L Modeling and Simulating Social Systems with MATLAB
|
|
|
- Grace Benson
- 5 years ago
- Views:
Transcription
1 L Modeling and Simulating Social Systems with MATLAB Lecture 4 Cellular Automata Karsten Donnay and Stefano Balietti Chair of Sociology, in particular of Modeling and Simulation ETH Zürich
2 Schedule of the course Introduction to MATLAB Working on projects (seminar theses) Create and Submit a Research Plan Introduction to social-science modeling and simulation Handing in seminar thesis and giving a presentation (final deadlines to be communicated) K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 2
3 Goals of Lecture 4: students will 1. Consolidate their knowledge of dynamical systems, through brief repetition of the main concepts and revision of the exercises. 2. Understand the important concept of Cellular Automata as discrete representations of interactions on an abstract grid (or configuration space). 3. Get familiar with the basic notion of Neighborhoods which is important for the definition of Cellular Automata. 4. Implement simple Cellular Automata in MATLAB (Game of Life, Highway Simulation, Epidemics: Kermack- McKendrick model revisited) K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 3
4 Repetition dynamical systems described by a set of differential equations (example: Lotka-Volterra) numerical solutions iteratively for instances using 1 st Euler s Method (example: Kermack-McKendrick) the values and ranges of parameters critically matter; they determine which dynamics the model represents (Ex. 2, the ratio of recuperation to infection parameter determines the epidemiological threshold) time resolution in Euler Method must be sufficiently high to capture ( fast ) system dynamics (Ex. 3) not all MATLAB-own ODE solvers work equally well for every dynamical system under consideration (Ex. 3) K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 4
5 Projects Suggested Topics 1 Artificial Financial Markets 7 Emergence of Culture 13 Language Formation 19 Specialization of Labor 2 Civil Violence 8 Emergence of Values 14 Learning 20 Traffic Dynamics 3 Collective Behavior 9 Evacuation Bottleneck 15 Opinion Formation 21 Trail Formation 4 Disaster Spreading 5 Emergence of Conventions 10 Friendship Network Formation 11 Innovation Diffusion 16 Pedestrian Dynamics 17 Self-organized criticality 22 Wikipedia 23 Modeling Peer Review 6 Emergence of Cooperation 12 Interstate Conflict 18 Social Networks Evolution 24 Sequential Invest. Game K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 5
6 Project Implementation of a model from the Social Science literature in MATLAB 1 week left: Form a group of two or three persons Choose a topic among the project suggestions (available on line) or propose your own idea Fill in the Research Plan (available on line) and send it to sbalietti@ethz.ch AND kdonnay@ethz.ch by Subject line: [MATLAB FS11] name1,name2,name3 E.g. [MATLAB FS11] donnay, balietti K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 6
7 Research Plan Structure 1-2 (not more!) pages stating: Brief, general introduction to the problem Fundamental questions you want to try to answer Existing literature you will base your model on and possible extensions Research methods you are planning to use Use a Word/Openoffice format, to easily track changes It will become the first page of your report K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 7
8 Research Plan Upon submission, the Research Plan can: be accepted be accepted under revision be modified later on only if change is justified (create new version) Talk to us if you are not sure Final deadline for signing-up for a project has been extended to: Monday March 21 th K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 8
9 Cellular Automaton (plural: Automata) A cellular automaton is a rule, defining how the state of a cell in a grid is updated, depending on the states of its neighbor cells. They are represented as grids with arbitrary dimension. Cellular-automata simulations are discrete both in time and space K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 9
10 Cellular Automaton The grid can have an arbitrary number of dimensions: 1-dimensional cellular automaton 2-dimensional cellular automaton K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 10
11 Moore Neighborhood The cells are interacting with each neighbor cells, and the neighborhood can be defined in different ways, e.g. the Moore neighborhood: 1 st order Moore neighborhood 2 nd order Moore neighborhood K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 11
12 Von-Neumann Neighborhood The cells are interacting with each neighbor cells, and the neighborhood can be defined in different ways, e.g. the Von-Neumann neighborhood: 1 st order Von-Neumann neighborhood 2 nd order Von-Neumann neighborhood K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 12
13 Game of Life N i = Number of 1 st order Moore neighbors to cell i that are activated. For each cell i: 1. Deactivate: If N i <2 or N i >3. 2. Activate: if cell i is deactivated and N i = K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 13
