An agent-based model (ABM) is a computational simulation of a complex system using autonomous agents that interact locally. An ABM consists of agents with states and behaviors governed by interaction rules within an environment. ABMs can simulate phenomena that emerge from the interactions of heterogeneous agents, like bird flocking or ant foraging behaviors. The open-source NetLogo platform is commonly used to build ABMs due to its ease of use. ABMs are useful for modeling complex systems where decentralized decisions and local interactions between agents generate global patterns.

1. Introduction to
Agent Based Modeling
Using NetLogo
Prem Sankar C
Research Scholar, Dept of Futures Studies

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Introduction to Agent Based Modeling
● An ABM is a computer simulation program:
● a collection of agents and their states
● the rules governing the interactions of the agents
● the environment (overall system) within which they live.
● ABM for Simulation of Complex Systems
● Helps to simulate artificial societies

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Flying patterns – Birds -Flocking

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Shortest path - Ants
http://agentbase.org/model.html?b24f11b263d0de2610f1#

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ABM Definition
● An agent-based model is a class of computational
models for simulating the actions and interactions of
autonomous agents with a view to assessing their
effects on the system as a whole. - Wikipedia
● Agent Based Models can help analyze and simulate
Complex Systems.

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Where ABM applied?
● Flows: evacuation, traffic, and
customer flowmanagement.
● Markets: stock market,
shopbots and software
agents,and strategic
simulation.
● Organizations: operational
risk and organizational design.
● Diffusion: diffusion of
innovation and adoption
dynamics.

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ABM Examples
● Population Dynamics
● Predator-Prey Dynamics
● Political Dynamics
● Migration Modeling
● Epidemic Simulation
● Crowd modeling
● Pedestrian modeling and simulation
● Policy / Decision making
● Modeling Financial Markets
Assignment: Agent Based Simulation Industrial Applications?

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Components of ABM model
● Space/ Enviornment
● Agents
● Time
● Visualization
● Interaction Rules

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Who is an agent?
● An agent is a thing which does things to things
(Kauffman)
● A discrete entity with its own goals and behaviors
● Autonomous, with a capability to adapt and modify its
behaviors
● has some state
● interacts with other agents mutually modifying
each others’ states
● your model your rules!!!

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OOPs V/s ABM
● Objects
● Class
● Attributes/ Properties
● Procedures/ Methods
● Agents
● Community
● Behaviour Values
● Interactions
● State to State
Yes, agents are objects!

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Agent Environments
Types of Agent Environments

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Agent Interactions
Depending on the environment agents interact in different
manners.
Typically an agent interact with neighbors
For Eg: In a Grid, surrounding cells are the neighborhood.
In a network, the first degree connections are neighbors.

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What is simulation time? How it
calculated?
● A schedule implies a timeline
● ask agents [
# do something
]
advance-tick

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Getting Started - NetLogo

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Introduction to NetLogo
● The Center for Connected Learning (CCL) and Computer-Based
Modeling, Northwestern University, USA.
● Free and Open Source
● The name NetLogo comes from “Network Logo”
● Uses a Procedural Language called Logo
● Other Tools -SWARM, RePast, MASON, MESA etc.
● Docs & tutorial: bit.ly/abm-mesa
http://mesa.readthedocs.io/en/latest/#contributing-back-to-mesa
● Refer Wikipedia ABM tools list

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NetLogo-User and Programming Interface

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Why NetLogo?
Very popular tool (educational purpose).
Easy-to-use language, great for beginners.
Mature platform, rare bugs to none.
Contains many useful already-made primitives and structures.
Great for prototyping models, deploys web Java applets.
Many example available.
Great documentation, active community.
Can be used for complex models also.
Scalability issues.
Becomes slow when things get complicated.
Lack of Object Oriented style, debugger.

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Wolf Sheep Predation model
● This model explores the stability of predator-prey ecosystems. [Biology]
Predator-Prey
Dynamics

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Virus spread in a network
● Demonstrates the spread
of a virus through a
network.
● Each node may be in one
of three states:
susceptible, infected, or
resistant
● referred as SIR model for
epidemic
● Try Dengi !!!

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Game of Life
Game Of Life:
two-dimensional orthogonal grid
Agents are cells, state dead or alive
Rules:
1)Any live cell with fewer than two live
neighbors dies, as if caused by under-
population.
2)Any live cell with two or three live
neighbors lives on to the next generation.
3)Any live cell with more than three live
neighbors dies, as if by overcrowding.
4)Any dead cell with exactly three live
neighbors becomes a live cell, as if by
reproduction.

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Try this !!!
Assignment : List out Steady State patterns

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Preferential Attachment
The model starts with two
nodes connected by an edge.
At each step, a new node is
added. A new node picks an
existing node to connect to
randomly, but with some
bias
Node’s chance of being
selected is directly
proportional to the number
of connections it already has.

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BeeSmart Hive Finding
● The BeeSmart Master model shows the swarm
intelligence of honeybees during their hive-
finding process.
● A swarm of tens of thousands of honeybees
can accurately pick the best new hive site
available among dozens of potential choices
through self-organizing behavior.
Story of Waggle Lab

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When we use ABM ?
1)When there are decisions and behaviors that can be
well-defined.
2)When it is important that agents adapt and change
their behaviors.
3)When it is important that agents have a dynamic
relationship with other agents, and agent relationships
form, change and decay.
4)When the past is no predictor of the future because the
processes of growth and change are dynamic.

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Why we use ABM ?
● Agent-based models represent individuals,
their behaviors and their interactions
● Equation-based models represent aggregates
and their dynamics.
● Agents have decision-making abilities and an
understanding of their environment
● Micro to Macro: Agent behaviors to System
Behaviors

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ABM features
● Bottom-up approach
● No central authority
● Individual behavior is nonlinear
● Agent interactions are heterogeneous
● Studying effects of
– decentralized decision making
– local-global interaction, self-organization, emergence
– heterogeneity in the system

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We have a model, then?
● Every model has a parameters space
● Simulate each possible combination many
times
● Compare what you have found with empirical
values

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The research question is: “how
could the decentralized local
interactions of heterogeneous
autonomous agents generate
a global pattern?

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How to ask for help ?
● Google Search
● Professional Communities
● Ask a question on www.stackoverflow.com
● Please be smart in the title:
● Please be specific in the message:

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Reference
1) Vizzari, EASSS 2009 - Torino – 3-4/9/2009 Tutorial.
2) CM Macal and MJ North, Tutorial on agent-based modeling and simulation,
Journal of Simulation 2010.
3) CM Macal and MJ North, AGENT-BASED MODELING AND SIMULATION,
Proceedings of the 2009 Winter Simulation Conference.
4) Paul Davidsson , Agent Based Social Simulation: A Computer
Science View, Journal of Artificial Societies and Social Simulation vol. 5,
no. 1.
5) Rob Allan, Survey of Agent Based Modelling and Simulation Tools

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Thank you for your attention!