SlideShare a Scribd company logo
1 of 31
Download to read offline
Introduction to
Agent Based Modeling
Using NetLogo
Prem Sankar C
Research Scholar, Dept of Futures Studies
Prem Sankar C - Dept of Futures Studies 2
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
Prem Sankar C - Dept of Futures Studies 3
Flying patterns – Birds -Flocking
Prem Sankar C - Dept of Futures Studies 4
Shortest path - Ants
http://agentbase.org/model.html?b24f11b263d0de2610f1#
Prem Sankar C - Dept of Futures Studies 5
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.
Prem Sankar C - Dept of Futures Studies 6
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.
Prem Sankar C - Dept of Futures Studies 7
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?
Prem Sankar C - Dept of Futures Studies 8
Components of ABM model
● Space/ Enviornment
● Agents
● Time
● Visualization
● Interaction Rules
Prem Sankar C - Dept of Futures Studies 9
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!!!
Prem Sankar C - Dept of Futures Studies 10
OOPs V/s ABM
● Objects
● Class
● Attributes/ Properties
● Procedures/ Methods
● Agents
● Community
● Behaviour Values
● Interactions
● State to State
Yes, agents are objects!
Prem Sankar C - Dept of Futures Studies 11
Agent Environments
Types of Agent Environments
Prem Sankar C - Dept of Futures Studies 12
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.
Prem Sankar C - Dept of Futures Studies 13
What is simulation time? How it
calculated?
● A schedule implies a timeline
● ask agents [
# do something
]
advance-tick
Prem Sankar C - Dept of Futures Studies 14
Getting Started - NetLogo
Prem Sankar C - Dept of Futures Studies 15
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
Prem Sankar C - Dept of Futures Studies 16
NetLogo-User and Programming Interface
Prem Sankar C - Dept of Futures Studies 17
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.
Prem Sankar C - Dept of Futures Studies 18
Wolf Sheep Predation model
● This model explores the stability of predator-prey ecosystems. [Biology]
Predator-Prey
Dynamics
Prem Sankar C - Dept of Futures Studies 19
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 !!!
Prem Sankar C - Dept of Futures Studies 20
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.
Prem Sankar C - Dept of Futures Studies 21
Try this !!!
Assignment : List out Steady State patterns
Prem Sankar C - Dept of Futures Studies 22
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.
Prem Sankar C - Dept of Futures Studies 23
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
Prem Sankar C - Dept of Futures Studies 24
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.
Prem Sankar C - Dept of Futures Studies 25
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
Prem Sankar C - Dept of Futures Studies 26
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
Prem Sankar C - Dept of Futures Studies 27
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
Prem Sankar C - Dept of Futures Studies 28
The research question is: “how
could the decentralized local
interactions of heterogeneous
autonomous agents generate
a global pattern?
Prem Sankar C - Dept of Futures Studies 29
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:
Prem Sankar C - Dept of Futures Studies 30
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
Prem Sankar C - Dept of Futures Studies 31
Thank you for your attention!

More Related Content

What's hot

Temporal difference learning
Temporal difference learningTemporal difference learning
Temporal difference learningJie-Han Chen
 
Model based rl
Model based rlModel based rl
Model based rlSeolhokim
 
Complexity Explained: A brief intro to complex systems
Complexity Explained: A brief intro to complex systemsComplexity Explained: A brief intro to complex systems
Complexity Explained: A brief intro to complex systemsHiroki Sayama
 
Winning Data Science Competitions
Winning Data Science CompetitionsWinning Data Science Competitions
Winning Data Science CompetitionsJeong-Yoon Lee
 
Reinforcement Learning 5. Monte Carlo Methods
Reinforcement Learning 5. Monte Carlo MethodsReinforcement Learning 5. Monte Carlo Methods
Reinforcement Learning 5. Monte Carlo MethodsSeung Jae Lee
 
Deep deterministic policy gradient
Deep deterministic policy gradientDeep deterministic policy gradient
Deep deterministic policy gradientSlobodan Blazeski
 
Intro to Deep Reinforcement Learning
Intro to Deep Reinforcement LearningIntro to Deep Reinforcement Learning
Intro to Deep Reinforcement LearningKhaled Saleh
 