14 Highway Simulation As an example of a 1-dimensional cellular automaton, we will present a highway simulation. For each car at cell i: 1. Stay: If the cell directly to the right is occupied. 2. Move: Otherwise, move one step to the right, with probability p Move to the next cell, with the probability p K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 14
15 Highway Simulation We have prepared some files for the highway simulations: draw_car.m : Draws a car, with the function draw_car(x0, y0, w, h) simulate_cars.m: Runs the simulation, with the function simulate_cars(moveprob, inflow, withgraphics) K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 15
16 Highway Simulation Running the simulation is done like this: simulate_cars(0.9, 0.2, true) K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 16
17 Kermack-McKendrick Model In lecture 3, we introduced the Kermack- McKendrick model, used for simulating disease spreading. We will now implement the model again, but this time instead of using differential equations we define it within the cellular-automata framework K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 17
18 Kermack-McKendrick Model The Kermack-McKendrick model is specified as: S: Susceptible persons I: Infected persons R: Removed (immune) persons β: Infection rate γ: Immunity rate R S γ recovery β transmission I K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 18
19 Kermack-McKendrick Model The Kermack-McKendrick model is specified as: S: Susceptible persons I: Infected persons R: Removed (immune) persons β: Infection rate γ: Immunity rate K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 19
20 Kermack-McKendrick Model The Kermack-McKendrick model is specified as: S: Susceptible persons I: Infected persons R: Removed (immune) persons β: Infection rate γ: Immunity rate K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 20
21 Kermack-McKendrick Model The Kermack-McKendrick model is specified as: S: Susceptible persons I: Infected persons R: Removed (immune) persons β: Infection rate γ: Immunity rate K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 21
22 Kermack-McKendrick Model The Kermack-McKendrick model is specified as: S: Susceptible persons I: Infected persons R: Removed (immune) persons β: Infection rate γ: Immunity rate K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 22
23 Kermack-McKendrick Model The Kermack-McKendrick model is specified as: For the MATLAB implementation, we need to decode the states {S, I, R}={0, 1, 2} in a matrix x. S S S S S S S I I I S S S S I I I I S S S R I I I S S S R I I I S S S I I I S S S S I S S S S S S K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 23
24 Kermack-McKendrick Model The Kermack-McKendrick model is specified as: We now define a 2-dimensional cellularautomaton, by defining a grid (matrix) x, where each of the cells is in one of the states: 0: Susceptible 1: Infected 2: Recovered K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 24
25 Kermack-McKendrick Model The Kermack-McKendrick model is specified as: At each time step, the cells can change states according to: A Susceptible individual can be infected by an Infected neighbor with probability β, i.e. State 0 -> 1, with probability β. An individual can recover from an infection with probability γ, i.e. State 1 -> 2, with probability γ K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 25
26 Cellular-Automaton Implementation Implementation of a 2-dimensional cellularautomaton model in MATLAB is done like this: Iterate the time variable, t Iterate over all cells, i=1..n, j=1..n Iterate over all neighbors, k=1..m The iteration over the cells can be done either sequentially, or randomly K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 26
27 Cellular-Automaton Implementation Sequential update: K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 27
28 Cellular-Automaton Implementation Sequential update: K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 28
29 Cellular-automaton implementation Sequential update: K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 29
30 Cellular-automaton implementation Sequential update: K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 30
31 Cellular-automaton implementation Sequential update: K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 31
32 Cellular-automaton implementation Sequential update: K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 32
33 Cellular-automaton implementation Sequential update: K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 33
34 Cellular-automaton implementation Sequential update: K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 34
35 Cellular-automaton implementation Sequential update: K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 35
36 Cellular-automaton implementation Sequential update: K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 36
37 Cellular-automaton implementation Sequential update: K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 37
38 Cellular-automaton implementation Sequential update: K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 38
39 Cellular-automaton implementation Sequential update: K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 39
