False Assumption Registry

About the False Assumption Registry

What is the False Assumption Registry?

The False Assumption Registry (FAR) is a documented record of widely held beliefs that turned out to be wrong, and the destructive policies and social harm they produced. Each entry documents the assumption itself, the people and institutions that promoted it, the early dissenters who warned against it, and the real-world consequences that followed.

The Thesis

The False Assumption Registry was built to test a simple idea: that many of our current social and political problems are not random misfortunes but downstream consequences of bad policies, and those bad policies were built on false assumptions. But the thesis goes one step further: false assumptions are not spontaneous errors. They originate with experts giving bad information, amplified by media and institutions until they become unchallenged mainstream belief. The chain runs: bad expert claims → unchallenged common knowledge (the false assumption) → bad policy → real-world harm.

The full thesis explores how moral convictions migrated into empirical science after the Enlightenment dismantled their philosophical foundations, why the replication crisis was a predictable consequence, and how both progressive and populist reactions follow from the same structural failure. Read the full thesis →

Quality Standards

To maintain objectivity, every entry included in this analysis and on the public site must meet quality thresholds. All entries require at least 3 independent sources, with at least one that is not classified as opinion (e.g., peer-reviewed research, primary source, or reputable journalism). Entries classified as "strong" (most experts agree it was false) or "contested" (a significant portion of experts think it was false) must meet these baseline standards. Entries classified as "emerging" (a small but growing group of experts) face a higher bar: they must include at least 3 hard-evidence sources–peer-reviewed studies, primary sources (government reports, court documents, official statistics), or reputable journalism–because claims with less expert support require harder evidence. Entries classified as "denied" (only fringe experts dissent) are excluded entirely. All entries remain in the raw database but are excluded from publication until they meet these criteria.

Reports

Beyond individual entries, FAR produces several reports that synthesize the registry's contents:

  • Summary of Findings: an AI-generated analysis of the registry's entries, testing the thesis above against the data and covering common themes, downstream consequences, responsibility, and worldview divides
  • Summary Report: a condensed table showing each false assumption alongside the policies it drove and the harm those policies caused, written in plain prose
  • Causal Chain Report: a detailed, searchable table of every documented causal chain from false assumption to policy to consequence, drawn directly from source claims

How entries are structured

Every entry in FAR includes:

  • The false assumption: the specific belief that was wrong
  • People involved: proponents, bad actors, early critics, and beneficiaries
  • Organizations involved: institutions that promoted or enforced it
  • Foundation: the arguments and sub-beliefs that propped it up
  • Propagation: how it spread through media, institutions, and culture
  • Policies & Harm: resulting policies and documented damage
  • Downfall: key events and evidence that exposed the assumption
  • Sources: articles, papers, and reporting that support the entry

Consensus levels

Not every entry in the registry represents a settled question. Each entry is tagged with a consensus level that reflects how widely the assumption is now recognized as false:

  • Strong: most experts agree this assumption was false
  • Contested: a significant portion of experts think it was false
  • Emerging: a small but growing and influential group of experts think it was false. Likely to grow to "contested" and then "strong" over time.
  • Denied: only fringe experts think it was false. Unlikely to gain wider traction.

These labels represent a trajectory: denied → emerging → contested → strong. Each level reflects a larger share of expert opinion recognizing the assumption as false. An entry marked "emerging" is one to watch; "contested" means the tide has turned significantly; "strong" means the question is effectively settled.

How Content Is Written

All content in the False Assumption Registry is found by FARAgent, an AI system built on large language models. Topics are automatically selected via web crawling: FARAgent crawls news sites, academic databases, and policy publications to identify false assumptions that caused policy failures, then locates relevant articles, peer-reviewed studies, government reports, and journalism on each topic, evaluates their credibility, and builds each entry’s source pool automatically. FARAgent does not invent claims or fabricate evidence; every factual assertion is tied to a cited, human-authored source, and direct quotes are reproduced verbatim from the original material.

From these sources, FARAgent extracts factual claims, attributes them to specific sources, and assembles them into structured, narrative entries. Humans can also submit topics and source material directly through the submission form.

Sources are reviewed by Justin West for credibility, relevance, and fair representation of the original material. Summaries are reviewed by Justin West for accuracy, balance, and faithful representation of the source evidence before publication. The AI finds the content and drafts the prose; the sources are human-authored; and the editorial judgment is human. Entries should be read as editorially reviewed, AI-synthesized summaries of human reporting, not as independent journalism. The quality of each entry depends on the quality of its sources. Where sources are strong, entries are strong. Where sources are thin, entries say less. FARAgent does not fill gaps with speculation.

Contributing

Know of a false assumption that led to real harm? New entries can be submitted through the submission form. You can also suggest additional sources for existing entries using the link at the bottom of any entry page. All submissions are reviewed before being added to the registry.