An introduction to reinforcement learning
An introduction to reinforcement learningAn introduction to reinforcement learning
An introduction to reinforcement learningSubrat Panda, PhD
 
Reinforcement Learning 2. Multi-armed Bandits
Reinforcement Learning 2. Multi-armed BanditsReinforcement Learning 2. Multi-armed Bandits
Reinforcement Learning 2. Multi-armed BanditsSeung Jae Lee
 
The principles of simulation system design.pptx
The principles of simulation system design.pptxThe principles of simulation system design.pptx
The principles of simulation system design.pptxubaidullah75790
 
Context-aware Recommendation: A Quick View
Context-aware Recommendation: A Quick ViewContext-aware Recommendation: A Quick View
Context-aware Recommendation: A Quick ViewYONG ZHENG
 
Reinforcement learning
Reinforcement learning Reinforcement learning
Reinforcement learning Chandra Meena
 
Reinforcement learning 7313
Reinforcement learning 7313Reinforcement learning 7313
Reinforcement learning 7313Slideshare
 
Dowhy: An end-to-end library for causal inference
Dowhy: An end-to-end library for causal inferenceDowhy: An end-to-end library for causal inference
Dowhy: An end-to-end library for causal inferenceAmit Sharma
 
"A 30min Introduction to Agent-Based Modelling" for GORS
"A 30min Introduction to Agent-Based Modelling" for GORS"A 30min Introduction to Agent-Based Modelling" for GORS
"A 30min Introduction to Agent-Based Modelling" for GORSBruce Edmonds
 
Netflix talk at ML Platform meetup Sep 2019
Netflix talk at ML Platform meetup Sep 2019Netflix talk at ML Platform meetup Sep 2019
Netflix talk at ML Platform meetup Sep 2019Faisal Siddiqi
 
Machine Learning Pipelines
Machine Learning PipelinesMachine Learning Pipelines
Machine Learning Pipelinesjeykottalam
 
Inference in Bayesian Networks
Inference in Bayesian NetworksInference in Bayesian Networks
Inference in Bayesian Networksguestfee8698
 
Multi-Armed Bandit and Applications
Multi-Armed Bandit and ApplicationsMulti-Armed Bandit and Applications
Multi-Armed Bandit and ApplicationsSangwoo Mo
 

What's hot (20)

Temporal difference learning
Temporal difference learningTemporal difference learning
Temporal difference learning
 
Model based rl
Model based rlModel based rl
Model based rl
 
Complexity Explained: A brief intro to complex systems
Complexity Explained: A brief intro to complex systemsComplexity Explained: A brief intro to complex systems
Complexity Explained: A brief intro to complex systems
 
Winning Data Science Competitions
Winning Data Science CompetitionsWinning Data Science Competitions
Winning Data Science Competitions
 
Reinforcement Learning 5. Monte Carlo Methods
Reinforcement Learning 5. Monte Carlo MethodsReinforcement Learning 5. Monte Carlo Methods
Reinforcement Learning 5. Monte Carlo Methods
 
Deep deterministic policy gradient
Deep deterministic policy gradientDeep deterministic policy gradient
Deep deterministic policy gradient
 
Intro to Deep Reinforcement Learning
Intro to Deep Reinforcement LearningIntro to Deep Reinforcement Learning
Intro to Deep Reinforcement Learning
 
An introduction to reinforcement learning
An introduction to reinforcement learningAn introduction to reinforcement learning
An introduction to reinforcement learning
 
Reinforcement Learning 2. Multi-armed Bandits
Reinforcement Learning 2. Multi-armed BanditsReinforcement Learning 2. Multi-armed Bandits
Reinforcement Learning 2. Multi-armed Bandits
 
The principles of simulation system design.pptx
The principles of simulation system design.pptxThe principles of simulation system design.pptx
The principles of simulation system design.pptx
 
Context-aware Recommendation: A Quick View
Context-aware Recommendation: A Quick ViewContext-aware Recommendation: A Quick View
Context-aware Recommendation: A Quick View
 
Reinforcement learning
Reinforcement learning Reinforcement learning
Reinforcement learning
 
Reinforcement learning 7313
Reinforcement learning 7313Reinforcement learning 7313
Reinforcement learning 7313
 