40 Cellular-automaton implementation Sequential update: K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 40
41 Cellular-automaton implementation Sequential update: K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 41
42 Cellular-automaton implementation Sequential update: K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 42
43 Cellular-automaton implementation Sequential update: K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 43
44 Cellular-automaton implementation Random update: K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 44
45 Cellular-automaton implementation Random update: K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 45
46 Cellular-automaton implementation Random update: K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 46
47 Cellular-automaton implementation Random update: K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 47
48 Cellular-automaton implementation Random update: K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 48
49 Cellular-automaton implementation Random update: K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 49
50 Cellular-automaton implementation Random update: K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 50
51 Cellular-automaton implementation Random update: K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 51
52 Cellular-automaton implementation Random update: K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 52
53 Boundary Conditions The boundary conditions can be any of the following: Periodic: The grid is wrapped, so that what crosses a border is reappearing at the other side of the grid. Fixed: Agents are not influenced by what happens at the other side of a border K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 53
54 Boundary Conditions The boundary conditions can be any of the following: Fixed boundaries Periodic boundaries K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 54
55 MATLAB Implementation of the Kermack- McKendrick Model K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 55
56 MATLAB implementation Set parameter values K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 56
57 MATLAB implementation Define grid, x K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 57
58 MATLAB implementation Define neighborhood K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 58
59 Main loop. Iterate the MATLAB implementation time variable, t K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 59
60 Iterate over all cells, MATLAB implementation i=1..n, j=1..n K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 60
61 For each cell i, j: Iterate MATLAB implementation over the neighbors K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 61
62 The model, i.e. updating MATLAB implementation rule goes here K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 62
63 Breaking Execution When running large computations or animations, the execution can be stopped by pressing Ctrl+C in the main window: K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 63
64 Exercise 1 Download the files draw_car.m and simulate_cars.m from the course web page, Investigate how the flow (moving vehicles per time step) depends on the density (occupancy 0%..100%) in the simulator. This relation is called the fundamental diagram in transportation engineering K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 64
65 Exercise 2 Download the file disease.m which is an implementation of the Kermack-McKendrick model as a Cellular Automaton. Plot the relative fractions of the states S, I, R, as a function of time, and see if the curves look the same as for the old implementation K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 65
66 Exercise 2b Modify the model in the following ways: Change from the 1 st order Moore neighborhood to a 2 nd and 3 rd order Moore neighborhood. Make it possible for Removed individuals to change state back to Susceptible K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 66
67 References Wolfram, Stephen, A New Kind of Science. Wolfram Media, Inc., May 14, proj_gamelife/conwayscientificamerican.htm Schelling, Thomas C "Dynamic Models of Segregation." Journal of Mathematical Sociology 1: K. Donnay & S. Balietti / kdonnay@ethz.ch sbalietti@ethz.ch 67
Modeling and Simulating Social Systems with MATLAB
Modeling and Simulating Social Systems with MATLAB Lecture 4 Cellular Automata Olivia Woolley, Tobias Kuhn, Dario Biasini, Dirk Helbing Chair of Sociology, in particular of Modeling and Simulation ETH
L Modeling and Simulating Social Systems with MATLAB
851-0585-04L Modeling and Simulating Social Systems with MATLAB Lecture 6 Introduction to Graphs/Networks Karsten Donnay and Stefano Balietti Chair of Sociology, in particular of Modeling and Simulation
L Modeling and Simulating Social Systems with MATLAB
851-0585-04L Modeling and Simulating Social Systems with MATLAB Lecture 3 GIT Connected Karsten Donnay and Stefano Balietti Chair of Sociology, in particular of Modeling and Simulation ETH Zürich 2012-10-08
COMPUTER SIMULATION OF COMPLEX SYSTEMS USING AUTOMATA NETWORKS K. Ming Leung
POLYTECHNIC UNIVERSITY Department of Computer and Information Science COMPUTER SIMULATION OF COMPLEX SYSTEMS USING AUTOMATA NETWORKS K. Ming Leung Abstract: Computer simulation of the dynamics of complex
