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monty_hall_error
The Monty Hall error is the failure to correctly update conditional probabilities after structured information is revealed by a knowledgeable agent. In the classic problem, switching doors after the host (who knows where the prize is) reveals a goat doubles the probability of winning from 1/3 to 2/3. Most people — including statisticians — intuitively believe switching makes no difference.
A game show contestant picks Door 1. The host opens Door 3, revealing a goat. Intuition says the probability is now 50/50, but switching wins 2/3 of the time because the host's knowledge turns his choice into a signal about which door hides the car.
In a fraud investigation, a compliance officer narrows three suspicious accounts down to one flagged transaction. A senior auditor — who already knows which of the remaining two accounts is clean — points to one and says 'that one checks out.' The officer shrugs and says 'so it's 50/50 between the other two.' But because the auditor's reveal was informed, the originally flagged account is still twice as likely to be the source of fraud.
A doctor orders tests for three possible diagnoses. A specialist, having reviewed the full chart, rules out one of the two diagnoses the GP had not initially favored, saying 'it's definitely not condition B.' The GP concludes it must now be 50/50 between the original suspected condition A and condition C — but because the specialist's elimination was knowledge-based, not random, condition A remains far more probable than a naive 50/50 split suggests.
Binary (yes/no) questions an LLM must answer to identify this aspect:
Does the argument assume that conditional probabilities remain unchanged after new information is revealed?
Type: binaryIs the source of new information non-random (i.e., the revealer knows the correct answer)?
Type: binaryDoes the analysis fail to update prior probabilities in light of the information structure?
Type: binaryWould applying Bayes' theorem change the conclusion?
Type: binaryThe Monty Hall error is the failure to correctly update conditional probabilities after structured information is revealed by a knowledgeable agent. In the classic problem, switching doors after the host (who knows where the prize is) reveals a goat doubles the probability of winning from 1/3 to 2/3. Most people — including statisticians — intuitively believe switching makes no difference.
Human intuition treats the host's reveal as random, failing to account for the information content of a constrained choice. The asymmetry of knowledge between host and contestant is cognitively invisible.
Model the information structure explicitly. Ask who made a decision and what they knew at the time. Run simulations or apply Bayes' theorem to quantify the posterior probability after the reveal.
Variants of the Monty Hall structure appear in medical testing (ordered test results), legal reasoning (suspect elimination), and financial market event studies.
Use these tools to detect, analyze, or train this aspect.