An urn contains k black balls and a single red ball.
Peter and Paula draw without replacement balls from this urn,
alternating after each draw until the red ball is drawn. The game is won
by the player who happens to draw the single red ball. Peter is a
gentleman and offers Paula the choice of whether she wants to start or
not. Paula has a hunch that she might be better off if she starts; after
all, she might succeed in the first draw. On the other hand, if her
first draw yields a black ball, then Peter’s chances to draw the red
ball in his first draw are increased, because then one black ball is
already removed from the urn. How should Paula decide in order to
maximize her probability of winning?
Let’s solve this by looking at values of k. If
k is 0, Going first has a 100% chance of winning, and
second has a 0% chance. If k is 1, Going first has a 50%
chance of winning, and second has a 50% chance of winning. If
k is 2, Going first means you can take 2/3 of the balls vs
1/3 if you go second, so going first is better. If k is 3,
Going first or second means you take 2/4 balls, so this is also
50/50.
Thus, even and odd numbers of k are the only difference,
where if k is odd, then you have a 50/50 chance of winning
as either first or second, and if k is even, you have an
edge, so you should always go first.
Ten players participate in the first round of a tennis tournament: 2 females and 8 males. Five single matches are fixed at random by successively drawing, without replacement, the names of all 10 players from an urn: the player drawn first plays against the one whose name comes up second, the third against the fourth, etc. a. What is the probability that there will not be a single match involving two female players? Is this probability smaller, equal to, or larger than the corresponding probability with 20 females and 80 males? b. Try to answer the general case in which there are 2n players, of whom 2 ≤ k ≤ n are female. What is the probability p(k, n) that among the n matches there will not be a single one involving two female players
Peter and Paula play a simple game of dice, as follows. Peter keeps throw- ing the (unbiased) die until he obtains the sequence 1 − 1 in two successive throws. For Paula, the rules are similar, but she throws the die until she obtains the sequence 1 − 2 in two successive throws. a. On average, will both have to throw the die the same number of times? If not, whose expected waiting time is shorter (no explicit calculations are required)? b. Derive the actual expected waiting times for Peter and Paula
We can model this using a game tree for Paul:
p (\(\frac{1}{6}\)), Paul will roll a 1.
p (\(\frac{1}{6}\)) Paul will again roll a
1.1 - p (\(\frac{1}{6}\)), Paul won’t roll a 1 and
restart.1 - p, (\(\frac{5}{6}\)), Paul won’t roll a 1 and
restart.Thus, there are three outcomes:
The outcome that ends the game, 1-1 has a chance of \(p^2\) or (\(\frac{1}{36}\). The outcome that he rolls a 1 and then anything else is \(p * (1 - p)\) or \(\frac{5}{36}\). The outcome that restarts is \(1 - p\) or \(\frac{5}{6}\).
The first two outcomes take 2 rounds and the last one takes 1 round. Thus, the total waiting time is the time taken * the probability they occur:
\[ W = (1 + W) * (1 - p) + (2 + W) * p(1 - p) + 2p^2 \]
Simplifying:
\[ W = \frac{1 + p}{p^2} \]
When \(p = \frac{1}{6}\), W = 42.
For Paula’s case, we can model this as a markov chain:
There are three states, the starting state \(S_0\), a state for when the last throw was a 1 \(S_1\), and the final state of 1-2, \(S_2\).
\[ \begin{pmatrix} \frac{5}{6} & \frac{1}{6} & - \\ \frac{2}{3} & \frac{1}{6} & \frac{1}{6} \\ - & - & 1 \end{pmatrix} \]
Thus, we can find the expected waiting time for both \(S_0\) and \(S_1\) as follows:
\[ W_0 = 1 + \frac{5}{6} W_0 + \frac{1}{6} W_1 \]
\[ W_1 = 1 + \frac{2}{3} W_0 + \frac{1}{6} W_1 + \frac{1}{6} W_2 \]
This gives us W = 36, solving for \(W_0\).
