Two Envelopes Paradox Calculator

Two Envelopes Paradox

Explore the classic probability puzzle where switching between two envelopes appears to give infinite expected value — revealing hidden assumptions in conditional probability reasoning.

Last updated: March 2026

Simulation

One envelope contains x, the other contains 2x

Number of times to repeat the experiment

Average Payoffs (10000 trials)

Stay Strategy

$149.97

Wins: 4997

Switch Strategy

$150.03

Wins: 5003

Paradox Resolution

Both strategies converge to equal expected value. The paradox arises from conditioning on seeing a value A without knowing if it's the smaller or larger amount — this creates implicit probability assumptions that break the symmetry argument.

What is the Two Envelopes Paradox?

The Two Envelopes Paradox (also called the Exchange Paradox) is a famous probability puzzle. You're given two envelopes: one contains $x and the other contains $2x. You're shown the contents of one envelope, then asked whether you should stick with it or switch to the other.

The Paradoxical Argument:
If your envelope contains amount A, switching gives you:
E[switch | see A] = ½(2A) + ½(A/2) = 1.25A

This seems to suggest you should always switch — but both envelopes are chosen symmetrically, so neither should have an advantage! This apparent contradiction is the paradox.

Resolution:

The issue is that you don't know whether A is the smaller or larger amount. Conditioning on seeing A $\Rightarrow$ the prior probabilities of "A is smaller" and "A is larger" are not both ½. The implicit distribution of x matters: infinite expected value appears only under infinite or unbounded priors.

How to Analyze the Paradox

1.

Understand the Setup

One envelope has amount x, the other has 2x. You observe the contents of one envelope (call it A). The paradox asks: is switching always better?

2.

Apply Conditional Probability

When you see amount A, you must ask: what is P(A is the smaller amount | I see A)? This depends on the prior distribution of x, not just symmetry.

3.

Identify the Issue

The 'always switch' argument assumes P(small | see A) = P(large | see A) = ½, but this is only true under specific prior distributions — not universally.

4.

Simulate to Convince Yourself

Run many trials where x is fixed (e.g., $100) and envelopes are assigned randomly. You'll see both strategies average out equally, confirming no advantage.

5.

Relate to Uninformative Priors

If you use a truly uninformative (improper) prior on x, the expected value calculations break down, revealing why the paradox arises mathematically.

Real-World Example

Scenario: A game show offers two prize envelopes. You're told one has twice the money of the other. The host shows you an envelope with $1,000. Should you switch?

Naive Paradox Reasoning:
If I switch, I get $500 or $2,000 with equal probability × expect $1,250. Switching seems better!

Expected value if I stay: $1,000

Expected value if I switch: 0.5 × $500 + 0.5 × $2,000 = $1,250

The Resolution: The above calculation is flawed. Before seeing $1,000, both envelopes had equal value by symmetry. Seeing $1,000 doesn't change that symmetry — the envelope you have and the other envelope are still interchangeable from your perspective. The apparent advantage comes from treating the conditional distribution as if it's uniform over the set {smaller, larger} — but you don't know the prior on x, so this assumption breaks down.

Frequently Asked Questions

Why is this called a paradox?
It seems like you should always switch and get more expected value, yet switching every time and staying every time give equal results. This apparent logical contradiction is the paradox.
Doesn't seeing a large amount make it more likely to be the smaller envelope?
Only if you know something about the distribution of x. Without knowing how x was chosen, seeing a value A doesn't change the prior symmetry between the envelopes.
What if the person setting up the envelopes chose x = some maximum amount / 2?
Then your conditional probability shifts, and you might have a true strategic advantage. The paradox highlights that envelope selection strategy matters — it's not just about the numbers.
How does this relate to decision theory?
It's a classic example of how unconditional expected value can mislead in decision-making. Without a proper prior on x and careful conditioning, naive calculations fail.
Can I use Bayes' Theorem to resolve it?
Yes. P(A is smaller | see A) = P(see A | A is smaller) × P(A is smaller) / P(see A). The key is specifying a proper prior on x.
What's the connection to improper priors?
If x follows an improper prior (e.g., uniform on [0, ∞)), expected values diverge. The paradox arises because conditioning on a value 'selects' part of an infinite distribution, creating apparent infinite advantage.

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