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wtf is imario-benchmark?

laugherxiao/imario-benchmark — explained in plain English

Analysis updated 2026-05-18

1PythonAudience · researcherComplexity · 2/5LicenseSetup · easy

TL;DR

An open benchmark and scoring script that lets anyone independently verify how accurately a synthetic survey engine predicts real human survey answers.

Mindmap

mindmap
  root((imario-benchmark))
    What it does
      Verifies synthetic survey accuracy
      Reproducible scoring script
      Publishes per person evidence
    Tech stack
      Python
      Standard library only
    Use cases
      Reproduce accuracy numbers
      Audit vendor claims
      Rebuild question bank
    Audience
      Researchers
      Data scientists
      Survey tool buyers

Code map

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Why would anyone build with this?

REASON 1

Run score.py to independently reproduce the published accuracy numbers from raw per-person answers.

REASON 2

Audit whether a synthetic-audience survey tool's accuracy claims hold up against real human survey distributions.

REASON 3

Rebuild the original question bank from official Pew, CGSS, ANES, or Stack Overflow survey downloads.

REASON 4

Compare accuracy across novel, trend, and covered question tiers to see where the engine is weakest.

What's in the stack?

Python

How it stacks up

laugherxiao/imario-benchmark0xustaz/streamgatea-bissell/unleash-lite
Stars111
LanguagePythonPythonPython
Setup difficultyeasyhardhard
Complexity2/54/54/5
Audienceresearcherdeveloperresearcher

Figures from each repo's GitHub metadata at analysis time.

How do you spin it up?

Difficulty · easy Time to first run · 5min

Rebuilding the question bank from raw microdata requires downloading licensed survey data separately.

Use freely, including commercially, as long as you keep the license notice and state any changes you made.

Wtf does this do

This repository is an open benchmark for checking how accurately a product called iMario can predict how real people would answer survey questions, using simulated respondents instead of actual human panels. Rather than just publishing a single accuracy number, it publishes the underlying data and a small script so that anyone can rerun the check themselves and confirm the published results independently. Running the included score.py file, which needs nothing beyond a standard Python install, recomputes the accuracy scores from the raw simulated answers and compares them against the real survey distributions. This is meant to prove the numbers are not just claimed but reproducible from evidence anyone can inspect, including thousands of individual simulated answers for each question, not just the final averages. The benchmark measures how close the distribution of simulated answers is to the distribution of real human answers on the same question, across eleven countries and one open ended question set. It groups questions into three honesty tiers: brand new questions with no prior data to learn from, questions repeated from an earlier year, and questions with a very recent prior answer to draw on, so that easier and harder cases are not blended together misleadingly. Its source survey question banks come from well known public research sources such as Pew Research and the Stack Overflow Developer Survey, and the underlying licensed survey microdata itself is not included, only extraction scripts to rebuild the question bank from official downloads. This repository is for verifying published accuracy claims only. It does not let you run the actual simulated survey engine on your own questions, that requires a separate paid product. It is aimed at researchers, data scientists, and buyers evaluating synthetic survey tools who want to audit accuracy claims rather than take a vendor's word for it. The code is released under the Apache 2.0 license.

Yoink these prompts

Prompt 1
Clone this repo and walk me through running score.py to reproduce the published accuracy numbers.
Prompt 2
Explain the difference between the raw 1-TV and 1-MAE accuracy metrics used in this benchmark.
Prompt 3
Help me understand what the novel, trend, and covered question tiers mean in this benchmark.
Prompt 4
Show me how to rebuild the Pew Research question bank using the extraction scripts in data/extraction.
Prompt 5
Explain what evidence in verification/ makes this benchmark's numbers hard to fake.

Frequently asked questions

wtf is imario-benchmark?

An open benchmark and scoring script that lets anyone independently verify how accurately a synthetic survey engine predicts real human survey answers.

What language is imario-benchmark written in?

Mainly Python. The stack also includes Python.

What license does imario-benchmark use?

Use freely, including commercially, as long as you keep the license notice and state any changes you made.

How hard is imario-benchmark to set up?

Setup difficulty is rated easy, with roughly 5min to a first successful run.

Who is imario-benchmark for?

Mainly researcher.

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