by Yohan J. John
Probability theory is a relative newcomer in the history of ideas. It was only in the 19th century, two centuries after Isaac Newton ushered in the scientific revolution, that thinkers began to systematize the laws of chance. In just a few generations, the language of probability has seeped into popular discourse — a feat that older branches of mathematics, such as calculus, have not quite managed. We encounter numbers that express probabilities all the time. Here are a few examples:
- “With a 6-sided die, the probability of rolling a 5 is 1 in 6, or around 16.7%. “
- “The chance of rain tomorrow is 60%.”
- “The chance of Bernie Sanders winning the 2016 US Presidential Election is 15%.”
What exactly do these numbers — 16.7%, 60%, 15% — mean? Does the fact that we use the words 'chance' or 'probability' for all three suggest that dice, rainfall, and elections have something in common? And how can we assess the accuracy and usefulness of such numbers?
Mathematicians, scientists and philosophers agree on the basic rules governing probability, but there is still no consensus on what probability is. As it turns out, there are several different interpretations of probability, each rooted in a different way of looking at the world.
Before we get to the interpretations of probability, let us review the basic mathematical rules that any interpretation must conform to. Let's imagine we have a set of possible events: S = {A, B, C,…Z}. The set S is called a possibility space or a reference class. The probability of event 'A' is symbolized by P(A), and its value must be between 0 and 1. A value of 0 means the event cannot occur, and a value of 1 means the event definitely occurs. For the set of possible events, P(S) = 1. This means that some event from the set S will occur, and nothing outside the set can occur. In other words, the probability that something will occur must be 100%. Finally, for any two events 'A' and 'B' that cannot happen simultaneously, the probability that either one or the other will occur is just the sum of the two individual probabilities, so P(A or B) = P(A) + P(B).
The Stanford Encyclopedia of Philosophy lists 6 major interpretations of probability, but for our purposes it makes sense to look at the three that are most common and easily understood. These are
- Classical probability — a way to quantify “balanced ignorance”
- Frequentist probability — a way to quantify observations of a random process
- Subjective probability — a way to quantify subjective degree of belief