St Petersburg Paradox
A fair coin is flipped until a heads appears. You win $Y = 2^X$ dollars where $X$ is the number of flips to the first heads (including the heads). What is the expected value of this game?
$$ E[Y] = E[2^X] = \sum_{k=1}^\infty 2^k \left( \frac{1}{2} \right)^k = \sum_{k=1}^\infty 1 = \infty$$
How to make sense of this? Since there isn't an infinite amount of money, let's bound the payout to $2^{40}$ dollars which is more than a trillion.
Say that if it goes past $40$ tails in a row then he flees the country and you get $0$ dollars. So $Y = 2^X$ dollars if $X \le 40$ and $Y = 0$ if $X > 40$. Therefore
$$ E[Y] = \sum_{k=1}^{40} 2^k \left( \frac{1}{2} \right)^k + \sum_{k=41}^\infty 0 \cdot \left( \frac{1}{2} \right)^k= \sum_{k=1}^{40} 1 = 40 $$If he doesn't flee the country and will pay you your trillion dollars then the expected value is $41$ dollars. Note that here $Y = 2^X$ dollars if $X \le 40$ and $Y = 2^{40}$ if $X > 40$.
$$ E[Y] = \sum_{k=1}^{40} 2^k \left( \frac{1}{2} \right)^k + \sum_{k=41}^{\infty} 2^{40} \left( \frac{1}{2} \right)^k = \sum_{k=1}^{40} 1 + 1 = 41 $$since the geometric series $\frac{1}{2} + \frac{1}{4} + \frac{1}{8} + ... = 1$.
Articles
Personal notes I've written over the years.
- When does the Binomial become approximately Normal
- Gambler's ruin problem
- The t-distribution becomes Normal as n increases
- Marcus Aurelius on death
- Proof of the Central Limit Theorem
- Proof of the Strong Law of Large Numbers
- Deriving Multiple Linear Regression
- Safety stock formula derivation
- Derivation of the Normal Distribution
- Comparing means of Normal populations
- Concentrate like a Roman
- How to read a Regression summary in R
- Notes on Expected Value
- How to read an ANOVA summary in R
- The time I lost faith in Expected Value
- Notes on Weighted Linear Regression
- How information can update Conditional Probability
- Coupon collecting singeltons with equal probability
- Coupon collecting with n pulls and different probabilities
- Coupon collecting with different probabilities
- Coupon collecting with equal probability
- Adding Independent Normals Is Normal
- The value of fame during and after life
- Notes on the Beta Distribution
- Notes on the Gamma distribution
- Notes on Conditioning
- Notes on Independence
- A part of society
- Conditional Expectation and Prediction
- Notes on Covariance
- Deriving Simple Linear Regression
- Nature of the body
- Set Theory Basics
- Polynomial Regression
- The Negative Hyper Geometric RV
- Notes on the MVN
- Deriving the Cauchy density function
- Exponential and Geometric relationship
- Joint Distribution of Functions of RVs
- Order Statistics
- The Sample Mean and Sample Variance
- Probability that one RV is greater than another
- St Petersburg Paradox
- Drunk guy by a cliff
- The things that happen to us