# Introducing Probability Theory without Statistics

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The Probability Axioms are:

1. Zero <= P(E[j]) <= 1 for each j. Each event has a probability in the range from zero to 1, inclusive.
2. P(S) = 1. All the possible results are included in S, and it is certain that one of those results will occur.
3. P(E[i] U E[j]) = P(E[i]) + P(E[j]) if (E[i]) and P(E[j]) are mutually exclusive. The probability of the union of two exclusive events equals the sum of the probabilities of each of those separate events.
4. Extend the third axiom to any group of mutually exclusive events, by saying that the probability of the union of a group of mutually exclusive events equals the sum of probabilities of each of those separate events.

Events are “mutually exclusive” if the intersection set of the events is the empty set.

Consider the sample space “S1” of two coin throws, where we keep track of the sequence. For example, “HT” means that the first toss comes up heads, and the second is tails. The sample space is {HH, HT, TH, TT}.

The probability of each outcome is 0.25, the results are exclusive, and the four probability axioms are satisfied:

• Each event has P(E) = 0.25, and zero < 0.25 < 1.
• After two coin tosses, the outcome is one of the elements in the sample space, so P(S1) = 1.
• Each outcome excludes the others. The probability of the union of two events, say {HH, TT} equals the sum of the probabilities of either alone.

Generally we also have to mention that we are using a “fair coin”, so it is not biased either for heads nor tails, and that we exclude the coin landing on edge or exploding in mid-air.

We could have a different sample space if we did not track the sequence. S2 = {(two heads), (two tails), (one head and one tail in either order)} which could be represented as, say, the number of heads in two tosses : {2, 0, 1}. In sample space S2, the probability of (one head and one tail, in either order) is 0.5. Again, sample space S2 satisfies the Probability Axioms.

## The Lottery Axioms, Including the Independence Axiom

Although the usual “lottery” is a draw rather than a coin toss, it really is just a probability event with the distinction that there is a payoff structure and the observer will prefer some outcomes over others.

In a lottery, the preference for outcome ‘X’ over outcome ‘Y’ is shown as “X > Y”. Indifferent outcomes, such as having equal payouts, are shown as “X ~ Y”.

The Lottery Axioms are similar to the Probability Axioms:

1. For all outcomes X and Y, there is a relationship “X > Y” or “Y > X” or “X ~ Y”. This is the “completeness” axiom.
2. For all outcomes X, Y and Z, if X > Y and Y > Z, then X > Z. This is the “transitive” axiom. It should probably add that “if X ~ Y and Y ~ Z, then X ~ Z”.
3. For all outcomes X, Y and Z, if X > Y and Y > Z, then there is a unique numeric value for ‘p’ such that p*X + (1-p)*Z ~ Y. This “continuity” axiom should, in my opinion, add that 0 < p < 1 in the Real numbers.
4. For all outcomes X, Y and Z, if X > Y, then for all ‘p’ where 0 <= p <= 1, p*X + (1-p)*Z > p*Y + (1-p)*Z . This is the “independence” axiom.

In ordinary English, “independent” events are events where one does not “depend on”, or influence, the other. For example, your coin toss is independent of a poker hand dealt by a stranger in another city.

In mathematics, events are independent if the probability of both happening equals the product of their separate probabilities. This is expressed in the equation P(A.B) = P(A)*P(B).

What do these axioms mean for a lottery? Either one outcome is preferable to another, or there is no preference; but the gambler can decide on a preference for each and every outcome.

If preferences were not transitive, there might be no “preferred” outcome out of three. By contrast, the “rock, paper, scissors” game is nottransitive: no one choice is best. Transitive lotteries allow gamblers to make consistent choices.

When playing cards, understanding probability offers an advantage. Image by fdecomite

Suppose the “house” is running three lotteries, and a gambler has the preference X > Y > Z. Then “Continuity” ensures that the “house” could adjust the payoffs for X and Z such that the gambler is indifferent to playing Y or to playing the adjusted mix of X and Z.

Independence means that, if X > Y, then adjusting them equally and adding a third lottery equally to each does not affect the original preference. While this may be sound mathematics, human behaviour can be more complex as will be seen in the Allais Paradox.

An easy lottery example would be drawing from a standard pack of playing cards, with the payout equal to the face value on the card. The independence axiom might add a bonus for the number shown on the roll of two dice.