This project is meant to be a pretty simple simulation of living creatures in general, a topic we discussed at length in RELIW4815 Technology Religion Future taught by David Kittay. This simulation is not meant to be completely accurate; it's meant to be a simplified abstraction of our world and hopes to gain some insight into our reality and into simulations in general.
This simulation takes the form of a 2D grid. Each cell in the grid has a value from 0 to 1 called hostility; this value is an abstraction of all the natural features of that part of the land. Higher hostility values mean it is more difficult for life to thrive there.
This world is inhabited by creatures, each of which occupies one cell in the grid. Each creature is assigned to a type, which is simply a number; creatures of the same type are friendly, while creatures of different types are enemies. This was chosen as a simplistic way to create conflict and competition among creatures.
These creatures have three attributes, each of which is held as a value between 0 and 1:
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Strength: determines how likely a creature is to survive the hostility of the environment, and how likely it is to defeat an enemy creature in a fight.
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Fertility: determines how likely a creature is to successfully reproduce with a friendly creature.
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Intelligence: supplements both strength and fertility.
Creatures are initially created with random values for each of their attributes and randomly assigned a type.
The empty world is displayed as a black screen. Creatures are displayed as squares in the simulator, with their RGB values derived from their attributes: red is strength, green is fertility, and blue is intelligence. Thus, a creature with perfect strength, fertility, and intelligence would be white.
On each turn, the simulation performs the following in order to each creature:
- Determine if the creature gets killed by the hostility of the environment (strength vs hostility)
- Handle the creature's interactions with each of its neighbors:
- if the neighbor is friendly, it tries to reproduce with it. (strength vs strength)
- if the neighbor is an enemy, it tries to kill it. (fertility)
- If there is a less hostile cell near the creature, the creature moves there.
All these events are determined by generating random numbers with the relevant attribute as the maximum (effectively rolling dice).
When two creatures reproduce, the offspring has the same type as its parents. The value of each of its attributes is drawn from a normal distribution; the mean of the distribution is the mean of the parents' values, and the standard deviation is half the difference between them. This is meant to be a simple replication of inheritance, taking into account the parents' "genes," but allowing for random mutation.
- Compile:
javac Simulator.java - Run:
java Simulator
Hit me up, and I'll send you an executable that you can just click to run.
When run, the following window will open:
The width of the square simulator world. Works with values 1-800. Keep in mind that high values for this will make the simulator very slow.
How many creatures the simulator will start off with. Use big numbers here; many creatures die in the very beginning.
A factor of how easy it will be to reproduce in the simulation. A value of 2 makes it twice as easy, a value of 0.5 makes it twice as hard.
Adjusts the hostility of the simulation as a whole. While the exact values will vary across different parts of the board, a value of 2 for hostility will make it twice as hard for the creatures to survive.
Adjusts how much intelligence supplements fertility and hostility. A value of 2 makes intelligence worth twice as much as the other two, while 0.5 will make it half as valuable.
Determines how many different types of creatures will be in the simulation. Remember that creatures of the same type are friendly while those of different types are enemies; therefore simulations with more types are likely to have more conflicts.
Starts a new simulation with the configuration in the fields above.
Pauses a simulation if it is not already paused; continues it if it is paused . Unpausing a simulation will not change its configurations.
Adjusts the speed of the simulation. The FPS of a simulation can be changed after it is started by changing the value after pausing the simulation, and then unpausing it.
Opens up a dialog allowing you to save a screenshot of the simulation. Probably pause the simulation before using this.
Thank you to the authors of libgdx, specifically Mario Zechner and Nathan Sweet, for writing a smooth noise generator and keeping it open under the Apache license. I used their method to randomly generate a smooth hostility field for my environment.
Thank you to Nick Bostrum, Hans Moravec, Robin Hanson, and other authors whom we read for this class, and who greatly inspired our discussion and this project.
Thank you to Professor Kittay and the members of the Technology Religion Future class for the fantastic discussion this semester, in and out of class. No class has challenged my thinking as much as this one, and I hope that we'll remember each other many years from now when we are computer programs living off test-tube meat.