayesian inference (cannot do backwards inference from data)
• Much slower than languages like Stan or PyMC on large models
• Limited scientific computing ecosystem
• Beta distributions display poorly when alpha or beta are below 1.0

SquiggleAI

SquiggleAI integrates large language models to assist with probabilistic model creation, addressing a key challenge QURI identified: even highly skilled domain experts frequently struggle with basic programming requirements for probabilistic modeling.

How It Works

SquiggleAI uses prompt caching to cache approximately 20,000 tokens of information about the Squiggle language, ensuring the LLM has deep knowledge of syntax, distributions, and best practices. Most workflows complete within 20 seconds to 3 minutes, producing models typically 100-500 lines long depending on the model used.

Model Support

Capabilities

SquiggleAI Outputs

SquiggleAI outputs on Squiggle Hub are private by default. Users who want to share models or make them public can explicitly do so by creating new public models. This privacy-first approach encourages experimentation without concern about incomplete drafts being visible.

Integration with Squiggle Hub

SquiggleAI is directly accessible from within Squiggle Hub, allowing users to generate models and immediately save, version, and share them through the platform. The tight integration reduces friction between ideation and publication.

Squiggle Hub

Squiggle Hub is a platform for creating, sharing, and collaborating on Squiggle models. It was announced in 2024 as a central repository for probabilistic models in the EA and rationalist communities.

Core Features

Groups and Collections

Squiggle Hub organizes models into groups for easier discovery:

• Meta-Squiggle: Models about Squiggle itself and QURI operations
• Innovation: Models exploring novel estimation techniques
• User Collections: Individual user portfolios

Notable Models and Use Cases

The platform hosts various types of probabilistic models:

Guesstimate Legacy

Squiggle Hub also provides access to 17,000+ public models from Guesstimate, Gooen's earlier Monte Carlo spreadsheet tool. This archive represents years of probabilistic modeling by the EA community and serves as a reference library for common estimation patterns.

Metaforecast

Metaforecast aggregates forecasts from multiple prediction platforms into a single searchable interface. The initial version was created by Nuño Sempere, with help from Ozzie Gooen, at QURI.

Purpose and Design

Forecasting is a public good, but platform fragmentation reduces its utility. Metaforecast addresses this by combining data from 10+ platforms into a unified search interface. Data is fetched daily, showing immediate forecasts without historical data—optimized for quick lookups rather than trend analysis.

Platforms Aggregated

Scale: Currently indexes approximately 2,100 active forecasting questions across all platforms, plus access to 17,000+ public Guesstimate models.

Features

Integrations

Metaforecast has been integrated with several external services:

• Twitter: Bot posting notable forecasts
• Fletcher: Discord bot integration
• GlobalGuessing: Forecasting community platform
• Elicit (previously): AI research assistant

The open-source nature and GraphQL API enable custom integrations for research and analysis workflows.

2024 US Election Performance

During the 2024 US presidential election, Polymarket (one of Metaforecast's sources) demonstrated the value of prediction market aggregation. Polymarket strongly favored Trump's victory even as traditional polls showed a closer race. The market's prediction proved correct, with reports of approximately $30 million in bets from individual traders helping shape the odds.

Additional Tools

RoastMyPostProjectRoastMyPostRoastMyPost is an LLM tool (Claude Sonnet 4.5 + Perplexity) that evaluates written content through multiple specialized AI agents—fact-checking, logical fallacy detection, math verification, and mo...Quality: 35/100

RoastMyPostProjectRoastMyPostRoastMyPost is an LLM tool (Claude Sonnet 4.5 + Perplexity) that evaluates written content through multiple specialized AI agents—fact-checking, logical fallacy detection, math verification, and mo...Quality: 35/100 is a QURI application that uses LLMs and code to evaluate blog posts and research documents. It was announced as a tool for improving writing quality in the EA and rationalist communities.

