M: Is there any research available online dealing with the probability of a particular story coming close to a real-life story, kind of in a way that's related to perception bias? What I'm talking about is stemming from a discussion of the movie Eyes Wide Shut in the light of the Epstein files and all of that information coming out, and people acting like maybe the director was killed because he was doing an exposé. And I was just wondering in terms of absolute numbers, like how many... I'm wondering how many, if there has been any investigation numerically of the number of stories that have to be released as movies or TV shows or whatever each year in order for one of them to be accurate, or vice versa. I'm not sure if I'm saying this well, but I hope you're understanding what my question is.
G: Yes—what you’re asking about actually sits at the intersection of probability theory, cognitive psychology, and statistics of coincidence. There is research related to it, though it’s rarely framed exactly as “movies predicting real events.” Instead, scholars study three overlapping ideas:
Together they explain why something like Eyes Wide Shut seeming to “predict” Epstein-like scandals is almost inevitable if enough stories exist.
If thousands of stories are produced every year about power, crime, conspiracies, and elites, the odds that some of them resemble real events later are extremely high—even if none of them had insider knowledge. This effect is explained by the Law of Really Large Numbers and our brain’s tendency to see patterns (apophenia).
One of the clearest explanations comes from statistician David J. Hand in The Improbability Principle.
The core idea:
Extremely unlikely events become likely when there are enough opportunities for them to happen. citeturn0search2turn0search23
If you think about fiction:
Over decades that becomes millions of narratives.
Even if the probability of a fictional plot resembling a later real event were 1 in 1,000,000, millions of stories mean:
So when one appears to “predict reality,” it feels shocking—but statistically it is almost guaranteed.
Psychology explains why these coincidences feel meaningful.
The key concept is apophenia:
Humans evolved to detect patterns quickly—even false ones—because it was safer to assume a pattern than miss a predator. citeturn0search22
In modern contexts this leads to:
because people connect unrelated events into a narrative.
Researchers often use dream-prediction examples to illustrate the math.
One analysis estimated:
Nothing supernatural happened—there were simply thousands of chances for coincidence.
Fiction works the same way.
There’s also a more mundane explanation.
Writers often extrapolate from existing reality:
So when a scandal later emerges, fiction may look prophetic when it was actually just an informed guess about human behavior.
A good rule:
Fiction rarely predicts specific events, but it often predicts types of events.
That film hits several narrative elements that recur in real scandals:
Those themes appear constantly in fiction because they are common in history.
So when a case like Epstein appears, people retroactively connect it to the film.
A simplified version would look like this:
Variables:
The expected number of matches roughly grows like:
[ Matches \approx N \times P(\text{story resembles event}) ]
As N increases, matches become inevitable.
This is the same math behind:
Several overlapping concepts describe what you’re asking about:
Together they explain why fiction-reality overlaps feel eerie but are statistically normal.
✅ Your intuition about “how many stories must exist before
one matches reality” is exactly the right framing.
That is basically the Law of Really Large Numbers applied to
storytelling.
If you want, I can also show you a back-of-the-envelope calculation using the number of movies and TV shows produced each year to estimate how often a “prophetic” narrative should statistically appear. That actually produces surprisingly high numbers.
M: Yeah, I'm glad that you can see the idea I was pointing at, and that was a very thorough description of the question, but can you give me any actual numbers to work with?
G: Yes. We can actually put ballpark numbers on this using a very simple probabilistic model. It won’t be perfect, but it gives a realistic scale.
I’ll walk through a back-of-the-envelope calculation based on how many stories exist and how often events occur.
Rough global production per year (rounded):
| Medium | Approx per year |
|---|---|
| Movies | ~7,000 worldwide |
| Scripted TV shows | ~500–700 |
| Novels | ~300,000+ |
| Short stories / web fiction | millions |
If we restrict ourselves to major narratives people actually encounter:
≈ 10,000 new stories per year
Over 50 years:
[ 10,000 \times 50 = 500,000 \text{ stories} ]
Half a million narratives.
Most stories use recurring narrative components like:
If we assume just 20 such elements, a story may contain several of them.
The number of combinations of only 3 elements is:
[ \binom{20}{3} = 1140 ]
So there are ~1,000 distinct thematic combinations just at that level.
With half a million stories produced, each combination appears hundreds of times.
Examples (rough scale):
Across 50 years, we easily get thousands of real events.
We can approximate expected overlaps.
Let:
Even if the resemblance probability is extremely small:
[ P = 1/1,000,000 ]
Expected matches:
[ S \times E \times P ]
[ 500,000 \times 5,000 \times \frac{1}{1,000,000} ]
[ = 2,500 ]
So thousands of overlaps are expected statistically.
Most are never noticed.
People only notice the dramatic ones.
The brain filters coincidences.
Out of thousands of overlaps:
Those become internet myths.
Examples people often cite:
But the unnoticed misses are far larger.
If 500,000 stories exist, even a 1 in 100,000 coincidence will occur:
[ 500,000 / 100,000 = 5 ]
So you’d expect five eerie coincidences even with extremely small probabilities.
Two extra biases strengthen it:
People remember the one match and ignore thousands of non-matches.
People adjust details until the match feels closer.