Tabnet presentation
Tabnet presentationTabnet presentation
Tabnet presentation
 
Dowhy: An end-to-end library for causal inference
Dowhy: An end-to-end library for causal inferenceDowhy: An end-to-end library for causal inference
Dowhy: An end-to-end library for causal inference
 
"A 30min Introduction to Agent-Based Modelling" for GORS
"A 30min Introduction to Agent-Based Modelling" for GORS"A 30min Introduction to Agent-Based Modelling" for GORS
"A 30min Introduction to Agent-Based Modelling" for GORS
 
Netflix talk at ML Platform meetup Sep 2019
Netflix talk at ML Platform meetup Sep 2019Netflix talk at ML Platform meetup Sep 2019
Netflix talk at ML Platform meetup Sep 2019
 
Machine Learning Pipelines
Machine Learning PipelinesMachine Learning Pipelines
Machine Learning Pipelines
 
Inference in Bayesian Networks
Inference in Bayesian NetworksInference in Bayesian Networks
Inference in Bayesian Networks
 
Multi-Armed Bandit and Applications
Multi-Armed Bandit and ApplicationsMulti-Armed Bandit and Applications
Multi-Armed Bandit and Applications
 

Similar to Introduction to Agent Based Modeling Using NetLogo

Predictive analytics semi-supervised learning with GANs
Predictive analytics   semi-supervised learning with GANsPredictive analytics   semi-supervised learning with GANs
Predictive analytics semi-supervised learning with GANsterek47
 
Model evaluation in the land of deep learning
Model evaluation in the land of deep learningModel evaluation in the land of deep learning
Model evaluation in the land of deep learningPramit Choudhary
 
Unified Modeling Language (UML), Object-Oriented Programming Concepts & Desig...
Unified Modeling Language (UML), Object-Oriented Programming Concepts & Desig...Unified Modeling Language (UML), Object-Oriented Programming Concepts & Desig...
Unified Modeling Language (UML), Object-Oriented Programming Concepts & Desig...Isuru Perera
 
Actor critic algorithm
Actor critic algorithmActor critic algorithm
Actor critic algorithmJie-Han Chen
 
Study and development of methods and tools for testing, validation and verif...
 Study and development of methods and tools for testing, validation and verif... Study and development of methods and tools for testing, validation and verif...
Study and development of methods and tools for testing, validation and verif...Emilio Serrano
 
Continuous Evaluation of Collaborative Recommender Systems in Data Stream Man...
Continuous Evaluation of Collaborative Recommender Systems in Data Stream Man...Continuous Evaluation of Collaborative Recommender Systems in Data Stream Man...
Continuous Evaluation of Collaborative Recommender Systems in Data Stream Man...Dr. Cornelius Ludmann
 
Explainable AI in Industry (KDD 2019 Tutorial)
Explainable AI in Industry (KDD 2019 Tutorial)Explainable AI in Industry (KDD 2019 Tutorial)
Explainable AI in Industry (KDD 2019 Tutorial)Krishnaram Kenthapadi
 
achine Learning and Model Risk
achine Learning and Model Riskachine Learning and Model Risk
achine Learning and Model RiskQuantUniversity
 
Machine Learning and AI: Core Methods and Applications
Machine Learning and AI: Core Methods and ApplicationsMachine Learning and AI: Core Methods and Applications
Machine Learning and AI: Core Methods and ApplicationsQuantUniversity
 
Introduction of abm
Introduction of abmIntroduction of abm
Introduction of abmyudiyasik
 
IRJET- Criminal Recognization in CCTV Surveillance Video
IRJET-  	  Criminal Recognization in CCTV Surveillance VideoIRJET-  	  Criminal Recognization in CCTV Surveillance Video
IRJET- Criminal Recognization in CCTV Surveillance VideoIRJET Journal
 
Learning to Learn Model Behavior ( Capital One: data intelligence conference )
Learning to Learn Model Behavior ( Capital One: data intelligence conference )Learning to Learn Model Behavior ( Capital One: data intelligence conference )
Learning to Learn Model Behavior ( Capital One: data intelligence conference )Pramit Choudhary
 
Deep Credit Risk Ranking with LSTM with Kyle Grove
Deep Credit Risk Ranking with LSTM with Kyle GroveDeep Credit Risk Ranking with LSTM with Kyle Grove
Deep Credit Risk Ranking with LSTM with Kyle GroveDatabricks
 
