CS620 Important Topics
🔹 What does Imminent Event mean?
An imminent event is the next event that’s going to happen in a simulation. It's the one with
the closest (earliest) scheduled time.
🧠 Think of it like: In a to-do list, the task with the nearest deadline is your "imminent task" —
same for events in simulations.
🔹 What is a Complex Adaptive System?
It’s a system made of many parts (agents) that interact and adapt to each other and the
environment.
📌 Properties:
Emergence: New behaviors appear from simple rules.
Adaptation: System changes over time.
Non-linearity: Small changes can cause big effects.
Feedback Loops: System gets feedback from its own actions.
Self-Organization: No central control, system organizes itself.
🧠 Example: Ant colony, traffic system, stock market.
🔹 Role of Verification in Model Correctness:
Verification checks:
👉 “Are we building the model correctly?”
It ensures:
No coding mistakes
Logic flows correctly
Model works as per design
🧠 Simple Tip: “Verification = Checking code logic”
🔹 Example of Distribution in Physical Basis:
Example: The Normal Distribution is used to model:
Height of people
Measurement errors
Temperature changes
🧠 It’s based on natural/physical behaviors that are common in real life.
🔹 What is Imperfection Debugging?
It's the process of fixing errors or unexpected behaviors in a simulation model caused by:
Logical flaws
Wrong assumptions
Programming mistakes
🧠 Think of it as finding bugs that make your model give wrong or weird results.
🧾 Long Questions (Explained Simply)
✅ Macro-validation of Agent-Based Modelling:
Checks if the overall behavior of your agent model matches real-world data or expected
patterns.
📌 Example:
If you simulate traffic, macro-validation means checking if your simulation matches real-world
traffic trends — not just individual car behavior.
✅ What is Common Random Number (CRN)?
A technique used to compare two models or systems fairly by giving both the same random
inputs.
📌 Used in:
Simulation experiments
Reducing variability
🧠 Like giving two players the same dice rolls to compare their strategies.
✅ Bernoulli Distribution:
It’s a distribution with only two outcomes:
✔️ Success (1)
❌ Failure (0)
📌 Used when:
Tossing a coin (Heads/Tails)
Yes or No type problems
✅ Chaos Theory:
It studies systems that:
Are deterministic (rule-based)
But behave in unpredictable ways due to sensitivity to initial conditions
🧠 Small change in input → Big change in output
📌 Example: Weather forecasting, Butterfly Effect
🔥 Paper Questions (Simplified)
✅ Agent-Based Modeling (ABM) Properties:
Agents act independently
They interact with each other
Behavior is rule-based
System behavior emerges from agent actions
✅ Definitions:
Agent: A self-acting entity in a simulation (e.g., a car, person, etc.)
Observer: Watches the simulation, doesn’t take part
Environment: The space where agents live and act
Interaction: How agents affect one another
✅ Operational Analysis as Simulation Tool:
Used in military/logistics to test:
How a system performs
Without needing to run it in real life
📌 Example: Simulating battlefield scenarios to improve decision-making.
✅ Functions of Sheep and Wolves (ABM Example):
Sheep: Agents that try to survive, move, eat grass
Wolves: Predators that hunt sheep
🧠 Used to show interaction, survival, and population dynamics in agent-based models.
✅ Module 7: Military, Logistics & Traffic Simulations
🔹 Military Simulations:
Used for training, planning, and analyzing military operations.
Example: Simulating a battlefield to test strategies without real-world risks.
🔹 Logistics and Supply Chain Simulations:
Used to optimize delivery routes, inventory, and warehouse operations.
Example: Amazon simulates product flow to minimize delivery delays.
🔹 Traffic/Transport System Simulations:
Simulating airports, ports, firefighting, public transport to improve safety and efficiency.
🔹 Business Process Simulations:
Used in call centers, hospitals, etc., to reduce waiting time and improve services.
✅ Module 8: System & Environment
🔹 Definitions:
System: A group of components interacting with each other for a purpose.