Peter and Paula play a game of chance that consists of several rounds. Each individual round is won, with equal probabilities of $, by either Peter or Paula; the winner then receives one point. Successive rounds are independent. Each has staked $50 for a total of $100, and they agree that the game ends as soon as one of them has won a total of 5 points; this player then receives the $100. After they have completed four rounds, of which Peter has won three and Paula only one, a fire breaks out so that they cannot continue their game. a. How should the $100 be divided between Peter and Paula? b. How should the $100 be divided in the general case, when Peter needs to win a more rounds and Paula needs to win b more rounds?
We can enumerate all the cases, or we can just play 5 rounds and count the times where Peter wins 0 or 1 times out of the 5, to find out Paula’s winning chance:
\[ \binom{5}{0} \left( \frac{1}{2} \right)^5 + \binom{5}{1} \left( \frac{1}{2} \right)^5 = \frac{6}{32} \]
This is \(\frac{3}{16}\), or Peter should receive \(\frac{13}{16}\) of the portion, and Paula should receive \(\frac{3}{16}\).
The number of rounds that can be played is \(a + b - 1\).
We can apply the same formula in the general case, where we count \([1, a)\) and multiply it by \(\frac{1}{2}^{a + b - 1}\).
According to many people’s intuition, when two events, such as head and tail in coin tossing, are equally likely then the probability that these events will occur equally often increases with the number of trials. This expectation reflects the intuitive notion that in the long run, asymmetries of the frequencies of head and tail will “balance out” and cancel. To find the basis of this intuition, consider that \(2n\) fair and independent coins are thrown at a time. a. What is the probability of an even \(n : n\) split for head and tail when \(2n = 20\)? b. Consider the same question for \(2n = 200\) and \(2n = 2000\).
In the case of 20 coin flips, there are \(2^20\) or about 1 million ways to flip them in a way where order doesn’t matter.
To count the number of times where there are exactly 10 heads and 10 tails:
\[\binom{20}{10} = \frac{10! \cdot 10!}{20!}\]
Or about 184,756. Dividing this by \(2^20\), we get 17.62%.
We can approximate this probability as the following, trying to create a rectangle of the area below the curve:
\[\frac{1}{\sqrt{n\pi}}\]
An urn contains six balls — three red and three blue. One of these balls — let us call it ball A — is selected at random and permanently removed from the urn without the color of this ball being shown to an observer. This observer may now draw successively — at random and with replacement — a number of individual balls (one at a time) from among the five remaining balls, so as to form a noisy impression about the ratio of red vs. blue balls that remained in the urn after A was removed. Peter may draw a ball six times, and each time the ball he draws turns out to be red. Paula may draw a ball 600 times; 303 times she draws a red ball, and 297 times a blue ball. Clearly, both will tend to predict that ball A was probably blue. Which of them — if either — has the stronger empirical evidence for his/her prediction?
They both have the same amount of empirical evidence. The only thing that matters is the difference between the red and blue balls being drawn, which is 6 for both.
To calculate the probability that the ball drawn was blue for Peter, we can use Bayes theorem, calculating the probability that the ball drawn was blue or red:
\[P(A = b \mid \langle rrrrrr \rangle) = \frac{1} {1 + \left( \frac{2}{3} \right)^6} = 0.919\]
For Paula, we get the same result:
If we pull blue: \[P(303r \land 297b \mid A = b) = \binom{600}{303} \left( \frac{3}{5} \right)^{303} \left( \frac{2}{5} \right)^{297}\]
If we pull red: \[P(303r \land 297b \mid A = r) = \binom{600}{303} \left( \frac{2}{5} \right)^{303} \left( \frac{3}{5} \right)^{297}\]
The chance of blue out of both blue and red: \[P(A = b \mid 303r \land 297b) = \frac{1}{1 + \left( \frac{2}{3} \right)^6}\]
Which is the same as above.
The following problem is described in Cacoullos (1989, pp. 35–36). A worker’s legal code specifies as a holiday any day during which at least one worker in a certain factory has a birthday. All other days are working days. How many workers (n) must the factory employ so that the expected number of working man-days is maximized during the year?
Each worker can work up to 365 days a year. However, if they have the same birthday, everyones workdays go down by 1. Thus, we want to find the point at which