Modeling and Simulating Social Systems with MATLAB
Modeling and Simulating Social Systems with MATLAB Lecture 7 Game Theory / Agent-Based Modeling Stefano Balietti, Olivia Woolley, Lloyd Sanders, Dirk Helbing Computational Social Science ETH Zürich 02-11-2015
Epidemic spreading on networks
Epidemic spreading on networks Due date: Sunday October 25th, 2015, at 23:59. Always show all the steps which you made to arrive at your solution. Make sure you answer all parts of each question. Always
UNIT 9C Randomness in Computation: Cellular Automata Principles of Computing, Carnegie Mellon University
UNIT 9C Randomness in Computation: Cellular Automata 1 Exam locations: Announcements 2:30 Exam: Sections A, B, C, D, E go to Rashid (GHC 4401) Sections F, G go to PH 125C. 3:30 Exam: All sections go to
Modeling of Complex Social. MATH 800 Fall 2011
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
Cellular Automata. Cellular Automata contains three modes: 1. One Dimensional, 2. Two Dimensional, and 3. Life
Cellular Automata Cellular Automata is a program that explores the dynamics of cellular automata. As described in Chapter 9 of Peak and Frame, a cellular automaton is determined by four features: The state
Networks in economics and finance. Lecture 1 - Measuring networks
Networks in economics and finance Lecture 1 - Measuring networks What are networks and why study them? A network is a set of items (nodes) connected by edges or links. Units (nodes) Individuals Firms Banks
Cellular Automata. Nicholas Geis. January 22, 2015
Cellular Automata Nicholas Geis January 22, 2015 In Stephen Wolfram s book, A New Kind of Science, he postulates that the world as we know it and all its complexities is just a simple Sequential Dynamical
COMPUTER EXERCISE: POPULATION DYNAMICS IN SPACE September 3, 2013
COMPUTER EXERCISE: POPULATION DYNAMICS IN SPACE September 3, 2013 Objectives: Introduction to coupled maps lattice as a basis for spatial modeling Solve a spatial Ricker model to investigate how wave speed
10.2 Diffusion and Cellular Automata
10.2 Diffusion and Cellular Automata Simulating Motion: Cellular Automata If all we have to work with is a grid of cells (spreadsheet), how can we simulate a random walk? Moving a value from one cell to
Drawdown Automata, Part 1: Basic Concepts
Drawdown Automata, Part 1: Basic Concepts Cellular Automata A cellular automaton is an array of identical, interacting cells. There are many possible geometries for cellular automata; the most commonly
Modeling and Simulating Social Systems with MATLAB
Modeling and Simulating Social Systems with MATLAB Lecture 8 Introduction to Graphs/Networks Olivia Woolley, Stefano Balietti, Lloyd Sanders, Dirk Helbing Chair of Sociology, in particular of Modeling
CUDA. Fluid simulation Lattice Boltzmann Models Cellular Automata
CUDA Fluid simulation Lattice Boltzmann Models Cellular Automata Please excuse my layout of slides for the remaining part of the talk! Fluid Simulation Navier Stokes equations for incompressible fluids
L Modelling and Simulating Social Systems with MATLAB
851-0585-04L Modelling and Simulating Social Systems with MATLAB Lesson 6 Graphs (Networks) Anders Johansson and Wenjian Yu (with S. Lozano and S. Wehrli) ETH Zürich 2010-03-29 Lesson 6 Contents History:
CELLULAR AUTOMATA IN MATHEMATICAL MODELING JOSH KANTOR. 1. History
CELLULAR AUTOMATA IN MATHEMATICAL MODELING JOSH KANTOR 1. History Cellular automata were initially conceived of in 1948 by John von Neumann who was searching for ways of modeling evolution. He was trying
Robots & Cellular Automata
Integrated Seminar: Intelligent Robotics Robots & Cellular Automata Julius Mayer Table of Contents Cellular Automata Introduction Update Rule 3 4 Neighborhood 5 Examples. 6 Robots Cellular Neural Network
High-Performance Computing
Informatik und Angewandte Kognitionswissenschaft Lehrstuhl für Hochleistungsrechnen Rainer Schlönvoigt Thomas Fogal Prof. Dr. Jens Krüger High-Performance Computing http://hpc.uni-duisburg-essen.de/teaching/wt2013/pp-nbody.html
Temporal Networks. Hiroki Sayama
Temporal Networks Hiroki Sayama sayama@binghamton.edu Temporal networks Networks whose topologies and activities change over time Heavily data-driven research E.g. human contacts (email, social media,
GPU-based Distributed Behavior Models with CUDA
GPU-based Distributed Behavior Models with CUDA Courtesy: YouTube, ISIS Lab, Universita degli Studi di Salerno Bradly Alicea Introduction Flocking: Reynolds boids algorithm. * models simple local behaviors
Cascades. Rik Sarkar. Social and Technological Networks. University of Edinburgh, 2018.
Cascades Social and Technological Networks Rik Sarkar University of Edinburgh, 2018. Course Solutions to Ex0 are up Make sure you are comfortable with this material Notes 1 with exercise questions are
Small-World Models and Network Growth Models. Anastassia Semjonova Roman Tekhov
Small-World Models and Network Growth Models Anastassia Semjonova Roman Tekhov Small world 6 billion small world? 1960s Stanley Milgram Six degree of separation Small world effect Motivation Not only friends:
How Infections Spread on Networks CS 523: Complex Adaptive Systems Assignment 4: Due Dec. 2, :00 pm
