Information and entropy

Before We Begin ...

From the last module's introduction, information occurs in everyday life, and it consists of two aspects: surprise and meaning. We would like to emphasize that our focus will be on the mathematics of surprise or uncertainty. Whenever you study a subject, you also experience a subtle application of information theory. For example, you are asked to review your elementary algebra again. You have confidence that the topic is easy, and you only need very little "brainpower" for the subject. It looks and feels easy because you are familiar with the material. You already have the information for the topic. Suppose you were asked to review your calculus subjects (e.g., Math 20 series) you may find it more challenging because most theories may or may not be familiar to you. There is a higher degree of uncertainty. This time you need to exert more effort in studying. If you were asked to take on a new theory that you have no clue about, you have maximum uncertainty about that theory. However, once you have given enough time and effort to study that theory, that uncertainty now becomes acquired information for you. You may not need too much brainpower to review or teach that topic again. This leads to an important concept that may repeat in future discussions. We experience an uncertainty about a topic that we don't know about. However, when we "receive" that uncertainty, it becomes information.

There is a subtle tradeoff between uncertainty and brainpower for a particular subject. You will start to notice this later in the course. Observe that when there is high uncertainty (e.g., a completely new topic), our brain studies a material with effort. Whenever we have low uncertainty (e.g., review a familiar subject), we exert less effort for the subject. The amount of brainpower that we use can be analogous to computing power. The uncertainty can be associated with the data that we need to process. This example shows where information theory and complexity mix together. If we are given a similar problem, what would the best solution be? Information theory does not tell us how to solve a problem, because it is only a measurement. The solutions are up to us. Going back to our study example, if we need to study a completely new topic, what are our options? Do we spend so much time on the material to cover the bulk of it? How much brainpower do we use? Or can we cut the material into chunks so that we can process at optimum time and power? The solution is up to us. We just need to be creative.

Chunking is a well-known learning strategy to reduce the workload on a particular subject. It is proven to be effective in studying new topics.

Deriving Information

Bits, Bans, and Nats