Introduction of abm
Introduction of abmIntroduction of abm
Introduction of abmYudi Yasik
 

Similar to Introduction to Agent Based Modeling Using NetLogo (20)

Predictive analytics semi-supervised learning with GANs
Predictive analytics   semi-supervised learning with GANsPredictive analytics   semi-supervised learning with GANs
Predictive analytics semi-supervised learning with GANs
 
Model evaluation in the land of deep learning
Model evaluation in the land of deep learningModel evaluation in the land of deep learning
Model evaluation in the land of deep learning
 
Unified Modeling Language (UML), Object-Oriented Programming Concepts & Desig...
Unified Modeling Language (UML), Object-Oriented Programming Concepts & Desig...Unified Modeling Language (UML), Object-Oriented Programming Concepts & Desig...
Unified Modeling Language (UML), Object-Oriented Programming Concepts & Desig...
 
Learning to learn Model Behavior: How to use "human-in-the-loop" to explain d...
Learning to learn Model Behavior: How to use "human-in-the-loop" to explain d...Learning to learn Model Behavior: How to use "human-in-the-loop" to explain d...
Learning to learn Model Behavior: How to use "human-in-the-loop" to explain d...
 
Actor critic algorithm
Actor critic algorithmActor critic algorithm
Actor critic algorithm
 
Study and development of methods and tools for testing, validation and verif...
 Study and development of methods and tools for testing, validation and verif... Study and development of methods and tools for testing, validation and verif...
Study and development of methods and tools for testing, validation and verif...
 
Continuous Evaluation of Collaborative Recommender Systems in Data Stream Man...
Continuous Evaluation of Collaborative Recommender Systems in Data Stream Man...Continuous Evaluation of Collaborative Recommender Systems in Data Stream Man...
Continuous Evaluation of Collaborative Recommender Systems in Data Stream Man...
 
Explainable AI in Industry (KDD 2019 Tutorial)
Explainable AI in Industry (KDD 2019 Tutorial)Explainable AI in Industry (KDD 2019 Tutorial)
Explainable AI in Industry (KDD 2019 Tutorial)
 
achine Learning and Model Risk
achine Learning and Model Riskachine Learning and Model Risk
achine Learning and Model Risk
 
Machine Learning and AI: Core Methods and Applications
Machine Learning and AI: Core Methods and ApplicationsMachine Learning and AI: Core Methods and Applications
Machine Learning and AI: Core Methods and Applications
 
MAS
MASMAS
MAS
 
Introduction of abm
Introduction of abmIntroduction of abm
Introduction of abm
 
IRJET- Criminal Recognization in CCTV Surveillance Video
IRJET-  	  Criminal Recognization in CCTV Surveillance VideoIRJET-  	  Criminal Recognization in CCTV Surveillance Video
IRJET- Criminal Recognization in CCTV Surveillance Video
 
Learning to Learn Model Behavior ( Capital One: data intelligence conference )
Learning to Learn Model Behavior ( Capital One: data intelligence conference )Learning to Learn Model Behavior ( Capital One: data intelligence conference )
Learning to Learn Model Behavior ( Capital One: data intelligence conference )
 
Operations Research
Operations ResearchOperations Research
Operations Research
 
Deep Credit Risk Ranking with LSTM with Kyle Grove
Deep Credit Risk Ranking with LSTM with Kyle GroveDeep Credit Risk Ranking with LSTM with Kyle Grove
Deep Credit Risk Ranking with LSTM with Kyle Grove
 
Introduction of abm
Introduction of abmIntroduction of abm
Introduction of abm
 
Ai in finance
Ai in financeAi in finance
Ai in finance
 
Object Oriented Programming
Object Oriented ProgrammingObject Oriented Programming
Object Oriented Programming
 
C3 w5
C3 w5C3 w5
C3 w5
 

More from Premsankar Chakkingal

AI for Educators - Integrating AI in the Classrooms
AI for Educators - Integrating AI in the ClassroomsAI for Educators - Integrating AI in the Classrooms
AI for Educators - Integrating AI in the ClassroomsPremsankar Chakkingal
 