1 Introduction How Infections Spread on Networks CS 523: Complex Adaptive Systems Assignment 4: Due Dec. 2, 2015 5:00 pm In this assignment you will investigate the spread of disease on networks. In class
Automata Network Simulator Applied to the Epidemiology of Urban Dengue Fever
Automata Network Simulator Applied to the Epidemiology of Urban Dengue Fever Henrique F. Gagliardi 1,3, Fabrício A.B. da Silva 3, and Domingos Alves 1,2 1 Laboratório de Computação Científica Aplicada
NUMB3RS Activity: Hide and Seep. Episode: Waste Not
Teacher Page 1 : Hide and Seep Episode: Waste Not Topic: Visual mathematical modeling Grade Level: 9-12 Objective: Students create a visual model of liquid percolating through the soil with a cellular
The coupling effect on VRTP of SIR epidemics in Scale- Free Networks
The coupling effect on VRTP of SIR epidemics in Scale- Free Networks Kiseong Kim iames@kaist.ac.kr Sangyeon Lee lsy5518@kaist.ac.kr Kwang-Hyung Lee* khlee@kaist.ac.kr Doheon Lee* dhlee@kaist.ac.kr ABSTRACT
Advanced 1D/2D Modeling Using HEC-RAS
Advanced 1D/2D Modeling Using HEC-RAS Davis, California Objectives This is an advanced course in applying computer program HEC-RAS. The course provides participants with the knowledge to effectively use
EP2200 Queueing theory and teletraffic systems
EP2200 Queueing theory and teletraffic systems Viktoria Fodor Laboratory of Communication Networks School of Electrical Engineering Lecture 1 If you want to model networks Or a complex data flow A queue's
CSCI5070 Advanced Topics in Social Computing
CSCI5070 Advanced Topics in Social Computing Irwin King The Chinese University of Hong Kong king@cse.cuhk.edu.hk!! 2012 All Rights Reserved. Outline Scale-Free Networks Generation Properties Analysis Dynamic
Math 408R: UT Fall 2016
Mini-Project 2: SIR using Matlab, Due October 7 In this assignment, you will be asked to run, modify, discuss, and print output from the SIREulers program you have on Matlab (which may be found on our
Cellular Learning Automata-Based Color Image Segmentation using Adaptive Chains
Cellular Learning Automata-Based Color Image Segmentation using Adaptive Chains Ahmad Ali Abin, Mehran Fotouhi, Shohreh Kasaei, Senior Member, IEEE Sharif University of Technology, Tehran, Iran abin@ce.sharif.edu,
EP2200 Queueing theory and teletraffic systems
EP2200 Queueing theory and teletraffic systems Viktoria Fodor Laboratory of Communication Networks School of Electrical Engineering Lecture 1 If you want to model networks Or a complex data flow A queue's
High-Performance Computing
Informatik und Angewandte Kognitionswissenschaft Lehrstuhl für Hochleistungsrechnen Thomas Fogal Prof. Dr. Jens Krüger High-Performance Computing http://hpc.uni-due.de/teaching/wt2014/nbody.html Exercise
Chapter 1: Building Blocks of Programming
Chapter 1: Building Blocks of Programming (Completion Time: 4 weeks) Topics: Pseudocode An introductions into express computational ideas in a language that can be translated to code. Used correctly, thinking
Generalized Coordinates for Cellular Automata Grids
Generalized Coordinates for Cellular Automata Grids Lev Naumov Saint-Peterburg State Institute of Fine Mechanics and Optics, Computer Science Department, 197101 Sablinskaya st. 14, Saint-Peterburg, Russia
Graph Theory and its Applications
Department of Mathematics and Statistics deeringj@goldmail.etsu.edu October 17, 2012 What is a Graph? Introduction Graphs Graph Theory Simply a modeling tool or set of relationships Graphs Graph Theory
Two-dimensional Totalistic Code 52
Two-dimensional Totalistic Code 52 Todd Rowland Senior Research Associate, Wolfram Research, Inc. 100 Trade Center Drive, Champaign, IL The totalistic two-dimensional cellular automaton code 52 is capable
Application of Two-dimensional Periodic Cellular Automata in Image Processing
International Journal of Computer, Mathematical Sciences and Applications Serials Publications Vol. 5, No. 1-2, January-June 2011, pp. 49 55 ISSN: 0973-6786 Application of Two-dimensional Periodic Cellular
Complex Dynamics in Life-like Rules Described with de Bruijn Diagrams: Complex and Chaotic Cellular Automata
Complex Dynamics in Life-like Rules Described with de Bruijn Diagrams: Complex and Chaotic Cellular Automata Paulina A. León Centro de Investigación y de Estudios Avanzados Instituto Politécnico Nacional
TSC 220. complex systems
TSC 220 complex systems a complex system is a set of interconnected parts making an integrated whole...... that exhibits behavior not obvious from the properties of the parts 5:1 gear ratio not a complex
Enhanced Cellular Automata for Image Noise Removal
Enhanced Cellular Automata for Image Noise Removal Abdel latif Abu Dalhoum Ibraheem Al-Dhamari a.latif@ju.edu.jo ibr_ex@yahoo.com Department of Computer Science, King Abdulla II School for Information
ENGR 1181 MATLAB 09: For Loops 2
ENGR 1181 MATLAB 09: For Loops Learning Objectives 1. Use more complex ways of setting the loop index. Construct nested loops in the following situations: a. For use with two dimensional arrays b. For
Data mining --- mining graphs
Data mining --- mining graphs University of South Florida Xiaoning Qian Today s Lecture 1. Complex networks 2. Graph representation for networks 3. Markov chain 4. Viral propagation 5. Google s PageRank
