Text
This is going to be a big chapter so it will be broken up into small parts.

Contents

1. Internal Decisions
   • A. Thought Process
2. External Decisions
   • A. Observing Results

1. Internal Decisions

Thought Process
Thought process

Text
This is going to be a big chapter so it will be broken up into small parts.

Contents

1. Positive Learning
   • A. Learning that benefits Compy
     • Pleasure
   • B. Learning that benefits other Compies
     • Altruism
2. Negative Learning
   • A. Learning that hurts Compy
     • Pain
   • B. Learning that hurts other Compies
     • Greed/Malice

1. Positive Learning

Learning that benefits Compy
Positive learning is any learning that occurs from an action or event that directly or indirectly benefits a Compy. For example, if a Compy finds a plant, decides to eat that plant and the plant does not injure the Compy–the Compy will learn that this type of plant is safe to eat and that it is beneficial to eat.

Pleasure is something that encompasses learning that benefits a Compy. In general, a Compy will do something if it “feels good”.

Learning that benefits other Compies
Positive learning includes learning that occurs from an action or event that directly or indirectly benefits another Compy. For example, (using the plant example again,) say a Compy (Compy A) finds a plant that is safe to eat and is good to eat. Instead of directly eating the plant, the Compy decides to save it for later and soon happens upon another Compy (Compy B). Compy B offers to trade an item (of higher value than the plant,) for the previously mentioned plant. Compy A accepts the trade and learns that it got a “better deal” than Compy B. (Note: economics is another topic that will probably start from the bartering level and has to be developed by the Compies themselves.)

Another type of positive learning that benefits another Compy has to do with altruism. For example, if a Compy (Compy A) happens upon another Compy (Compy B) who is hungry and starving, they may give that Compy B some food. This is only an option though because Compy A might not give Compy B anything at all and Compy B might even starve to death. This is decided by a number of factors described in the chapter on Compy Decisions.

2. Negative Learning

Learning that hurts Compy

Pain

Learning that hurts other Compies

Greed/Malice

Text
There are male and female Compies. When male and female compies have a baby, the baby is a combination of the parent colors. For example, say the father Compy is blue and the mother Compy is red. The child will either be blue, red, a combination of those two colors.

This brings in the whole Punnentt square heredity thing. I’m not sure how detailed I want this part to be for the time being.

Darker colors/shades are dominant–if someone is blue for example, it is stronger than yellow.

List of colors/shades (from strongest to weakest)

• Black
• Blue
• Red
• Green
• Orange
• Yellow
• White

At least that is what it looks like on a more detailed level. At first, I think I’ll start out with just a rectangle filled with a specified color.

Text
For now, food comes in the form of plants.  Plants can be planted by Compies if desired.

Text
A Compy is like a person in the sense that it needs food. It also has a certain level of healthiness. If a Compy does not have enough food it’s health deteriorates. If a Compy’s health goes down to 0% it is dead. Compies do not come back from the dead in Compy World.

Properties

• Health (0-100%)

Text
The simulation should have a certain number of defined resources. Resources are things that can be found in RPG or strategy games such as trees, gold, stone, etc.

Resources are basically elements. When combined, these resources can become different things. The goal is to somehow code behaviors and rules for these various resources and yet make it somewhat open-ended as far as what can be created with different amounts of these resources.

It’s not necessarily how it works in the real world, but this is a simulation of a much, much smaller scale type of world.

Resource List

• Solid
  • Tree (wood)
  • Dirt
  • Stone
  • Gold
• Liquid
  • Water
• Gas
  • Oxygen
  • Fire (it’s not an element, but this is simplified)