Modeling of Complex Social. MATH 800 Fall 2011

Similar documents
Bioinformatics: Network Analysis

Learning Network Graph of SIR Epidemic Cascades Using Minimal Hitting Set based Approach

L Modeling and Simulating Social Systems with MATLAB

Temporal Networks. Hiroki Sayama

System dynamic (SD) modeling. Lisa Brouwers

Epidemic spreading on networks

Automata Network Simulator Applied to the Epidemiology of Urban Dengue Fever

Continuous-time stochastic simulation of epidemics in R

The Coral Project: Defending against Large-scale Attacks on the Internet. Chenxi Wang

Math 408R: UT Fall 2016

Modeling and Simulating Social Systems with MATLAB

The coupling effect on VRTP of SIR epidemics in Scale- Free Networks

Lecture: Simulation. of Manufacturing Systems. Sivakumar AI. Simulation. SMA6304 M2 ---Factory Planning and scheduling. Simulation - A Predictive Tool

PharmaSUG China. Compartmental Models in SAS: Application to Model Epidemics Ka Chun Chong and Benny Chung-Ying Zee

Algorithmic Problems in Epidemiology

How Infections Spread on Networks CS 523: Complex Adaptive Systems Assignment 4: Due Dec. 2, :00 pm

Discrete-Event Simulation: A First Course. Steve Park and Larry Leemis College of William and Mary

7/27/2015. UNIT 10A Discrete Simulation. Last Time. Waking Up. How to generate pseudo-random numbers. Using randomness in interesting applications

SEEM: A simulation platform for modeling of infectious disease spreading

Small-World Models and Network Growth Models. Anastassia Semjonova Roman Tekhov

A stochastic agent-based model of pathogen propagation in dynamic multi-relational social networks

Models and Algorithms for Network Immunization

Samuel Coolidge, Dan Simon, Dennis Shasha, Technical Report NYU/CIMS/TR

A&S 320: Mathematical Modeling in Biology

Robotics. Lecture 5: Monte Carlo Localisation. See course website for up to date information.

Markov Chains and Multiaccess Protocols: An. Introduction

Numerical approach estimate

Topic 3: GIS Models 10/2/2017. What is a Model? What is a GIS Model. Geography 38/42:477 Advanced Geomatics

REQUIREMENTS ON WORM MITIGATION TECHNOLOGIES IN MANETS

Overview of the Simulation Process. CS1538: Introduction to Simulations

DATA ITEM DESCRIPTION

THE EFFECT OF SEGREGATION IN NON- REPEATED PRISONER'S DILEMMA

DISCRETE DOMAIN REPRESENTATION FOR SHAPE CONCEPTUALIZATION

An Introduction to Complex Systems Science

Houghton Mifflin MATHEMATICS Level 1 correlated to NCTM Standard

Modeling and Simulation (An Introduction)

Lecture 13: Learning from Demonstration

Estimating R 0 : Solutions

CS224W: Social and Information Network Analysis Jure Leskovec, Stanford University

Microscopic Traffic Simulation

Routing Performance Analysis in Delay Tolerant Networks

Chapter 16. Microscopic Traffic Simulation Overview Traffic Simulation Models

Object-Oriented Programming and Laboratory of Simulation Development

Week 10: DTMC Applications Randomized Routing. Network Performance 10-1

On the parallelization of network diffusion models

Discrete-Event Simulation:

Math 1505G, 2013 Graphs and Matrices

Data mining --- mining graphs

Math Modeling in Java: An S-I Compartment Model

Class #2. Data Models: maps as models of reality, geographical and attribute measurement & vector and raster (and other) data structures

Cloud Computing: "network access to shared pool of configurable computing resources"

Lecture 15. Lecturer: Prof. Sergei Fedotov Calculus and Vectors. Length of a Curve and Parametric Equations

Queuing Systems. 1 Lecturer: Hawraa Sh. Modeling & Simulation- Lecture -4-21/10/2012

III. CONCEPTS OF MODELLING II.

Massachusetts Institute of Technology Sloan School of Management. Understanding Epidemics Using. VensimPLE. For use with VensimPLE, version 6.

The Uneasy Relationship Between Statistical Methods and Anthropological Research. Dr. Dwight Read Dept. of Anthropology and Dept. of Statistics UCLA

Stability Analysis of a Window-based Flow Control Mechanism for TCP Connections with Different Propagation Delays

Dependable and Secure Systems Dependability

Supplementary material to Epidemic spreading on complex networks with community structures

Introduction to C omputational F luid Dynamics. D. Murrin

Uncertain Data Management Non-relational models: Graphs

FAST ESTIMATION OF TIME-VARYING TRANSMISSION RATES

Data Statistics Population. Census Sample Correlation... Statistical & Practical Significance. Qualitative Data Discrete Data Continuous Data

An exact approach to calibrating infectious disease models to surveillance data: The case of HIV and HSV-2

Graphing and Extrapolating Data

INITIAL STUDIES ON WORM PROPAGATION IN MANETS FOR FUTURE ARMY COMBAT SYSTEMS. Robert G. Cole JHU Applied Physics Laboratory Laurel, MD, 20723

Lecture notes. Com Page 1

Social Network Analysis as an Intelligence Technique: the Iranian Nuclear Weapons Program Revisited.

Active Network Tomography: Design and Estimation Issues George Michailidis Department of Statistics The University of Michigan

MODELING OF A MICRO-GRIPPER COMPLIANT JOINT USING COMSOL MULTIPHYSICS SIMULATION

A Survey Based on Product Usability and Feature Fatigue Analysis Methods for Online Product

Math 1311 Section 3.3 Modeling Data with Linear Functions

Modeling of Epidemic Diffusion in Peer-to-Peer File-Sharing Networks

What can we represent as a Surface?