Lagrangian methods and Smoothed Particle Hydrodynamics (SPH) Computation in Astrophysics Seminar (Spring 2006) L. J. Dursi
Lagrangian methods and Smoothed Particle Hydrodynamics (SPH) Eulerian Grid Methods The methods covered so far in this course use an Eulerian grid: Prescribed coordinates In `lab frame' Fluid elements flow
How Do Pedestrians find their Way? Results of an experimental study with students compared to simulation results
How Do Pedestrians find their Way? Results of an experimental study with students compared to simulation results Angelika Kneidl Computational Modeling and Simulation Group, Technische Universität München,
Failure in Complex Social Networks
Journal of Mathematical Sociology, 33:64 68, 2009 Copyright # Taylor & Francis Group, LLC ISSN: 0022-250X print/1545-5874 online DOI: 10.1080/00222500802536988 Failure in Complex Social Networks Damon
Partial Differential Equations
Simulation in Computer Graphics Partial Differential Equations Matthias Teschner Computer Science Department University of Freiburg Motivation various dynamic effects and physical processes are described
THE EFFECT OF SEGREGATION IN NON- REPEATED PRISONER'S DILEMMA
THE EFFECT OF SEGREGATION IN NON- REPEATED PRISONER'S DILEMMA Thomas Nordli University of South-Eastern Norway, Norway ABSTRACT This article consolidates the idea that non-random pairing can promote the
Cellular Automata and Roundabout Traffic Simulation
Cellular Automata and Roundabout Traffic Simulation Enrico G. Campari 1, Giuseppe Levi 1, and Vittorio Maniezzo 2 1 Scienze dell Informazione dell Università di Bologna, sede di Cesena via Sacchi, 3 I-47023
A SIMPLE MODEL OF THE BELOUSOV-ZHABOTINSKY REACTION FROM FIRST PRINCIPLES
A SIMPLE MODEL OF THE BELOUSOV-ZHABOTINSKY REACTION FROM FIRST PRINCIPLES Alasdair Turner IMPLEMENTATION NOTE A Simple Model of the Belousov-Zhabotinsky Reaction from First Principles Alasdair Turner Bartlett
Conway s Game of Life Wang An Aloysius & Koh Shang Hui
Wang An Aloysius & Koh Shang Hui Winner of Foo Kean Pew Memorial Prize and Gold Award Singapore Mathematics Project Festival 2014 Abstract Conway s Game of Life is a cellular automaton devised by the British
CSE151 Assignment 2 Markov Decision Processes in the Grid World
CSE5 Assignment Markov Decision Processes in the Grid World Grace Lin A484 gclin@ucsd.edu Tom Maddock A55645 tmaddock@ucsd.edu Abstract Markov decision processes exemplify sequential problems, which are
Variations on Genetic Cellular Automata
Variations on Genetic Cellular Automata Alice Durand David Olson Physics Department amdurand@ucdavis.edu daolson@ucdavis.edu Abstract: We investigated the properties of cellular automata with three or
Assignment 2 in Simulation of Telesystems Laboratory exercise: Introduction to Simulink and Communications Blockset
Mid Sweden University Revised: 2013-11-12 Magnus Eriksson Assignment 2 in Simulation of Telesystems Laboratory exercise: Introduction to Simulink and Communications Blockset You are expected to conclude
Tie strength, social capital, betweenness and homophily. Rik Sarkar
Tie strength, social capital, betweenness and homophily Rik Sarkar Networks Position of a node in a network determines its role/importance Structure of a network determines its properties 2 Today Notion
Self-formation, Development and Reproduction of the Artificial System
Solid State Phenomena Vols. 97-98 (4) pp 77-84 (4) Trans Tech Publications, Switzerland Journal doi:.48/www.scientific.net/ssp.97-98.77 Citation (to be inserted by the publisher) Copyright by Trans Tech
REINFORCEMENT LEARNING: MDP APPLIED TO AUTONOMOUS NAVIGATION
REINFORCEMENT LEARNING: MDP APPLIED TO AUTONOMOUS NAVIGATION ABSTRACT Mark A. Mueller Georgia Institute of Technology, Computer Science, Atlanta, GA USA The problem of autonomous vehicle navigation between
The Ising Model. George Legrady Studio, March 2018
The Ising Model The Ising Model series consists of a number of computer-generated animations and works-on-paper that consist of a matrix of cells that transition between 2 states, taking stock of their
Cellular Automata Simulations
Cellular Automata Simulations Tan Kok Cheng, Anthony School of Physics, Georgia Institute of Technology School of Physical and Mathematical Sciences, Nanyang Technological University Shah, Karan School
Interactive Design and Visualization of Urban Spaces using Geometrical and Behavioral Modeling
Interactive Design and Visualization of Urban Spaces using Geometrical and Behavioral Modeling Carlos Vanegas 1,4,5 Daniel Aliaga 1 Bedřich Beneš 2 Paul Waddell 3 1 Computer Science, Purdue University,
Math 3820 Project. 1 Typeset or handwritten? Guidelines
Math 3820 Project Guidelines Abstract These are some recommendations concerning the projects in Math 3820. 1 Typeset or handwritten? Although handwritten reports will be accepted, I strongly recommended
SIMULATION OF ARTIFICIAL SYSTEMS BEHAVIOR IN PARAMETRIC EIGHT-DIMENSIONAL SPACE
78 Proceedings of the 4 th International Conference on Informatics and Information Technology SIMULATION OF ARTIFICIAL SYSTEMS BEHAVIOR IN PARAMETRIC EIGHT-DIMENSIONAL SPACE D. Ulbikiene, J. Ulbikas, K.
