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MIS 585 Special Topics in MIS: Agent-Based Modeling 2015/2016 Fall Chapter 6 Analysing ABMs

MIS 585 Special Topics in MIS: Agent-Based Modeling 2015/2016 Fall Chapter 6 Analysing ABMs. Outline. Types of Measurements Modeling the Spread of Disease Summarizing Analysis of ABMs. Types of Measurments. learn to employ ABMs to produce new and interesting results

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MIS 585 Special Topics in MIS: Agent-Based Modeling 2015/2016 Fall Chapter 6 Analysing ABMs

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  1. MIS 585 Special Topics in MIS: Agent-Based Modeling 2015/2016 Fall Chapter 6 Analysing ABMs

  2. Outline Types of Measurements Modeling the Spread of Disease Summarizing Analysis of ABMs

  3. Types of Measurments • learn to employ ABMs to produce new and interesting results • Different ways of examining and analyzing ABM data • advantages and disadvantages of varieity of tools and techniques • useful to conside analysis methods before building ABM to design output

  4. Outline Types of Measurements Modeling the Spread of Disease Summarizing Analysis of ABMs

  5. Modeling the Spread of Disease • Statistical Analysis of ABM: Moving beyond Raw Data • The Necessity of Multiple Runs within ABM • Using Graphs to Examine Results in ABM • Analyzing Networks within ABM • Environmental Data and ABM

  6. Modeling the Spread of Disease • A simple model • if someone catches a cold he might • infect others • say constant number 5 • sperad virus to 5 others • total infected: 6 • Each 5 spread other 5 so • total infected: 31 • Each 25 other 5 • toatl infected: 156 • exponential groth • assume no overlap

  7. A More Realistic Model • agent • keep track of who is infeced • moving in space randomly, see many people in the neighborhood • infect people who contact with them • Examine: • number of population v.s. spreding time to all population • population v.s. time to 100% infected population

  8. A More Realistic Model • Run the model for population sizes • 50, 100, 150, 200 – keeping size of world same • as the population density increases • time to full infection dramatically decreases • one infeced infects mor people if neighbor is crowded (dence) • the last few people remaining is more kikely to be infected

  9. A More Realistic Model • Runing model several times produces different data • Most ABMs – randomness • the code includes random number generators • E.g.: in SoDM • movemnts of agents at each time step • Table 6.3 ten runs with the same parameter set

  10. Statistical Analysis of ABM: Moving • Data generated from • natural experiments, • computer programs • Descriptive statistics • E.g.: hypothesis a coin is fair • H0 p=0.5 • HA P0.5 • test based on obsered probablity and standard deviation

  11. in SoDM summary statistics in Table 6.4 population menn std.dev. 50 100 150 200 as population density increases mean time decreases std. dev. of time also decreases

  12. Develop hypothesis about hwo inputs affects outputs • ABMs produces large amounts of data • NetLogo Mathematica • NetLogo R extension

  13. The Necessity of Multiple Runs within ABM • to collect statistical data • run the model multiple times with the same parameter set • in NetLogo BehaviorSpace • without it repeat 10 [ set num-people 50 setup while [count trutles with [not infected?] > 0] [go] ;; without forever print ticks ]

  14. batch experiment tools • automatically run the model multiple times with different setings and collect the resulting data • BehaviorSapce • select a new experiment “population density” – name of the output file • Select parameters and parameter ranges

  15. [“num-people” 50] • sets NUM-PEOPLE to 50 [“num-people” [50 50 200]] • sets NUM-PEOPLE from 50 to 200 incremented by 50 • onther parameter at the same time • NUM-INFECTED [“num-infected” [1 1 5]] • all combinations of the two parameters

  16. 10 repitations • setup, go, stop and final commands • time limit • after OK • select format

  17. Using Graphs to Examine Results in ABM • take data from cvs • population density vs. time to 100% infection • all replications • or for each parameter value • plot mean and error bars 1 std up and below

  18. Time series graphes • values of variables vs. time (ticks) • in ABM most dat temporal • number-of-people-infected vs. time • figure 6.6 • three phases • first phase: slowly increase – few infected • second phase: increases sharply – nany infeced infects otghers • third phase: indectionrate slows again – few people remains uninfected

  19. multiple graphs in the same figure for different vaues of parameters • or • mean – X*std, mean, mean+X*std • vs. time graphs

  20. Analyzing Networks within ABM • many interactions in social networks • E.g.: SoDM • chooser – “network” • Erdos Renyi random graph • parameter – connections per node; average degree of the network • how many connections an individual with other individuals on the average • connection per node : 4

  21. Experiments • number of people infeced may not be 200 • not all people are infected • experiment with paramter • connection per node • critical point is around 1.0 • set time limit to 50 • ANC: from 0.5 to 4.0 at incremets of 0.5

  22. a general characteristics of RN • as average degree exceeds 1.0 a gient component forms • In clasical epidemics • ratio of rate of infection to rate of recovery • > 1.0 • population is infeced or • the desies dies out

  23. Other Parameters • average path length (APL) • as APL  spread slower • as APL   spread faster • Cllustering coefficient • how tightly the network is clustered • how nany nodes an egent has links are also links with themselfs • ABM and SNA used together

  24. Environmental Data and ABM • in spread of a cold • agent to environment or • environment to agent • interactions • in SoDM – environmental interaction effect • set variant to “environmental” • parameter DESEAS-DECAY

  25. Experiments • hold num-population at 200 • vary disease-decay from 0 (original) to 10 • Table 6.7 • as disease-decay   time to 100% infection  • both num-population and desease-decay varied • two dimensional graphs Figure 6.9

  26. patterns • policy measures • quarantine an area of the world? • prevents a disease spreading • or track each individual and attapt to cure? • if starts from a certain area • put a ring aroud the area snd gradually srink the ring

  27. patterns • qualitative and quantitaive patterns • lanscape metrics – edge density • of yellow trails • ratio of the number of edges between two states of the environment • infected and uninfeced patches • edge density low – few shared edges • similar patches are highly clustered

  28. Outline Types of Measurements Modeling the Spread of Disease Summarizing Analysis of ABMs

  29. Summarizing Analysis of ABMs • Analysing ABMs chalenges • number of inputs and outputs high • outputs at aggregate or individual levels • ability to control model but combinatorial explosion • parameters their values • agent environment global • outputs – end values, dynamics over time • START SIMPLE MODELS

  30. Chalenges • vast number of parameters • validation – real data or experiment with • is the model robust to changes to these parameters • vast number of outputs • lost in data • difficulty of finding patterns • relations between inputs and outputs

  31. Formats of Data • Four formats of data • statistical • graphical • network • spatial • Inputs can be specified based on these formats

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