Evaluators Available:

How It Works:

1. Import document via markdown text or URL (optimized for EA Forum and LessWrong)
2. Select evaluators (system-recommended or custom)
3. Processing takes 1-5 minutes
4. Review flagged issues for human judgment

Use Cases:

• Draft polishing: Catch errors before publication
• Public trust signaling: Link to evaluations in blog posts (like GitHub badges)
• LLM-assisted workflows: Automated quality checks on AI-generated content

Limitations: The false positive rate for error detection is significant. RoastMyPostProjectRoastMyPostRoastMyPost is an LLM tool (Claude Sonnet 4.5 + Perplexity) that evaluates written content through multiple specialized AI agents—fact-checking, logical fallacy detection, math verification, and mo...Quality: 35/100 is best for flagging issues for human review, not for treating results as authoritative. Fact Check and Fallacy Check work best on fact-dense, rigorous articles.

Fermi Competition

QURI runs periodic $100 Fermi Model Competitions to encourage creative Fermi estimation using AI tools. The February 2025 competition used Claude 3.5 Sonnet and the QURI team as judges.

Evaluation Criteria:

The competition explicitly encourages novel approaches over exhaustively researched calculations, promoting experimentation with AI-assisted quantitative reasoning.

Squigglepy (Python Implementation)

Squigglepy is a Python package developed by Rethink Priorities that implements Squiggle-like functionalities for those who prefer working in the Python ecosystem. While Squiggle is implemented in JavaScript, Squigglepy allows integration with Python's scientific computing stack (NumPy, SciPy, pandas).

Funding and Organization

Funding History

Funding Context

The Survival and Flourishing Fund (SFF) is financed primarily by Jaan Tallinn (Skype co-founder, AI safety advocate). In 2025, SFF allocated $34.33 million to organizations working on existential risk, with ~86% ($29M) going to AI-related projects. QURI represents one of several epistemic infrastructure investments in SFF's portfolio.

The Future Fund's 2022 grant was affected by the FTX collapse, which eliminated potential future funding from that source. QURI has since diversified funding through LTFF and individual donors.

Organizational Structure

Rethink Priorities Fiscal Sponsorship

QURI works with Rethink Priorities under their Special Projects Program, which provides:

• Fiscal sponsorship and tax-exempt status
• Financial administration and payroll
• Recruitment support
• Operational infrastructure

This model allows QURI to focus on tool development while RP handles administrative overhead. The arrangement is common in the EA ecosystem for small research organizations.

Research Collaborations

Arb Research Collaboration

QURI collaborated with Arb Research on their 2025 technical AI safety review, building the interactive shallowreview.ai website. The Shallow Review is "a shallow-by-design review of technical AI safety research in 2025: 800+ papers and posts across 80+ research agendas."

The website was created by QURI (Ozzie and Tomáš) to make the research more navigable. The review covers alignment, control, capability restraint, and risk awareness work, processing every arXiv paper on alignment and all Alignment Forum posts. The project was funded by Coefficient Giving.

GiveWell Cost-Effectiveness Quantification

Multiple projects have used Squiggle to add uncertainty quantification to GiveWell's cost-effectiveness analyses:

These projects demonstrate Squiggle's utility for making cost-effectiveness analyses more transparent and explorable.

EA/Rationalist Community Integration

QURI tools are widely used across the effective altruism and rationalist communities:

Comparison with Similar Tools

When to Use Squiggle vs. Alternatives

Use Squiggle when:

• You need intuition-driven estimation without much data
• Rapid prototyping of uncertainty models is priority
• Web-based sharing and collaboration is important
• You want readable, auditable probabilistic code

Use Stan/PyMC when:

• You have data and need Bayesian inference
• Model complexity requires advanced MCMC methods
• Performance on large models is critical

Use Guesstimate when:

• Spreadsheet interface is preferred
• Quick one-off calculations
• Non-programmers need to contribute

Use Cases

Cost-Effectiveness Analysis

Organizations use Squiggle for intervention comparisons with explicit uncertainty:

The GiveWell CEA quantification project demonstrated that Squiggle can make charity evaluations more transparent by showing full probability distributions rather than point estimates.

