In today's class we talked about data, information, and knowledge. Data is sometimes a numeric and sometimes qualitative, like this blog post. Once we add context we get information. Information has an associated certainty. Once we have a level of certainty that we are comfortable with we have knowledge. We use that knowledge to drive our actions, i.e., make decisions.

I read this blog today by Jared Dillian and I wanted to share it with you as it explains decision behavior and how even with knowledge and knowing the probabilities of the information, different people make different decisions and sometimes those decisions are "emotional" rather than "rational" -- and we often presume, as Information Scientists, that decisions are made rationally and without emotion (or a degree of "irrationality".)

Here's an excerpt from his blog:

If you’ve ever taken a decision theory class (I have, and it was awesome), this is the first thing you learn.
You have a 50/50 chance of winning $10,000. You have P = 0.5 of getting $10,000 and P = 0.5 of getting 0.
Or you can choose not to play the game and get $3,000. What do you do? The answer is: it depends on who you are and what your risk preferences are. Rationally, the number to get you to walk away should be $5,000. But most people will walk away for less. Sometimes much less. Sometimes people will walk away for $500 or $1,000. After all, it’s money they didn’t have before. Anyone who accepts less than $5,000 to walk away is known as risk-averse. Here is where things get interesting. Turn the experiment on its head. Tell people they have a 50/50 chance of either losing $10,000 or breaking even. Then tell them that they could either take their chances with the 50/50 game or they could lose a certain $2,000. Faced with this choice, people will roll the dice. Every. Single. Time. Nobody wants to “lock in” a certain loss. As long as there is a chance that they could break even, they will hope—except hope is not a strategy. This is known as loss aversion.