Agent Based Simulation Python

Agent Based Simulation Python is used for designing complicated models in which individual agents communicate with one another and their platform, agent-based simulation is considered as a robust approach. Python is appropriate for agent-based simulations due to its clearness and widespread libraries. For this utilization mesa is examined as the prevalent and efficient library. For developing and visualizing agent-based systems, it offers a suitable model. Along with short explanations and extension plans, we suggest procedural instruction on agent-based simulation in Python:

Procedural Instruction to Agent-Based Simulation in Python

Step 1: Install Mesa

Initially, we must install the mesa library.

pip install mesa

Step 2: Create the Model

A basic agent-based framework has to be developed by us in which agents travel around a grid and are capable of communicating with one another. Our team assumes a simple instance of a Schelling segregation model.

from mesa import Agent, Model

from mesa.space import MultiGrid

from mesa.time import RandomActivation

import matplotlib.pyplot as plt

import numpy as np

class SchellingAgent(Agent):

“”” An agent with a given type (1 or 2) in the Schelling segregation model. “””

def __init__(self, unique_id, model, agent_type):

super().__init__(unique_id, model)

self.agent_type = agent_type

def step(self):

similar = 0

different = 0

for neighbor in self.model.grid.neighbor_iter(self.pos):

if neighbor.agent_type == self.agent_type:

similar += 1

else:

different += 1

if similar + different > 0 and similar / (similar + different) < self.model.similarity_threshold:

self.model.grid.move_to_empty(self)

class SchellingModel(Model):

“”” Model class for the Schelling segregation model. “””

def __init__(self, width, height, density, similarity_threshold):

self.num_agents = int(width * height * density)

self.grid = MultiGrid(width, height, True)

self.schedule = RandomActivation(self)

self.similarity_threshold = similarity_threshold

for i in range(self.num_agents):

agent_type = self.random.choice([1, 2])

agent = SchellingAgent(i, self, agent_type)

self.schedule.add(agent)

x = self.random.randrange(self.grid.width)

y = self.random.randrange(self.grid.height)

self.grid.place_agent(agent, (x, y))

def step(self):

self.schedule.step()

def plot_grid(model, width, height):

grid = np.zeros((width, height))

for cell in model.grid.coord_iter():

cell_content, x, y = cell

if cell_content:

grid[x][y] = cell_content.agent_type

plt.imshow(grid, interpolation=’none’, cmap=’viridis’)

plt.title(“Schelling Model”)

plt.show()

# Parameters

width, height = 20, 20

density = 0.8

similarity_threshold = 0.3

steps = 10

# Create and run the model

model = SchellingModel(width, height, density, similarity_threshold)

for i in range(steps):

model.step()

plot_grid(model, width, height)

Description of the Code

1. SchellingAgent Class: An individual agent with a kind 1 or 2 is demonstrated in SchellingAgent Class. The characteristics of the agent are described by the step approach, in which it examines the kinds of its surroundings and on the basis of the similarity threshold, determines either to move or not.
2. SchellingModel Class: The entire model is depicted by SchellingModel Class. Generally, it assigns agents in a random manner by setting the grid. To facilitate the process of simulation, focus on describing the step technique.
3. plot_grid Function: As a means to visualize the grid and the dissemination of agents, it is beneficial to employ helper function.
4. Simulation Parameters: For the simulation, we aim to describe parameters such as the agent density, number of steps, grid size, and similarity threshold.
5. Model Execution: An instance of the SchellingModel ought to be constructed in such a manner, for the fixed number of steps, it executes and then visualizes the final condition.

Extension Plans

1. Different Movement Rules: On the basis of other measures, we intend to apply various rules for agent movement.
2. Multiple Agent Types: Numerous kinds of agents have to be appended and focus on examining in what way they communicate.
3. Environment Interaction: The ecological aspects should be initiated which impacts the characteristics of the agent.
4. Data Collection: Periodically, based on segregation trends, our team aims to gather and examine data.
5. Interactive Visualization: Through the utilization of libraries such as Bokeh or Plotly, we plan to develop an interactive visualization.

agent based simulation python projects

If you are choosing a project topic based on agent based simulation, you must prefer efficient as well as significant topics. We suggest some crucial projects that encompass a diversity of complications and fields by offering extensive possibility for learning and investigation:

Social and Economic Simulations

1. Conway’s Game of Life
2. Market Trading Simulation
3. Crowd Behavior in Emergencies
4. Traffic Flow Simulation
5. Social Network Dynamics
6. Schelling Segregation Model
7. Epidemic Spread Simulation
8. Voting Behavior Simulation
9. Urban Development Simulation
10. Resource Allocation in Societies

Environmental Simulations

1. Wildlife Migration Patterns
2. Pollution Spread in Water Bodies
3. Climate Change Impact on Ecosystems
4. Energy Consumption in Smart Grids
5. Coral Reef Ecosystem Dynamics
6. Forest Fire Spread
7. Water Resource Management
8. Deforestation and Reforestation
9. Waste Management Systems
10. Air Pollution Dispersion

Biological and Medical Simulations

1. Bacterial Colony Growth
2. Ant Colony Optimization
3. Immune System Response
4. Plant Growth Simulation
5. Food Chain Dynamics
6. Cellular Automata for Tumor Growth
7. Disease Spread in Human Populations
8. Evolutionary Biology Simulations
9. Neural Network Dynamics in Brain
10. Genetic Drift in Populations