Dynamics of Semantic Networks of Independence Day Speeches
Dynamics of Semantic Networks of Independence Day SpeechesDynamics of Semantic Networks of Independence Day Speeches
Dynamics of Semantic Networks of Independence Day SpeechesPremsankar Chakkingal
 
Introduction to Computational Social Science
Introduction to Computational Social ScienceIntroduction to Computational Social Science
Introduction to Computational Social SciencePremsankar Chakkingal
 
Introductory Talk on Social Network Analysis at Facebook Developer Circle Me...
Introductory Talk on Social Network Analysis  at Facebook Developer Circle Me...Introductory Talk on Social Network Analysis  at Facebook Developer Circle Me...
Introductory Talk on Social Network Analysis at Facebook Developer Circle Me...Premsankar Chakkingal
 
Introduction to Technology Assessments As tool for Forecasting and evaluation...
Introduction to Technology Assessments As tool for Forecasting and evaluation...Introduction to Technology Assessments As tool for Forecasting and evaluation...
Introduction to Technology Assessments As tool for Forecasting and evaluation...Premsankar Chakkingal
 
INTRODUCTION INFORMATION RETRIEVAL EVALUVATION
 INTRODUCTION INFORMATION RETRIEVAL EVALUVATION INTRODUCTION INFORMATION RETRIEVAL EVALUVATION
INTRODUCTION INFORMATION RETRIEVAL EVALUVATIONPremsankar Chakkingal
 
Negotiated Studies - A semantic social network based expert recommender system
Negotiated Studies - A semantic social network based expert recommender systemNegotiated Studies - A semantic social network based expert recommender system
Negotiated Studies - A semantic social network based expert recommender systemPremsankar Chakkingal
 
Business potential of Energy Auditing in Kerala
Business potential of Energy Auditing in KeralaBusiness potential of Energy Auditing in Kerala
Business potential of Energy Auditing in KeralaPremsankar Chakkingal
 
Negotiated Studies Presentation on Social Network Analysis of Knowledge Networks
Negotiated Studies Presentation on Social Network Analysis of Knowledge NetworksNegotiated Studies Presentation on Social Network Analysis of Knowledge Networks
Negotiated Studies Presentation on Social Network Analysis of Knowledge NetworksPremsankar Chakkingal
 
Introduction to Social Network Analysis
Introduction to Social Network AnalysisIntroduction to Social Network Analysis
Introduction to Social Network AnalysisPremsankar Chakkingal
 

More from Premsankar Chakkingal (14)

AI for Educators - Integrating AI in the Classrooms
AI for Educators - Integrating AI in the ClassroomsAI for Educators - Integrating AI in the Classrooms
AI for Educators - Integrating AI in the Classrooms
 
AI in Creative Space
AI in Creative SpaceAI in Creative Space
AI in Creative Space
 
Dynamics of Semantic Networks of Independence Day Speeches
Dynamics of Semantic Networks of Independence Day SpeechesDynamics of Semantic Networks of Independence Day Speeches
Dynamics of Semantic Networks of Independence Day Speeches
 
Introduction to Computational Social Science
Introduction to Computational Social ScienceIntroduction to Computational Social Science
Introduction to Computational Social Science
 
Introductory Talk on Social Network Analysis at Facebook Developer Circle Me...
Introductory Talk on Social Network Analysis  at Facebook Developer Circle Me...Introductory Talk on Social Network Analysis  at Facebook Developer Circle Me...
Introductory Talk on Social Network Analysis at Facebook Developer Circle Me...
 
Introduction to Technology Assessments As tool for Forecasting and evaluation...
Introduction to Technology Assessments As tool for Forecasting and evaluation...Introduction to Technology Assessments As tool for Forecasting and evaluation...
Introduction to Technology Assessments As tool for Forecasting and evaluation...
 
