Agnotology Information-Flood Model
Recognize how information overload manufactures doubt instead of belief
- Difficulty
- Moderate
- Time to result
- ~weeks to results
- Steps
- 6
- Confidence
- 94%
The information-flood model explains a form of influence that does not need to prove a counterclaim. An interested actor repeatedly fills the public information space with arguments, commentary, uncertainty, and nominal sources until almost any prior belief can find apparent support. People can then stop searching before encountering contradictory evidence, while institutions delay action because the issue appears perpetually unsettled. The process converts information abundance into manufactured ignorance: more material produces less shared knowledge. To resist it, evaluate incentives, provenance, independence, and evidentiary quality rather than treating the number of available sources as a proxy for truth. Deliberately seek disconfirming evidence and distinguish original research from content that merely repeats or reframes another claim. The key diagnostic question is whether a campaign is trying to establish a fact or simply keep doubt alive.
Origin
Extracted from Masters in Business during Rex Sorgatz's explanation of agnotology, tobacco-company tactics, and modern online confirmation loops.
Core principles
- 01Propaganda can succeed without persuading anyone of one alternative claim
- 02Abundant conflicting material makes motivated confirmation easy
- 03Repeated doubt can weaken action even when evidence is strong
- 04The source and incentive behind information matter as much as its volume
How to run it
- 1
Map the interested actors
Identify the organizations or individuals producing and funding the disputed information. Note who benefits economically, politically, or legally if uncertainty continues.
Pro tip Look beyond the named author to funders, sponsors, and affiliated institutions.
Watch out An incentive signals a reason for scrutiny, not automatic proof that a claim is false.
- 2
Locate primary evidence
Trace prominent claims to original studies, records, or observations. Separate direct evidence from summaries, opinion pieces, and repeated citations.
Pro tip Build a source chain showing which items depend on the same original material.
Watch out Ten articles repeating one study do not constitute ten independent sources.
- 3
Test for information flooding
Examine whether the volume of material exceeds its evidentiary diversity. Look for repetitive claims, shifting objections, and continual demands for certainty.
Pro tip Group sources by underlying evidence rather than by publication title.
Watch out Do not mistake a genuinely active scientific debate for a coordinated doubt campaign.
- 4
Seek disconfirmation
Search intentionally for the strongest credible evidence against your current view. Compare its methods and independence with the material supporting your view.
Pro tip Formulate searches that do not contain your preferred conclusion.
Watch out Algorithmic personalization can keep returning material similar to what you already consumed.
- 5
Judge quality, not quantity
Weight evidence according to methodology, provenance, independence, and replication. Summarize what is established, disputed, and unsupported.
Pro tip Use a compact evidence table to prevent repetition from masquerading as corroboration.
Watch out A neat numerical score can conceal subjective judgments, so preserve the underlying rationale.
- 6
Identify the intended outcome
Decide whether the communication seeks to persuade, confuse, delay regulation, or preserve social permission for inaction. Respond to that mechanism rather than rebutting every low-quality claim individually.
Pro tip Ask what happens if the audience remains uncertain for another year.
Watch out Endless point-by-point rebuttal may amplify the flood and consume the critic's attention.
In the wild
A trade group funds commentary questioning dozens of small details in a strong safety consensus. An analyst traces the articles to a few shared sources, separates original studies from repetition, compares funding and methods, and discovers that the campaign offers no coherent alternative explanation—it only insists that more research is needed.
→ The analyst shows that the apparent breadth of disagreement is repetition designed to delay action.
Common mistakes
Counting links instead of evidence
A large search-result count can reflect duplication, affiliate content, or coordinated repetition rather than independent corroboration.
Trying to rebut every assertion
Answering an unlimited stream of weak claims can legitimize the campaign and distract from the strongest evidence.
Assuming all uncertainty is manufactured
Real research contains legitimate uncertainty, so intent and evidence must be examined before labeling disagreement as agnotology.
Is it for you?
Best for
It is best for journalists, researchers, policymakers, and consumers evaluating heavily contested public claims.
Not ideal for
It is not ideal for ordinary disagreements where participants are exchanging limited evidence in good faith.
From the transcript
“the science of creating stupidity.”
“it was actually just like continually bombarding it over and over and over and over again with more and more and more and more information.”
“any belief that you have, you can google it and you will find someone on the internet who has the similar belief”
From the episode
Rex Sorgatz Discusses the Mechanics of Misinformation (Podcast)
Rex Sorgatz