[This post is a stub that will be expanded as time goes on and I learn more, or figure out how to present the question better.]
Humans, and many animals, tend to like predictability. When things get crazy, chaotic, unpredictable – we tend to avoid those things. This is called risk aversion: preferring safe, predictable outcomes to unpredictables ones.
Take the choice between a guaranteed $1,000,000 or a 10% chance of $10,000,000 with a 90% chance of nothing at all. How many people would choose the riskier option? Very few, it turns out. We aren’t always risk-averse. When animals search for food, they tend to prefer safer areas to riskier ones until they start getting exceptionally peckish. Once starving, animals are often risk-seeking, and are willing to go to great lengths for the chance to get food.
Why are we risk-averse? There are a few reasons. First off, unpredictability means that the information we have about our environment is not as useful, and possibly downright wrong. On the other hand, it may just come from experience. Imagine that you are given the choice between two boxes, each of which will give a reward when opened, and rewards are reset when closed. One of these boxes will give you lots of rewards sometimes, and no rewards the rest of the time, while the other box will always give you a little reward. Over the long run the two boxes will give you the same amount of reward but when you start opening them up? You are likely to have a dry run from the risky box. Whenever you get no reward from a box, you feel more inclined to open the safer box. This gives you a nice little reward! So now you like this box a little better. Maybe you think it’s a good idea to peak in the risky box now? Ah, foiled again, that box sucks, better stick with the safe box that you know.
This is the basic logic behind the Reinforcement Learning model of risk-aversion as characterized by Yael Niv in 2002 (does anyone know an older reference?).