INTRODUCTION INFORMATION RETRIEVAL EVALUVATION
 INTRODUCTION INFORMATION RETRIEVAL EVALUVATION INTRODUCTION INFORMATION RETRIEVAL EVALUVATION
INTRODUCTION INFORMATION RETRIEVAL EVALUVATION
 
Negotiated Studies - A semantic social network based expert recommender system
Negotiated Studies - A semantic social network based expert recommender systemNegotiated Studies - A semantic social network based expert recommender system
Negotiated Studies - A semantic social network based expert recommender system
 
Business potential of Energy Auditing in Kerala
Business potential of Energy Auditing in KeralaBusiness potential of Energy Auditing in Kerala
Business potential of Energy Auditing in Kerala
 
Introduction to Graph Theory
Introduction to Graph TheoryIntroduction to Graph Theory
Introduction to Graph Theory
 
Negotiated Studies Presentation on Social Network Analysis of Knowledge Networks
Negotiated Studies Presentation on Social Network Analysis of Knowledge NetworksNegotiated Studies Presentation on Social Network Analysis of Knowledge Networks
Negotiated Studies Presentation on Social Network Analysis of Knowledge Networks
 
Introduction to Social Network Analysis
Introduction to Social Network AnalysisIntroduction to Social Network Analysis
Introduction to Social Network Analysis
 
Hypothesis Testing for Beginners
Hypothesis Testing for BeginnersHypothesis Testing for Beginners
Hypothesis Testing for Beginners
 
Introduction to Genetic Algorithms
Introduction to Genetic AlgorithmsIntroduction to Genetic Algorithms
Introduction to Genetic Algorithms
 

Recently uploaded

A Business-Centric Approach to Design System Strategy
A Business-Centric Approach to Design System StrategyA Business-Centric Approach to Design System Strategy
A Business-Centric Approach to Design System StrategyUXDXConf
 
Free and Effective: Making Flows Publicly Accessible, Yumi Ibrahimzade
Free and Effective: Making Flows Publicly Accessible, Yumi IbrahimzadeFree and Effective: Making Flows Publicly Accessible, Yumi Ibrahimzade
Free and Effective: Making Flows Publicly Accessible, Yumi IbrahimzadeCzechDreamin
 
10 Differences between Sales Cloud and CPQ, Blanka Doktorová
10 Differences between Sales Cloud and CPQ, Blanka Doktorová10 Differences between Sales Cloud and CPQ, Blanka Doktorová
10 Differences between Sales Cloud and CPQ, Blanka DoktorováCzechDreamin
 
TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...
TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...
TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...marcuskenyatta275
 
Strategic AI Integration in Engineering Teams
Strategic AI Integration in Engineering TeamsStrategic AI Integration in Engineering Teams
Strategic AI Integration in Engineering TeamsUXDXConf
 
Oauth 2.0 Introduction and Flows with MuleSoft
Oauth 2.0 Introduction and Flows with MuleSoftOauth 2.0 Introduction and Flows with MuleSoft
Oauth 2.0 Introduction and Flows with MuleSoftshyamraj55
 
AI presentation and introduction - Retrieval Augmented Generation RAG 101
AI presentation and introduction - Retrieval Augmented Generation RAG 101AI presentation and introduction - Retrieval Augmented Generation RAG 101
AI presentation and introduction - Retrieval Augmented Generation RAG 101vincent683379
 
THE BEST IPTV in GERMANY for 2024: IPTVreel
THE BEST IPTV in  GERMANY for 2024: IPTVreelTHE BEST IPTV in  GERMANY for 2024: IPTVreel
THE BEST IPTV in GERMANY for 2024: IPTVreelreely ones
 
Speed Wins: From Kafka to APIs in Minutes
Speed Wins: From Kafka to APIs in MinutesSpeed Wins: From Kafka to APIs in Minutes
Speed Wins: From Kafka to APIs in Minutesconfluent
 
Intro in Product Management - Коротко про професію продакт менеджера
Intro in Product Management - Коротко про професію продакт менеджераIntro in Product Management - Коротко про професію продакт менеджера
Intro in Product Management - Коротко про професію продакт менеджераMark Opanasiuk
 
Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...
Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...
Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...FIDO Alliance
 
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...CzechDreamin
 
IoT Analytics Company Presentation May 2024
IoT Analytics Company Presentation May 2024IoT Analytics Company Presentation May 2024
IoT Analytics Company Presentation May 2024IoTAnalytics
 