Ancient Kiln Landscape Evolution Based on Particle Swarm Optimization and Cellular Automata Model
, pp.218-223 http://dx.doi.org/10.14257/astl.2014.53.46 Ancient Kiln Landscape Evolution Based on Particle Swarm Optimization and Cellular Automata Model Tao Liu 1 *, Xuan Xiao 2, Donglan Ying 3 E-mail:
1/16. Emergence in Artificial Life. Sebastian Marius Kirsch Back Close
1/16 Emergence in Artificial Life Sebastian Marius Kirsch skirsch@moebius.inka.de 2/16 Artificial Life not life as it is, but life as it could be very recent field of science first a-life conference in
CITS2401 Computer Analysis & Visualisation
FACULTY OF ENGINEERING, COMPUTING AND MATHEMATICS CITS2401 Computer Analysis & Visualisation SCHOOL OF COMPUTER SCIENCE AND SOFTWARE ENGINEERING Topic 13 Revision Notes CAV review Topics Covered Sample
The Repast Simulation/Modelling System for Geospatial Simulation. Andrew Crooks. CASA University College London 1-19 Torrington Place London
The Repast Simulation/Modelling System for Geospatial Simulation Andrew Crooks CASA University College London 1-19 Torrington Place London http://www.casa.ucl.ac.uk http://www.gisagents.blogspot.com Introduction
Course Summary Homework
Course Summary Homework (Max useful score: 100 - Available points: 210) 15-382: Collective Intelligence (Spring 2018) OUT: April 21, 2018, at 1:00am DUE: May 1, 2018 at 1pm - Available late days: 0 Instructions
Computer Architecture
Computer Architecture Topics: Machine Organization Machine Cycle Program Execution Machine Language Types of Memory & Access Von Neumann Design 1) Two key ideas 1) The stored program concept 1) instructions
Vasileios Vlachos, Eirini Kalliamvakou and Diomidis Spinellis Athens University of Economics and Business. 11th Panhellenic Conference on Informatics
Simulating Bandwidth-Limited Worms, One Graph to Rule Them All? Vasileios Vlachos, Eirini Kalliamvakou and Diomidis Spinellis Athens University of Economics and Business Rapid Malcode Is rapid malcode
University of Engineering and Technology, Taxila Department of Civil Engineering
University of Engineering and Technology, Taxila Department of Civil Engineering Course Title: Pre-requisite(s): Computer Applications (HU-210) Theory + Lab None Credit Hours: 2 + 2 Contact Hours: 2 +
4 Visualization and. Approximation
4 Visualization and Approximation b A slope field for the differential equation y tan(x + y) tan(x) tan(y). It is not always possible to write down an explicit formula for the solution to a differential
MASTER OF ENGINEERING PROGRAM IN INFORMATION
MASTER OF ENGINEERING PROGRAM IN INFORMATION AND COMMUNICATION TECHNOLOGY FOR EMBEDDED SYSTEMS (INTERNATIONAL PROGRAM) Curriculum Title Master of Engineering in Information and Communication Technology
LEARNING TO PROGRAM WITH MATLAB. Building GUI Tools. Wiley. University of Notre Dame. Craig S. Lent Department of Electrical Engineering
LEARNING TO PROGRAM WITH MATLAB Building GUI Tools Craig S. Lent Department of Electrical Engineering University of Notre Dame Wiley Contents Preface ix I MATLAB Programming 1 1 Getting Started 3 1.1 Running
Founder of NKN & Co-Founder of Onchain. Open source guru, Linux Kernel network subsystem code contributor.
Biography Yanbo Li Founder of NKN & Co-Founder of Onchain Open source guru, Linux Kernel network subsystem code contributor. Founded and led Onchain Beijing office and developed open source blockchain
A General Introduction to Modeling in Research
A General Introduction to Modeling in Research Simon D. Levy Dept. of Computer Science Program in Neuroscience 03 May 2011 I am never content until I have constructed mechanical model of the subject I
M.E.J. Newman: Models of the Small World
A Review Adaptive Informatics Research Centre Helsinki University of Technology November 7, 2007 Vocabulary N number of nodes of the graph l average distance between nodes D diameter of the graph d is
Agent based Cellular Automata Simulation
Agent based Cellular Automata Simulation P. Fornacciari, G. Lombardo, M. Mordonini, A. Poggi and M. Tomaiuolo Dipartimento di Ingegneria e Architettura Università degli Studi di Parma Parma, Italy {paolo.fornacciari,gianfranco.lombardo,monica.mordonini,agostino.poggi,michele.tomaiuolo}@unipr.it
Models and Algorithms for Network Immunization
Models and Algorithms for Network Immunization George Giakkoupis University of Toronto Aristides Gionis, Evimaria Terzi and Panayiotis Tsaparas University of Helsinki Abstract Recently, there has been
Genetic Algorithm Based Template Optimization for a Vision System: Obstacle Detection
ISTET'09 Umair Ali Khan, Alireza Fasih, Kyandoghere Kyamakya, Jean Chamberlain Chedjou Transportation Informatics Group, Alpen Adria University, Klagenfurt, Austria. Genetic Algorithm Based Template Optimization
ESTIMATING PARAMETERS FOR MODIFIED GREENSHIELD S MODEL AT FREEWAY SECTIONS FROM FIELD OBSERVATIONS
0 ESTIMATING PARAMETERS FOR MODIFIED GREENSHIELD S MODEL AT FREEWAY SECTIONS FROM FIELD OBSERVATIONS Omor Sharif University of South Carolina Department of Civil and Environmental Engineering 00 Main Street