Micro Simulations in ORA. Agenda

nalysis, Control, and Design of Stochastic Flow Systems Limited Storage

Business: Administrative Information Services Crosswalk to AZ Math Standards

Design and Construction of Relational Database for Structural Modeling Verification and Validation. Weiju Ren, Ph. D.

Spline Solutions to Dengue Disease Transmission Model

1. Mathematical Modelling

Regarding Python level necessary for the course

Epidemics and vaccination on weighted graphs

TTEDesigner User s Manual

Dependable and Secure Systems Dependability Master of Science in Embedded Computing Systems

Research Article Data Visualization Using Rational Trigonometric Spline

A Database Supported Modeling Environment for Pandemic Planning and Course of Action Analysis

LR Parsing - The Items

Preprocessing Short Lecture Notes cse352. Professor Anita Wasilewska

SISMID Module 11 Lab 1: EpiFire Tutorial

Modeling Historical and Future Spatio-Temporal Relationships of Moving Objects in Databases

A recursive point process model for infectious diseases.

16 April 2011 Alan, Edison, etc, Saturday.

CHAPTER 3. Elementary Fluid Dynamics

Infectious Disease Models. Angie Dobbs. A Thesis. Presented to. The University of Guelph. In partial fulfilment of requirements.

Approximate Bayesian Computation using Auxiliary Models

OPTIMAL CONTROL MODEL FOR COMPUTER VIRUSES

NEW CHALLENGES IN NETWORK RELIABILITY ANALYSIS

Machine Learning for NLP

Simulation and Analysis of Cascading Failure in Critical Infrastructure

SIMPLIFYING RATIONAL EXPRESSIONS

Transcription:

Modeling of Complex Social Systems MATH 800 Fall 2011

Complex SocialSystems A systemis a set of elements and relationships A complex system is a system whose behavior cannot be easily or intuitively predicted

Complex SocialSystems A systemis a set of elements and relationships A complex system is a system whose behavior cannot be easily or intuitively predicted 5 3 200 300 = (5-3) t + 200

Complex Social Systems Asocial system is a collection of individuals and interactions. A complex social system is a complex system whose behavior is primarily the result of the behavior of individuals

Complex Social Systems Asocial system is a collection of individuals and interactions. A complex social system is a complex system whose behavior is primarily the result of the behavior of individuals Ken Thompson

What is Modeling? is abstraction of reality! is a representation of a particular phenomena, idea, or condition..

What is Mathematical Modeling? Framing questions in/about the real world in mathematical terms. Simplified representations of some real-world entity in equations It is characterized: Variables (the things which change) Parameters (the things which do not change) Functional forms (the relationships)

What is Mathematical Modeling? 5 3 200 300 = (5-3) t + 200 5 3% X X(t) = (5-3% X(t-1)) + 200

Purpose of Modeling In General, Modeling helps Answering specific questions Understanding problems better Communicating with others

First Steps in Modeling Define systems and boundaries. Simplify assumptions. Draw an overall block diagram

Good Models Simple Have clearly defined questions Have clearly stated assumptions Adaptable Reproducible Have clearly defined variables Validated Use the best data available Interpret results with caution

The Modeling Process Conceptual Modeling Mathematical Modeling Computational Modeling Validation

The Modeling Process Conceptual Modeling Mathematical Modeling Computational Modeling Validation

A Conceptual Modeling Diagram The SIR Epidemic Model Transmission (α) Recover (β) Susceptibles(S): Individuals susceptible to the disease Infectious (I): Infected Individuals able to transmit the parasite to others Recovered (R): Individuals that have recovered, are immune or have died from the disease and do not contribute to the transmission of the disease Parameters: α, β Variables: S, I, R

A Conceptual Modeling Diagram The SIR Epidemic Model Death(δ) Transmission (α) Recover (β) (γ) Susceptibles(S): Individuals susceptible to the disease Infectious (I): Infected Individuals able to transmit the parasite to others Recovered (R): Individuals that have recovered Death (D): Individuals that have died from the disease Parameters: α, β, γ, δ Variables: S, I, R, D

Computational Modeling Simulation: Simulation is any technique for analyzing, designing, and operating complex systems. Visualization: Visualization is any technique Visualization: Visualization is any technique for creating images, diagrams, or animations to communicate and describe the behavior of complex systems.

Types of Mathematical Models Linear vs. Non-linear Models In linear models all the variables and the parameters are connected by linear equations. Otherwise the model is non-linear. The SIR Epidemic Model Death(δ) Transmission (α) Recover (β) (γ)

Types of Mathematical Models Aggregate vs. Individual Models Ken Thompson

Types of Mathematical Models Deterministic vs. Stochastic Models Deterministic models have no uncertain components(no parameters are characterized by probability), as opposed to stochastic models The SIR Epidemic Model Death(δ) Transmission (α) Recover (β) (γ)

Types of Mathematical Models Static vs. Dynamic Models A dynamic model refers to a system that changes over time, whereas static model refers to a system that is at steady state Dynamic model is a representation of the behavior of the static components of the system.

Types of Mathematical Models Continuous vs. Discrete Models In discrete Models variables change only at a countable number of points in time, whereas in continuous models variables change in a continuous way. α β X

Types of Mathematical Models Qualitative vs. Quantitative Models Quantitative models lead to a detailed, numerical predication about responses, whereas qualitative models lead to general descriptions about the responses. α β α X β X X