Forecasting Models

Squiggle enables structured forecasts with explicit uncertainty:

• AI timeline models: When will specific capabilities emerge?
• Technology adoption curves: S-curves with uncertainty bounds
• Risk scenario analysis: Probability-weighted outcome trees
• Market sizing: Fermi estimates for business planning

Research Communication

Squiggle models embedded in publications enable:

• Transparent assumptions: Every input visible and adjustable
• Reproducible calculations: Same code produces same outputs
• Interactive exploration: Readers can modify parameters
• Sensitivity analysis: Identify which inputs matter most

Academic and Policy Applications

While primarily used in EA/rationalist communities, Squiggle has potential applications in:

• Academic research requiring uncertainty quantification
• Policy analysis requiring transparent assumptions
• Risk assessment in regulated industries
• Teaching probabilistic reasoning

QURI Timeline

Strengths and Limitations

Organizational Strengths

• Purpose-built tools: Each product addresses specific epistemic needs
• Accessible: Browser-based, no installation needed
• Open Source: All code MIT licensed, community contributions welcome
• AI Integration: SquiggleAI and RoastMyPostProjectRoastMyPostRoastMyPost is an LLM tool (Claude Sonnet 4.5 + Perplexity) that evaluates written content through multiple specialized AI agents—fact-checking, logical fallacy detection, math verification, and mo...Quality: 35/100 leverage frontier LLMs
• Community: Active EA/rationalist user base, responsive to feedback
• Ecosystem approach: Tools work together (Squiggle + Hub + AI + Metaforecast)

Organizational Limitations

• Niche adoption: Limited use outside EA/rationalist communities
• Small team: ~3-5 core contributors limits development velocity
• Funding dependency: Reliant on EA-adjacent funders (SFF, LTFF)
• Learning curve: New syntax requires investment to learn
• Documentation gaps: Some features under-documented
• Ecosystem size: Fewer libraries than general-purpose languages

Strategic Challenges

Future Directions

Based on QURI's development trajectory and stated priorities:

Near-Term (2025-2026)

• Squiggle language improvements: Continued type system development, performance optimization
• SquiggleAI expansion: Support for more LLM providers, longer context windows
• Squiggle Hub features: Better collaboration tools, API improvements
• Community building: More Fermi competitions, educational content

Medium-Term

• Academic adoption: Partnerships with universities for teaching probabilistic reasoning
• Policy applications: Tools for government cost-benefit analysis
• Integration depth: Tighter connections between Squiggle, Metaforecast, and RoastMyPostProjectRoastMyPostRoastMyPost is an LLM tool (Claude Sonnet 4.5 + Perplexity) that evaluates written content through multiple specialized AI agents—fact-checking, logical fallacy detection, math verification, and mo...Quality: 35/100

Strategic Questions

• Can QURI expand beyond the EA community without losing focus?
• How should the tools evolve as general LLMs become better at estimation?
• What role should QURI play in AI safety evaluation/forecasting infrastructure?

Sources and Citations

Primary Sources

• QURI Official Website
• QURI About Page
• QURI Team Page
• Ozzie Gooen EA Forum Profile
• Ozzie Gooen Interview (Mutual Understanding)

Squiggle Documentation

• Squiggle Language Documentation
• Squiggle Distribution Creation Guide
• Squiggle Functions Guide
• Squiggle 0.10.0 Release Notes
• Squiggle GitHub Repository

EA Forum Posts

• Introducing Squiggle: Early Access
• Introducing SquiggleAI
• Announcing RoastMyPostProjectRoastMyPostRoastMyPost is an LLM tool (Claude Sonnet 4.5 + Perplexity) that evaluates written content through multiple specialized AI agents—fact-checking, logical fallacy detection, math verification, and mo...Quality: 35/100
• Introducing Metaforecast
• $100 Fermi Model Competition
• Squiggle: Why and How to Use It
• GiveWell CEA Quantification
• QURI EA Forum Topic

Funding and Organization

• Survival and Flourishing Fund
• SFF 2025 Funding Announcement
• QURI Charity Navigator Profile
• Rethink Priorities Special Projects

Collaborations

• Shallow Review 2025 (shallowreview.ai)
• Squigglepy (Rethink Priorities)

External Links

• QURI Website
• Squiggle Language
• Squiggle Hub
• Metaforecast
• RoastMyPostProjectRoastMyPostRoastMyPost is an LLM tool (Claude Sonnet 4.5 + Perplexity) that evaluates written content through multiple specialized AI agents—fact-checking, logical fallacy detection, math verification, and mo...Quality: 35/100
• GitHub Organization
• QURI Substack
• EA Forum Topic
• QURI Donate Page