Editing Agent-Based Modeling

<div style="background-color: #4B0082; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">
{{BloomIntro}}
Agent-Based Modeling (ABM) is a powerful computational technique for studying complex systems by simulating the actions and interactions of autonomous individuals (called '''Agents'''). It is a "Bottom-Up" way of doing science. Instead of using a single equation to predict how a whole population will act, we create thousands of "Digital People," give them simple rules, and watch what happens when they interact. ABM allows us to see how a small change in individual behavior can lead to massive "Emergent" shifts in a city, a market, or an ecosystem. It is the closest thing science has to a "Virtual Laboratory."
</div>

__TOC__

<div style="background-color: #000080; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">
== <span style="color: #FFFFFF;">Remembering</span> ==
* '''Agent-Based Modeling (ABM)''' — A simulation technique used to understand the behavior of a system by modeling its individual parts.
* '''Agent''' — An autonomous entity in a simulation that follows a set of rules (e.g., a Person, a Car, a Bird).
* '''Emergence''' — When a complex global behavior arises out of simple local rules (The "Magic" of ABM).
* '''Environment''' — The virtual space in which agents move and interact (e.g., a Grid, a Network, or a Map).
* '''Bounded Rationality''' — The idea that agents only have limited information and limited brainpower (unlike the "perfect" individuals in classical economics).
* '''Local Interaction''' — Agents only react to things immediately around them, not the whole system.
* '''Stochasticity''' — The use of "Randomness" in a model to represent the unpredictability of the real world.
* '''Monte Carlo Simulation''' — Running the same model thousands of times with different random seeds to see the average outcome.
* '''Validation''' — The process of ensuring that the model's output matches real-world data.
* '''Emergent Property''' — A macro-level pattern that was not explicitly programmed (e.g., a traffic jam).
* '''NetLogo''' — The most popular programming language and platform for building agent-based models.
* '''Sensitivity Analysis''' — Testing how much the final result changes when you slightly change one individual rule.
</div>

<div style="background-color: #006400; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">
== <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."
</div>

<div style="background-color: #8B0000; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">
== <span style="color: #FFFFFF;">Applying</span> ==
'''Modeling 'The Forest Fire' (A Cellular Automaton):'''
<syntaxhighlight lang="python">
import random

def simulate_fire(grid_size, density):
    """
    Agents: Trees. Rule: 'If neighbor is on fire, I catch fire.'
    """
    # Create a grid of trees (1) or empty (0)
    grid = [[1 if random.random() < density else 0 for _ in range(grid_size)] 
            for _ in range(grid_size)]
            
    # Start a fire at the top row
    fire = [(0, i) for i in range(grid_size) if grid[0][i] == 1]
    burnt_count = len(fire)
    
    # Simple 'Spread' logic
    while fire:
        x, y = fire.pop(0)
        grid[x][y] = 2 # Burnt
        # Check 4 neighbors
        for dx, dy in [(0,1), (0,-1), (1,0), (-1,0)]:
            nx, ny = x+dx, y+dy
            if 0 <= nx < grid_size and 0 <= ny < grid_size:
                if grid[nx][ny] == 1:
                    grid[nx][ny] = 2
                    fire.append((nx, ny))
                    burnt_count += 1
                    
    return burnt_count / (grid_size * grid_size)

# Low Density: 40% trees
print(f"Burnt area at 40%: {simulate_fire(20, 0.40) * 100:.1f}%")
# High Density: 70% trees
print(f"Burnt area at 70%: {simulate_fire(20, 0.70) * 100:.1f}%")
# Note: The fire doesn't just grow linearly; it 'explodes' 
# once the density hits a critical point (~59%).
</syntaxhighlight>

; ABM Landmarks
: '''Conway's Game of Life''' → A 0-player game where 4 simple rules create infinite patterns, gliders, and even "computers" made of cells.
: '''Sugarscape''' → A simulation that proved how inequality and wealth gaps emerge naturally even when everyone starts with the same ability.
: '''Epidemiological Models (COVID)''' → Using ABM to simulate every individual in a city to see how "closing schools" vs "wearing masks" affects the hospitals.
: '''Financial Market Simulators''' → Trading bots competing against each other to see how "Flash Crashes" happen.
</div>

<div style="background-color: #8B4500; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">
== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ Equation-Based vs. Agent-Based
! Feature !! Equation-Based (Physics/Calc) !! Agent-Based (ABM)
|-
| Level || Top-Down (The whole group) || Bottom-Up (The individual)
|-
| Variation || Everyone is 'Average' || Everyone is 'Unique'
|-
| Interaction || Aggregate (Mean-Field) || Direct and Local
|-
| Strength || Very fast / Mathematically pure || Can find 'Surprising' emergence
|-
| Analogy || A 'Formula' || A 'Video Game'
|}

'''The Concept of "Verification vs. Validation"''':
* '''Verification''': "Did I build the model right?" (Is the code bug-free?).
* '''Validation''': "Did I build the right model?" (Does it actually look like a real city?).
Analyzing the "Truthfulness" of a simulation is the most difficult part of ABM.
</div>

<div style="background-color: #483D8B; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">
== <span style="color: #FFFFFF;">Evaluating</span> ==
Evaluating an ABM: (1) '''Stochasticity''': If you run the model 10 times, do you get 10 completely different answers (too much randomness)? (2) '''Rule Simplicity''': Are the rules based on real psychology or just "guesses"? (3) '''Scaling''': Does the model still work when you go from 100 agents to 1,000,000? (4) '''Equifinality''': Is there more than one way the model could have produced the result?
</div>

<div style="background-color: #2F4F4F; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">
== <span style="color: #FFFFFF;">Creating</span> ==
Future Frontiers: (1) '''Digital Twins''': Creating a perfect ABM of a real-world city (like Singapore) to test every new law or bridge before it is built. (2) '''Social Media Simulation''': Using 100 million "Bot Agents" to see how fake news spreads and how to stop it. (3) '''AI-Agents''': Replacing simple "If/Then" rules with "Large Language Models" so the agents can actually "talk" and "reason" with each other. (4) '''Multi-Scale ABM''': Modeling a whole country by linking models of cells, people, and weather into one giant system.

[[Category:Systems Science]]
[[Category:Computer Science]]
[[Category:Sociology]]
</div>