SOQL 201 for Admins & Developers: Slice & Dice Your Org’s Data With Aggregate...
SOQL 201 for Admins & Developers: Slice & Dice Your Org’s Data With Aggregate...SOQL 201 for Admins & Developers: Slice & Dice Your Org’s Data With Aggregate...
SOQL 201 for Admins & Developers: Slice & Dice Your Org’s Data With Aggregate...CzechDreamin
 
How Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdf
How Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdfHow Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdf
How Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdfFIDO Alliance
 
Demystifying gRPC in .Net by John Staveley
Demystifying gRPC in .Net by John StaveleyDemystifying gRPC in .Net by John Staveley
Demystifying gRPC in .Net by John StaveleyJohn Staveley
 
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdfThe Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdfFIDO Alliance
 
PLAI - Acceleration Program for Generative A.I. Startups
PLAI - Acceleration Program for Generative A.I. StartupsPLAI - Acceleration Program for Generative A.I. Startups
PLAI - Acceleration Program for Generative A.I. StartupsStefano
 
Connecting the Dots in Product Design at KAYAK
Connecting the Dots in Product Design at KAYAKConnecting the Dots in Product Design at KAYAK
Connecting the Dots in Product Design at KAYAKUXDXConf
 
Top 10 Symfony Development Companies 2024
Top 10 Symfony Development Companies 2024Top 10 Symfony Development Companies 2024
Top 10 Symfony Development Companies 2024TopCSSGallery
 

Recently uploaded (20)

A Business-Centric Approach to Design System Strategy
A Business-Centric Approach to Design System StrategyA Business-Centric Approach to Design System Strategy
A Business-Centric Approach to Design System Strategy
 
Free and Effective: Making Flows Publicly Accessible, Yumi Ibrahimzade
Free and Effective: Making Flows Publicly Accessible, Yumi IbrahimzadeFree and Effective: Making Flows Publicly Accessible, Yumi Ibrahimzade
Free and Effective: Making Flows Publicly Accessible, Yumi Ibrahimzade
 
10 Differences between Sales Cloud and CPQ, Blanka Doktorová
10 Differences between Sales Cloud and CPQ, Blanka Doktorová10 Differences between Sales Cloud and CPQ, Blanka Doktorová
10 Differences between Sales Cloud and CPQ, Blanka Doktorová
 
TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...
TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...
TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...
 
Strategic AI Integration in Engineering Teams
Strategic AI Integration in Engineering TeamsStrategic AI Integration in Engineering Teams
Strategic AI Integration in Engineering Teams
 
Oauth 2.0 Introduction and Flows with MuleSoft
Oauth 2.0 Introduction and Flows with MuleSoftOauth 2.0 Introduction and Flows with MuleSoft
Oauth 2.0 Introduction and Flows with MuleSoft
 
AI presentation and introduction - Retrieval Augmented Generation RAG 101
AI presentation and introduction - Retrieval Augmented Generation RAG 101AI presentation and introduction - Retrieval Augmented Generation RAG 101
AI presentation and introduction - Retrieval Augmented Generation RAG 101
 
THE BEST IPTV in GERMANY for 2024: IPTVreel
THE BEST IPTV in  GERMANY for 2024: IPTVreelTHE BEST IPTV in  GERMANY for 2024: IPTVreel
THE BEST IPTV in GERMANY for 2024: IPTVreel
 
Speed Wins: From Kafka to APIs in Minutes
Speed Wins: From Kafka to APIs in MinutesSpeed Wins: From Kafka to APIs in Minutes
Speed Wins: From Kafka to APIs in Minutes
 
Intro in Product Management - Коротко про професію продакт менеджера
Intro in Product Management - Коротко про професію продакт менеджераIntro in Product Management - Коротко про професію продакт менеджера
Intro in Product Management - Коротко про професію продакт менеджера
 
Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...
Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...
Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...
 
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
 
IoT Analytics Company Presentation May 2024
IoT Analytics Company Presentation May 2024IoT Analytics Company Presentation May 2024
IoT Analytics Company Presentation May 2024
 
SOQL 201 for Admins & Developers: Slice & Dice Your Org’s Data With Aggregate...
SOQL 201 for Admins & Developers: Slice & Dice Your Org’s Data With Aggregate...SOQL 201 for Admins & Developers: Slice & Dice Your Org’s Data With Aggregate...
SOQL 201 for Admins & Developers: Slice & Dice Your Org’s Data With Aggregate...
 
How Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdf
How Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdfHow Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdf
How Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdf
 
Demystifying gRPC in .Net by John Staveley
Demystifying gRPC in .Net by John StaveleyDemystifying gRPC in .Net by John Staveley
Demystifying gRPC in .Net by John Staveley
 
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdfThe Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
 
PLAI - Acceleration Program for Generative A.I. Startups
PLAI - Acceleration Program for Generative A.I. StartupsPLAI - Acceleration Program for Generative A.I. Startups
PLAI - Acceleration Program for Generative A.I. Startups
 
Connecting the Dots in Product Design at KAYAK
Connecting the Dots in Product Design at KAYAKConnecting the Dots in Product Design at KAYAK
Connecting the Dots in Product Design at KAYAK
 
Top 10 Symfony Development Companies 2024
Top 10 Symfony Development Companies 2024Top 10 Symfony Development Companies 2024
Top 10 Symfony Development Companies 2024
 

Introduction to Agent Based Modeling Using NetLogo

  • 1. Introduction to Agent Based Modeling Using NetLogo Prem Sankar C Research Scholar, Dept of Futures Studies
  • 2. Prem Sankar C - Dept of Futures Studies 2 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
  • 3. Prem Sankar C - Dept of Futures Studies 3 Flying patterns – Birds -Flocking
  • 4. Prem Sankar C - Dept of Futures Studies 4 Shortest path - Ants http://agentbase.org/model.html?b24f11b263d0de2610f1#
  • 5. Prem Sankar C - Dept of Futures Studies 5 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.
  • 6. Prem Sankar C - Dept of Futures Studies 6 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.
  • 7. Prem Sankar C - Dept of Futures Studies 7 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?
  • 8. Prem Sankar C - Dept of Futures Studies 8 Components of ABM model ● Space/ Enviornment ● Agents ● Time ● Visualization ● Interaction Rules
  • 9. Prem Sankar C - Dept of Futures Studies 9 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!!!
  • 10. Prem Sankar C - Dept of Futures Studies 10 OOPs V/s ABM ● Objects ● Class ● Attributes/ Properties ● Procedures/ Methods ● Agents ● Community ● Behaviour Values ● Interactions ● State to State Yes, agents are objects!
  • 11. Prem Sankar C - Dept of Futures Studies 11 Agent Environments Types of Agent Environments
  • 12. Prem Sankar C - Dept of Futures Studies 12 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.
  • 13. Prem Sankar C - Dept of Futures Studies 13 What is simulation time? How it calculated? ● A schedule implies a timeline ● ask agents [ # do something ] advance-tick
  • 14. Prem Sankar C - Dept of Futures Studies 14 Getting Started - NetLogo
  • 15. Prem Sankar C - Dept of Futures Studies 15 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
  • 16. Prem Sankar C - Dept of Futures Studies 16 NetLogo-User and Programming Interface
  • 17. Prem Sankar C - Dept of Futures Studies 17 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.
  • 18. Prem Sankar C - Dept of Futures Studies 18 Wolf Sheep Predation model ● This model explores the stability of predator-prey ecosystems. [Biology] Predator-Prey Dynamics
  • 19. Prem Sankar C - Dept of Futures Studies 19 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 !!!
  • 20. Prem Sankar C - Dept of Futures Studies 20 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.
  • 21. Prem Sankar C - Dept of Futures Studies 21 Try this !!! Assignment : List out Steady State patterns
  • 22. Prem Sankar C - Dept of Futures Studies 22 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.
  • 23. Prem Sankar C - Dept of Futures Studies 23 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
  • 24. Prem Sankar C - Dept of Futures Studies 24 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.
  • 25. Prem Sankar C - Dept of Futures Studies 25 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
  • 26. Prem Sankar C - Dept of Futures Studies 26 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
  • 27. Prem Sankar C - Dept of Futures Studies 27 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
  • 28. Prem Sankar C - Dept of Futures Studies 28 The research question is: “how could the decentralized local interactions of heterogeneous autonomous agents generate a global pattern?
  • 29. Prem Sankar C - Dept of Futures Studies 29 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:
  • 30. Prem Sankar C - Dept of Futures Studies 30 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
  • 31. Prem Sankar C - Dept of Futures Studies 31 Thank you for your attention!