Agent Based Modeling - Revision history

Wordpad: BloomWiki: Agent Based Modeling

2026-04-25T01:46:39Z

BloomWiki: Agent Based Modeling

← Older revision		Revision as of 01:46, 25 April 2026
Line 1:		Line 1:
			<div style="background-color: #4B0082; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">
	{{BloomIntro}}		{{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."		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>

	== Remembering ==		__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-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).		* '''Agent''' — An autonomous entity in a simulation that follows a set of rules (e.g., a Person, a Car, a Bird).
Line 15:		Line 20:
	* '''NetLogo''' — The most popular programming language and platform for building agent-based models.		* '''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.		* '''Sensitivity Analysis''' — Testing how much the final result changes when you slightly change one individual rule.
			</div>

	== Understanding ==		<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'''.		ABM is understood through '''Rules''' and '''Aggregation'''.

Line 35:		Line 42:

	'''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."		'''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>

	== Applying ==		<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):'''		'''Modeling 'The Forest Fire' (A Cellular Automaton):'''
	<syntaxhighlight lang="python">		<syntaxhighlight lang="python">
Line 81:		Line 90:
	: '''Epidemiological Models (COVID)''' → Using ABM to simulate every individual in a city to see how "closing schools" vs "wearing masks" affects the hospitals.		: '''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.		: '''Financial Market Simulators''' → Trading bots competing against each other to see how "Flash Crashes" happen.
			</div>

	== Analyzing ==		<div style="background-color: #8B4500; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">
			== <span style="color: #FFFFFF;">Analyzing</span> ==
	{\| class="wikitable"		{\| class="wikitable"
	\|+ Equation-Based vs. Agent-Based		\|+ Equation-Based vs. Agent-Based
Line 102:		Line 113:
	* '''Validation''': "Did I build the right model?" (Does it actually look like a real city?).		* '''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.		Analyzing the "Truthfulness" of a simulation is the most difficult part of ABM.
			</div>

	== Evaluating ==		<div style="background-color: #483D8B; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">
			== <span style="color: #FFFFFF;">Evaluating</span> ==
	Evaluating an ABM:		Evaluating an ABM:
	# '''Stochasticity''': If you run the model 10 times, do you get 10 completely different answers (too much randomness)?		# '''Stochasticity''': If you run the model 10 times, do you get 10 completely different answers (too much randomness)?
Line 109:		Line 122:
	# '''Scaling''': Does the model still work when you go from 100 agents to 1,000,000?		# '''Scaling''': Does the model still work when you go from 100 agents to 1,000,000?
	# '''Equifinality''': Is there more than one way the model could have produced the result?		# '''Equifinality''': Is there more than one way the model could have produced the result?
			</div>

	== Creating ==		<div style="background-color: #2F4F4F; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">
			== <span style="color: #FFFFFF;">Creating</span> ==
	Future Frontiers:		Future Frontiers:
	# '''Digital Twins''': Creating a perfect ABM of a real-world city (like Singapore) to test every new law or bridge before it is built.		# '''Digital Twins''': Creating a perfect ABM of a real-world city (like Singapore) to test every new law or bridge before it is built.
Line 120:		Line 135:
	[[Category:Computer Science]]		[[Category:Computer Science]]
	[[Category:Sociology]]		[[Category:Sociology]]
			</div>

Wordpad: BloomWiki: Agent Based Modeling

2026-04-23T14:39:01Z

BloomWiki: Agent Based Modeling

← Older revision		Revision as of 14:39, 23 April 2026
Line 104:		Line 104:

	== Evaluating ==		== Evaluating ==
	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?		Evaluating an ABM:
			# '''Stochasticity''': If you run the model 10 times, do you get 10 completely different answers (too much randomness)?
			# '''Rule Simplicity''': Are the rules based on real psychology or just "guesses"?
			# '''Scaling''': Does the model still work when you go from 100 agents to 1,000,000?
			# '''Equifinality''': Is there more than one way the model could have produced the result?

	== Creating ==		== Creating ==
	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.		Future Frontiers:
			# '''Digital Twins''': Creating a perfect ABM of a real-world city (like Singapore) to test every new law or bridge before it is built.
			# '''Social Media Simulation''': Using 100 million "Bot Agents" to see how fake news spreads and how to stop it.
			# '''AI-Agents''': Replacing simple "If/Then" rules with "Large Language Models" so the agents can actually "talk" and "reason" with each other.
			# '''Multi-Scale ABM''': Modeling a whole country by linking models of cells, people, and weather into one giant system.

	[[Category:Systems Science]]		[[Category:Systems Science]]
	[[Category:Computer Science]]		[[Category:Computer Science]]
	[[Category:Sociology]]		[[Category:Sociology]]

Wordpad: BloomWiki: Agent Based Modeling

2026-04-23T14:23:58Z

BloomWiki: Agent Based Modeling

New page

{{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."

== Remembering ==
* '''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.

== Understanding ==
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."

== Applying ==
'''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.

== Analyzing ==
{| 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.

== Evaluating ==
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?

== Creating ==
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]]