For example:
But the details differ enormously.
A realistic statistical expectation:
So the existence of a film resembling a later scandal is not rare at all.
It’s almost inevitable.
One interesting twist: the most accurate fictional predictions tend to come from writers who are simply extrapolating existing trends, not from secret knowledge. Kubrick, for example, was adapting Arthur Schnitzler’s 1926 novella about elite sexual secrecy — meaning the idea already existed long before Epstein.
So the film isn’t predicting something new; it’s reflecting a recurring human pattern.
If you want, I can also show you a strange statistical effect involving “prophetic fiction” where the odds actually guarantee that several movies per decade will appear to predict future scandals almost exactly. It’s a weird consequence of the birthday paradox.
M: And the real pain in the ass is that even with all of that information and complete agreement by me in terms of how unlikely it was that it was directly a correlation that somehow Eyes Wide Shut was related to the Epstein files, probability also says that you can't completely rule it out, at least not at face value.
G: Yes. And that irritation you’re feeling is actually a correct application of probability theory, not a flaw in the reasoning.
The short version:
Statistics can make something extremely unlikely, but almost never reduce the probability to exactly zero.
That’s an unavoidable property of probabilistic reasoning.
You are correct:
Even when statistics strongly suggest a coincidence, you cannot
rule out the causal explanation purely with probability. You
can only say it is overwhelmingly less likely than
coincidence.
The correct framework here is Bayesian inference, which compares competing explanations.
The key relationship is:
genui{"math_block_widget_common_keywords": {"content": "P(H|E)=\frac{P(E|H)P(H)}{P(E)}"}}
Where:
What matters is not just the resemblance but also the prior probability.
We can simplify the competing explanations:
H₁: coincidence from many stories
H₂: Kubrick knew about a real elite trafficking
network
Now consider rough priors.
Probability a random film resembles a later scandal:
Probability Kubrick had secret knowledge and encoded it in a studio film:
Even though both are non-zero, the relative odds favor coincidence by orders of magnitude.
Conspiracy reasoning tends to stop at:
“You can’t prove it didn’t happen.”
Which is technically correct.
But Bayesian reasoning asks:
“Which explanation is more probable given everything else we know?”
And the answer is almost always the mundane one.
Imagine someone wins the lottery.
You cannot rule out that:
But the probability that:
is vastly higher.
So we accept the mundane explanation.
Humans prefer binary conclusions:
But probability theory operates on relative likelihood, not certainty.
So the honest conclusion often sounds unsatisfying:
“It’s possible, but extremely unlikely.”
Scientific conclusions almost never say:
“This cannot happen.”
Instead they say things like:
Absolute impossibility is rare outside pure mathematics.
There’s also a third explanation people often miss:
Shared underlying reality.
Kubrick didn’t need insider knowledge if:
In that case the film isn’t predicting Epstein.
It’s describing a pattern that already existed.
So your frustration is actually a sign you’re reasoning correctly:
M: And then he dies four days after it's supposed to be released, but at that point he hadn't necessarily shown it to anyone, let alone anyone who would be able to report to any sex cabal in a way that would let them respond that quickly. Well, from my personal experience, I know that an older person undergoing stress can get some serious health issues, either as the stress builds or once the stress is released.
G: You’re basically circling the right statistical framework again: coincidences cluster, and when several unusual things occur near each other (a provocative film, a famous director, a sudden death) the mind wants a causal link. Probability doesn’t forbid a link—but it also shows why coincidences like this occur more often than intuition suggests.
A few concrete facts help anchor the numbers.
entity["people","Stanley Kubrick","American film director (1928–1999)"] died on March 7, 1999 at age 70, from a heart attack.
For men around age 70 in the U.S.:
That means in any given year:
Those are not tiny probabilities.
If you compress a ~2.5% annual mortality risk into a week, the approximate probability becomes:
[ P(\text{death in week}) \approx \frac{0.025}{52} \approx 0.00048 ]
So about:
0.05% per week
≈ 1 in 2,000
That sounds rare—but now consider scale.
If we roughly estimate:
Then each year:
[ 20{,}000 \times 0.025 \approx 500 ]
About 500 deaths per year in that demographic alone.
Which means:
Some of those deaths will inevitably occur near major releases, premieres, or controversies.
Your intuition about stress is also medically grounded.
Stress can contribute to:
There’s even a known phenomenon sometimes called “post-stress crash”—a major health event occurring after a long project ends or pressure drops.
Large film productions are extremely stressful, and Kubrick was known for:
So the mundane explanation (age + stress + baseline risk) already fits the event.
Another perception bias is narrowing the time window after the fact.
Humans do this unconsciously.
Example:
If he had died:
people might still say:
“Right after finishing that film…”
The mind compresses time when building a narrative.
Kubrick had already screened a cut of the film for executives and actors a few days earlier. The movie wasn’t some hidden secret.
So if a powerful group had wanted to stop the film, the easier action would have been:
Which actually did happen to some extent after his death.
From a probability standpoint the combined explanation:
makes the event statistically unsurprising, even if emotionally eerie.
The conspiracy explanation remains possible but enormously less probable.
And your observation about stress-related health crashes after finishing a huge project is actually one of the most realistic pieces of the puzzle.