🔸 Example: Traffic System
Environment: Everything outside the system that interacts with it.
🔸 Example: Weather affecting traffic
Boundary: Line that separates the system from the environment.
🔹 Examples:
BMW Production System: A system to optimize car production.
Storm System: Natural weather system.
Cyber-Physical System: System integrating computer & physical processes (like smart
traffic lights).
✅ Module 9: System Concepts
🔹 Key Definitions:
Homeostasis: The system’s ability to remain stable.
🔸 Example: Human body temperature
Adaptation: How systems change in response to the environment.
🔸 Example: Animals growing thicker fur in cold climates
Feedback Loop: When system output influences its future behavior.
🔸 Example: Thermostat regulating room temperature
🔹 System Levels:
Microsystem: Small scale (e.g., individual)
Mesosystem: Interaction of microsystems (e.g., school and home)
Exosystem: Indirect influence (e.g., parent's workplace)
Macrosystem: Culture/society level
Chronosystem: Time-based changes (e.g., life events)
✅ Module 10: Components of a System
🔹 Key Terms:
Entity: Object being simulated.
🔸 Example: Customer
Attribute: Property of an entity.
🔸 Example: Age of customer
Activity: Action done by entity.
🔸 Example: Buying a product
State: System’s condition at a time.
🔸 Example: Bank queue is full
Event: An incident that changes system’s state.
🔸 Example: New customer arrives
🔹 Event Types:
Endogenous Event: Comes from within the system.
🔸 Example: Machine breakdown
Exogenous Event: Comes from outside the system.
🔸 Example: Power failure
✅ Module 11: Discrete vs. Continuous Systems
🔹 Discrete System:
Changes occur at specific times.
Example: Customer arrivals, ATM transactions.
🔹 Continuous System:
Changes occur continuously over time.
Example: Water level in a dam, vehicle speed.
✅ Module 12: Modeling a System
🔹 Why Use Models?
To study complex real systems without risk or cost.
Safer than experimenting on the real system.
🔹 When Not to Experiment on Real Systems:
Dangerous
Too expensive
Impractical
🔹 System vs. Model:
System: Real-world structure (e.g., hospital).
Model: Simplified version for study (e.g., simulation of hospital operations).
✅ Module 13: Types of Models
🔹 Model Types:
Physical Models: Real objects.
🔸 Example: Globe
Mathematical Models: Based on equations.
🔸 Example: Physics formula
Simulation Models: Computer-based.
🔸 Example: Hospital simulation
🔹 More Classifications:
Static Models: No time-based change.
🔸 Example: Pie chart
Dynamic Models: Change with time.
🔸 Example: Traffic simulation
Deterministic Models: Fixed input → fixed output.
🔸 Example: Calculator
Stochastic Models: Randomness involved.
🔸 Example: Weather model
✅ Module 14: Discrete Event Simulation (DES)
🔹 DES:
Simulation where the state changes at specific events.
🔹 Methods:
Analytical Methods: Using formulas (math-based)
Numerical Methods: Step-by-step computations
🔹 Manual vs. Computer Simulation:
Manual: Paper/pen method (slow)
Computer: Fast, automated, better for large systems
✅ Module 15: Steps in a Simulation Study
1. Problem Formulation: Define problem clearly
2. Objectives & Plan: What you want to achieve
3. Model Conceptualization: Sketch how the model should work
4. Data Collection: Gather system data
5. Model Translation: Convert concept into code
6. Verification: Check if model works as designed
7. Validation: Compare model with real-world behavior
8. Experimental Design: Decide how to test the model
9. Production Runs & Analysis: Run and analyze the simulation
10. More Runs?: Repeat for improvement
11. Documentation & Reporting: Record all findings
12. Implementation: Use model to support decisions
✅ Module 16: Model Conceptualization
🔹 Key Ideas:
Abstraction: Focus only on important details
Simplification: Keep it simple, but accurate
User Involvement: Involve actual users to ensure the model is useful
Complexity Management: Don’t make the model too complicated