Supplementary material to Epidemic spreading on complex networks with community structures
Supplementary material to Epidemic spreading on complex networks with community structures Clara Stegehuis, Remco van der Hofstad, Johan S. H. van Leeuwaarden Supplementary otes Supplementary ote etwork
Automatic Classification of One-Dimensional Cellular Automata
Automatic Classification of One-Dimensional Cellular Automata Rochester Institute of Technology Computer Science Department Master of Science Thesis Daniel R. Kunkle July 17, 2003 Advisor: Roger S. Gaborski
7. Traffic Simulation via Cellular Automata
EX7CellularAutomataForTraffic.nb 1 7. Traffic Simulation via Cellular Automata From Chapter 12 (mainly) of "Computer Simulations with Mathematica", Gaylord & Wellin, Springer 1995 [DCU library ref 510.2855GAY]
Final Report. Discontinuous Galerkin Compressible Euler Equation Solver. May 14, Andrey Andreyev. Adviser: Dr. James Baeder
Final Report Discontinuous Galerkin Compressible Euler Equation Solver May 14, 2013 Andrey Andreyev Adviser: Dr. James Baeder Abstract: In this work a Discontinuous Galerkin Method is developed for compressible
Diffusion and Clustering on Large Graphs
Diffusion and Clustering on Large Graphs Alexander Tsiatas Thesis Proposal / Advancement Exam 8 December 2011 Introduction Graphs are omnipresent in the real world both natural and man-made Examples of
Lecture 8 Object Descriptors
Lecture 8 Object Descriptors Azadeh Fakhrzadeh Centre for Image Analysis Swedish University of Agricultural Sciences Uppsala University 2 Reading instructions Chapter 11.1 11.4 in G-W Azadeh Fakhrzadeh
Netaji Subhash Engineering College, Technocity, Garia, Kolkata , India
Classification of Cellular Automata Rules Based on Their Properties Pabitra Pal Choudhury 1, Sudhakar Sahoo 2, Sarif Hasssan 3, Satrajit Basu 4, Dibyendu Ghosh 5, Debarun Kar 6, Abhishek Ghosh 7, Avijit
Simulation of Optimized Evacuation Processes in Complex Buildings Using Cellular Automata Model
1428 JOURNAL OF SOFTWARE, VOL. 9, NO. 6, JUNE 2014 Simulation of Optimized Evacuation Processes in Complex Buildings Using Cellular Automata Model Rong Xie International School of Software, Wuhan University,
PharmaSUG China. Compartmental Models in SAS: Application to Model Epidemics Ka Chun Chong and Benny Chung-Ying Zee
PharmaSUG China Compartmental Models in SAS: Application to Model Epidemics Ka Chun Chong and Benny Chung-Ying Zee Clinical for Clinical Research and Biostatistics, JC School of Public Health and Primary
1. Mathematical Modelling
1. describe a given problem with some mathematical formalism in order to get a formal and precise description see fundamental properties due to the abstraction allow a systematic treatment and, thus, solution
Traffic flow optimization on roundabouts
Available online at www.sciencedirect.com ScienceDirect Procedia - Social and Behavioral Sciences 111 ( 2014 ) 127 136 EWGT2013 16 th Meeting of the EURO Working Group on Transportation Traffic flow optimization
Homework # 2 Due: October 6. Programming Multiprocessors: Parallelism, Communication, and Synchronization
ECE669: Parallel Computer Architecture Fall 2 Handout #2 Homework # 2 Due: October 6 Programming Multiprocessors: Parallelism, Communication, and Synchronization 1 Introduction When developing multiprocessor
ECE 497 Introduction to Mobile Robotics Spring 09-10
Final Project: Localization and Map Making Lectures: 8-1 and 8-2 Reading: Ch. 11, Introduction to AI Robotics (Demonstrations due in class on Monday and Tuesday of Week 10) (Competition in Kahn room on
Eight units must be completed and passed to be awarded the Diploma.
Diploma of Computing Course Outline Campus Intake CRICOS Course Duration Teaching Methods Assessment Course Structure Units Melbourne Burwood Campus / Jakarta Campus, Indonesia March, June, October 022638B
Creating an Adaptive Network of Hubs Using Schelling s Model
Creating an Adaptive Network of Hubs Using Schelling s Model Atul Singh and Mads Haahr Distributed Systems Group, Department of Computer Science, Trinity College, Dublin, Ireland Email: Atul.Singh@cs.tcd.ie,
Development of a Maxwell Equation Solver for Application to Two Fluid Plasma Models. C. Aberle, A. Hakim, and U. Shumlak
Development of a Maxwell Equation Solver for Application to Two Fluid Plasma Models C. Aberle, A. Hakim, and U. Shumlak Aerospace and Astronautics University of Washington, Seattle American Physical Society
INTERNATIONAL JOURNAL OF CIVIL AND STRUCTURAL ENGINEERING Volume 2, No 3, 2012
INTERNATIONAL JOURNAL OF CIVIL AND STRUCTURAL ENGINEERING Volume 2, No 3, 2012 Copyright 2010 All rights reserved Integrated Publishing services Research article ISSN 0976 4399 Efficiency and performances
Cascading Disaster Spreading and Optimal, Network-Dependent Response Strategies
Cascading Disaster Spreading and Optimal, Network-Dependent Response Strategies Prof. Dr. rer. nat. Dirk Helbing Chair of Sociology, in particular of Modeling and Simulation www.soms.ethz.ch with Lubos