Economic and Business Simulations

1. Agent-Based Stock Market
2. Company Competition Dynamics
3. Labor Market Dynamics
4. Banking System Stability
5. Startup Ecosystem Simulation
6. Supply Chain Management
7. Consumer Behavior Simulation
8. Real Estate Market Simulation
9. Inflation and Deflation Cycles
10. Trade and Tariff Effects

Urban and Infrastructure Simulations

1. Public Transportation Optimization
2. Waste Collection and Recycling
3. Emergency Services Allocation
4. Smart Water Distribution Systems
5. Parking Management Systems
6. Traffic Light Control Systems
7. Smart City Energy Management
8. Urban Sprawl and Planning
9. Road Network Optimization
10. Urban Crime Simulation

Game and Recreation Simulations

1. Simulating Team Sports
2. Virtual Pet Behavior
3. Simulating Chess Algorithms
4. Puzzle Solving Algorithms
5. Gaming Economy Simulation
6. Multi-Agent Games
7. Board Game Strategy Simulation
8. Artificial Life and Evolution
9. Role-Playing Game Dynamics
10. Simulation of Competitive Games

Security and Defense Simulations

1. Military Strategy and Tactics
2. Disaster Response Planning
3. Crowd Control during Protests
4. Coastal Defense Simulation
5. Intelligence Network Simulation
6. Cybersecurity Attack and Defense
7. Border Security Management
8. Insurgency and Counterinsurgency
9. Simulating Rescue Operations
10. Urban Security Patrols

Education and Research Simulations

1. Learning and Teaching Strategies
2. Virtual Laboratory Experiments
3. Simulating Academic Performance
4. Educational Game Development
5. Scholarly Communication Networks
6. Simulating Classroom Dynamics
7. Research Collaboration Networks
8. Online Learning Platforms
9. Peer Review Process Simulation
10. Simulating Research Funding Allocation

Technology and Innovation Simulations

1. Innovation Diffusion in Industries
2. Artificial Intelligence Collaboration
3. Software Development Processes
4. Autonomous Vehicle Networks
5. Blockchain and Cryptocurrency Networks
6. Simulating Technology Adoption
7. Startup Ecosystem Dynamics
8. Product Development Cycles
9. Virtual Reality Environment Interactions
10. Internet of Things (IoT) Networks

Miscellaneous Simulations

1. Behavior of Swarms (e.g., bees, birds)
2. Simulating Natural Disasters
3. Gene Regulatory Networks
4. Online Social Media Interactions
5. Simulation of Virtual Worlds
6. Simulating Animal Behavior
7. Cultural Evolution and Spread
8. Molecular Dynamics
9. Customer Service Dynamics
10. Predicting Fashion Trends

Instance Project: Epidemic Spread Simulation

With the aid of the mesa library, we execute a basic epidemic spread simulation.

Step 1: Define the Agent and Model

from mesa import Agent, Model

from mesa.time import RandomActivation

from mesa.space import MultiGrid

import random

class PersonAgent(Agent):

def __init__(self, unique_id, model, infection_rate):

super().__init__(unique_id, model)

self.infected = False

self.infection_rate = infection_rate

def step(self):

if not self.infected:

return

neighbors = self.model.grid.get_neighbors(self.pos, moore=True, include_center=False)

for neighbor in neighbors:

if random.random() < self.infection_rate:

neighbor.infected = True

if random.random() < 0.1:  # Recovery rate

self.infected = False

class EpidemicModel(Model):

def __init__(self, width, height, density, infection_rate):

self.num_agents = int(width * height * density)

self.grid = MultiGrid(width, height, True)

self.schedule = RandomActivation(self)

self.infection_rate = infection_rate

for i in range(self.num_agents):

agent = PersonAgent(i, self, infection_rate)

if i == 0:  # Patient zero

agent.infected = True

self.schedule.add(agent)

x = self.random.randrange(self.grid.width)

y = self.random.randrange(self.grid.height)

self.grid.place_agent(agent, (x, y))

def step(self):

self.schedule.step()

# Parameters

width, height = 20, 20

density = 0.8

infection_rate = 0.2

steps = 50

# Create and run the model

model = EpidemicModel(width, height, density, infection_rate)

for i in range(steps):

model.step()

Step 2: Visualize the Results

import matplotlib.pyplot as plt

import numpy as np

def plot_grid(model, width, height):

grid = np.zeros((width, height))

for cell in model.grid.coord_iter():

cell_content, x, y = cell

if cell_content and cell_content.infected:

grid[x][y] = 1

plt.imshow(grid, interpolation=’none’, cmap=’viridis’)

plt.title(“Epidemic Spread Simulation”)

plt.show()

plot_grid(model, width, height)

The process of developing and visualizing an agent-based simulation of an epidemic spread is demonstrated in this simple instance. Typically, you can investigate different aspects and settings impacting the dissemination of diseases through prolonging this model.

Through this article, we have provided gradual direction on agent-based simulation in Python together with concise explanations and extension plans. Also, by offering numerous possibilities for learning and exploration, several projects that include a diversity of disciplines and complications are recommended by us in an explicit manner.

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