Editing Agent Based Modeling (section)

== <span style="color: #FFFFFF;">Understanding</span> ==
ABM is understood through '''Rules''' and '''Aggregation'''.

'''1. The 'Three-Step' Loop''':
Every agent in every simulation follows a loop:
* '''Observe''': What is happening around me? (e.g., "Is there a predator nearby?")
* '''Decide''': Based on my rules, what should I do? (e.g., "If yes, run away.")
* '''Act''': Change the state or position. (e.g., "Move to coordinate X,Y.")

'''2. Complexity from Simplicity''':
The power of ABM is that the rules can be very dumb, but the result is very smart.
* '''The Boids Model''': To simulate a flock of birds, you don't need a "Leader." You just give every bird three rules: "Don't hit others," "Stay near the group," and "Fly in the same direction."
* '''Result''': Perfectly realistic, "swarming" behavior.

'''3. Discovering 'Tipping Points'''':
ABM is excellent for finding "The Point of No Return."
* If 10% of people wear masks, the virus spreads. If 15% wear them, nothing changes. But if 20% wear them, the virus suddenly dies out. ABM helps us find that "Magic Number" (the '''Phase Transition''').

'''The Schelling Model of Segregation''': This is the most famous ABM. Thomas Schelling showed that even if every person is "tolerant" (meaning they are happy as long as 30% of their neighbors are like them), the entire city will still end up 100% segregated. This proved that "Individual Intent" is not the same as "Group Outcome."
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