ScienceToStartup
DevelopersTrends

113 Cherry St #92768

Seattle, WA 98104-2205

Backed by Research Labs
All systems operational

Proof

  • Proof Layer
  • Dashboard
  • Example paper page
  • Signal Canvas
  • Topic proof layer
  • Benchmark scoreboard
  • Public dataset
  • Evidence
  • Workspace
  • Terminal
  • Talent Layer
  • Build Loop

Developers

  • Overview
  • Start Here
  • REST API
  • MCP Server
  • Examples
  • OpenAI Guide
  • API Docs

Trends

  • Live Trends Desk
  • Operator Cycle
  • Founder Brief
  • Benchmark Movers

Resources

  • Resources Hub
  • All Resources
  • Benchmark
  • Database
  • Dataset
  • Calculator
  • Glossary
  • State Reports
  • Industry Index
  • Directory
  • Templates
  • Alternatives
  • Topics

Company

  • Articles
  • Changelog
  • About
  • Careers
  • Enterprise
  • Scout
  • RFPs
  • For Media
  • FAQ
  • Privacy Policy
  • Legal
  • Contact
ScienceToStartup

Copyright © 2026 ScienceToStartup. All rights reserved.

Privacy Policy|Legal
  1. Home
  2. Signal Canvas
  3. When simulations look right but causal effects go wrong: Lar
← Back to Paper

When simulations look right but causal effects go wrong: Large language models as behavioral simulators

Fresh8d ago
Export BriefOpen in Build LoopConnect with Author
View PDF ↗
Viability
0.0/10

Compared to this week’s papers

Evidence fresh

Use This Via API or MCP

Use Signal Canvas as the narrative proof surface

Signal Canvas is the citation-first public layer for turning one paper into a structured commercialization narrative. Use it to hand off into REST, MCP, Build Loop, and launch-pack execution without losing source lineage.

Signal Canvas APIPaper Proof PageOpen Build LoopLaunch Pack Example

Evidence Receipt

Freshness: 2026-04-06T20:16:59.808527+00:00

Claims: 0

References: 0

Proof: unverified

Freshness: fresh

Source paper: When simulations look right but causal effects go wrong: Large language models as behavioral simulators

PDF: https://arxiv.org/pdf/2604.02458v1

Source count: 0

Coverage: 0%

Last proof check: 2026-04-06T20:16:59.808Z

Paper Conversation

Citation-first answers with explicit evidence receipts, disagreement handling, commercialization framing, and next actions.

Paper Mode

When simulations look right but causal effects go wrong: Large language models as behavioral simulators

Overall score: 5/10
Lineage: 99dfcedae190…
Cmd/Ctrl+K
Search the latest paper corpus with startup-focused AI synthesis.

Canonical Paper Receipt

Last verification: 2026-04-06T20:16:59.808Z

Freshness: fresh

Proof: unverified

Repo: missing

References: 0

Sources: 0

Coverage: 0%

Missingness
  • - paper_evidence_receipts.references_count
  • - paper_evidence_receipts.coverage
Unknowns
  • - Canonical evidence receipt has not been materialized yet.

Mode Notes

  • Corpus mode searches the research corpus broadly.
  • Paper mode pins trust state to the canonical paper kernel.
  • Workspace mode blends saved sources, prior evidence queries, and linked papers.

Starting…

Dimensions overall score 5.0

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Claim extraction is still pending for this paper. Check back after the next analysis run.

Competitive landscape

Competitor map is still being generated for this paper. Enable generation or check back soon.

Keep exploring

Builds On This
This human study did not involve human subjects: Validating LLM simulations as behavioral evidence
Score 3.0down
Builds On This
Restoring Heterogeneity in LLM-based Social Simulation: An Audience Segmentation Approach
Score 3.0down
Builds On This
LLMs as Strategic Actors: Behavioral Alignment, Risk Calibration, and Argumentation Framing in Geopolitical Simulations
Score 3.0down
Builds On This
Social Meaning in Large Language Models: Structure, Magnitude, and Pragmatic Prompting
Score 3.0down
Builds On This
Bayesian Elicitation with LLMs: Model Size Helps, Extra "Reasoning" Doesn't Always
Score 4.0down
Builds On This
Evaluating LLM-Simulated Conversations in Modeling Inconsistent and Uncollaborative Behaviors in Human Social Interaction
Score 2.0down
Builds On This
Benchmarking Political Persuasion Risks Across Frontier Large Language Models
Score 4.0down
Higher Viability
Towards Simulating Social Media Users with LLMs: Evaluating the Operational Validity of Conditioned Comment Prediction
Score 6.0up

Startup potential card

Startup potential card preview
Share on XLinkedIn

Related Resources

  • What are the limitations of current LLM evaluation methods when assessing long-tail knowledge acquisition?(question)
  • What specific data science tasks does the DSAEval benchmark focus on for LLM evaluation?(question)
  • How does scenario diversity in AI benchmarking contribute to more robust LLM evaluations?(question)

BUILDER'S SANDBOX

Build This Paper

Use an AI coding agent to implement this research.

OpenAI Codex
OpenAI CodexAI Agent

Lightweight coding agent in your terminal.

Claude Code
Claude CodeAI Agent

Agentic coding tool for terminal workflows.

AntiGravity IDE
AntiGravity IDEScaffolding

AI agent mindset installer and workflow scaffolder.

Cursor
CursorIDE

AI-first code editor built on VS Code.

VS Code
VS CodeIDE

Free, open-source editor by Microsoft.

Recommended Stack

PyTorchML Framework
FastAPIBackend
TensorFlowML Framework
JAXML Framework
KerasML Framework

Startup Essentials

Antigravity

AI Agent IDE

Render

Deploy Backend

Railway

Full-Stack Deploy

Supabase

Backend & Auth

Vercel

Deploy Frontend

Firebase

Google Backend

Hugging Face Hub

ML Model Hub

Banana.dev

GPU Inference

Estimated $10K - $14K over 6-10 weeks.

MVP Investment

$10K - $14K
6-10 weeks
Engineering
$8,000
GPU Compute
$800
LLM API Credits
$500
SaaS Stack
$300
Domain & Legal
$100

6mo ROI

0.5-1x

3yr ROI

6-15x

GPU-heavy products have higher costs but premium pricing. Expect break-even by 12mo, then 40%+ margins at scale.

See exactly what it costs to build this -- with 3 comparable funded startups.

7-day free trial. Cancel anytime.

Talent Scout

Find Builders

LLM experts on LinkedIn & GitHub

Discover the researchers behind this paper and find similar experts.

7-day free trial. Cancel anytime.