MIS 643 Agent-Based Modeling and Simulation 2016/2017 Fall

1. MIS 643 Agent-Based Modeling and Simulation 2016/2017 Fall

2. Outline Virtual Corridors of Butterflies From ODD to NetLogo Implementation

3. Chapters 4,5 of RG-IABM

4. Virtual Corridors of Butterflies • virtual corridores – Peer et al. (2005) • mate-finding by butterflies (bf) • “hilltopping” strategy - males and females • uphills for meet and mate • Simple model • formulation by ODD protocol

5. Purpose • questions about virtual corridores (VC) • under what conditions • interactions of bfs hilltopping behavior and topograhpy • emergence of virtusl corridores • relatively nerrow paths bfs move • variabiity in bf. strategy affects formation of VCs

6. Entities, State Variables and Scales • Entities: • butterflies • square patches of land – • State Variables: • elevation – land • positions of butterflies on patches – x,y coordinates • Scales: • time and patch not impotant but • time length – time fo fly 25-35 meters • patch size 25 x25 meters • 1000 time steps with a 150x150 square landscape

7. Process Overview and Schedulling • Process – movement of the bfs. • at each time bfs. move one step • order of movements – not impotant • no interraction among bfs.

8. Design Concepts • basic principle - virtual corridores • emergence – how corridores emerge from • 1 - adaptive movement behavior of bfs • 2 – topograhy of landscape • adaptive behavior – moving behavior of butterflies based on an emprical law • objective, learning, prediction – not included

9. Design Concepts (cont.) • sensing – how bfs percive higher elevation • interraction – not included • stocasticity • – each bf. at each step move uphill its neighbors with probability q and • move random to its neighbors with probability 1-q

10. Initilization • topography of the landscape • 1- artificial • 2- real values from a file • 500 bfs set to a patch

11. Input Data • Environment is not chaning • not needed

12. Submodels • movement submodel: • how bfs decide to move • uphill: highest neigboring patch • random: randomly on of the eight naighboring pathces • For each butterfly at each time step • whether move uphill or random is by a contol parameter q • q: global variable from a uiform distribution

13. Submodels (cont.) • at each time step • each bf draws a random variable x • from a uniform distribution between 0.0 and 1.0 if x < q move uphill otherwise move randomly

14. From ODD to NetLogo Implementation • Purpose – information tab model description • Entities, State variables and Scales • tutles-own [ ] • patches-own [ ] • globals [ ]

15. From ODD to NetLogo Implementation • Process and Schedule – go • Design concepts • Initilization - setup • Input data – from file input • Submodels - processes called from go

16. Entities State Variables Scales globals [] turtles-own [] patches-own [elevation] For a 150x150 landscape from settings – corner – buttom left max-pxcor: 149,max-pycor: 149 Square landscape – turn off world wrapper patch size 3 or so

17. Initialization to setup ca ask patches [ ] reset-ticks end Templeate for initialization

18. Initialization – set elevations ask patches [ let elev1 100 - distancexy 30 30 let elev2 50 - distancexy 120 100 ifelse elev1 > elev2 [ set elevation elev1] [ set elevation elev2] set pcolor scale-color green elevation 0 100 ]

19. Initialization – set elevations elev1 elev2 local variables for creating two hills hill1 at 30 30 at a height of 100 hill2 at 120 100 at a height of 50 primitives:distancexy,scale-color set pcolor scale-color green elevation 0 100 Scales color

20. Initialization - turtles crt 1 [ set sıze 2 setxy 85 95 ] create one turtle at 85 95 with a size of 2

21. Process Schedule to go ask turtles [move] tick if ticks >= 1000 [stop] end to move end in go procedure primitives tick, ticks stop

22. Submodels - move to move ifelse random-float 1 < q [uphill elevation] [move-to one-of neighbors] end

23. Submodels - move probability q uphill with 1-q to random neighbor uphill move-to one-of define and initilize q

24. Chapter 5 of IABM • Introduction • Observation of Corridors • Analazing the Model • Time Series Resutls: Adding Plots and File Output • A Real Landscape • Summary and Conclusions

25. 5.1 Introduction • modeling – not formulating and implementing • iterative process – modifying refining model

26. The Problem • Problem: • where and how corridors are formed? • quantitative outputs to be analized • replace artificial landscape with a real topography

27. Learning Objectives • version control • quantitative outputs and simulation experiments • slider or switchs for global variables, reporters • output window • time series plot • exporting to a file • importing data from a file

28. 5.2 Observation of Corridors • How to caracterize a corridore? • if all bfs have the same path: • start from same posstion and q = 1.0 • corridor - very nerrow • if movement – completely random • q = 0.0 • no corridore like feature • How width of paths change as q or topography varies

29. quantifying width • bfs can start and end – different places • Assume: • bfs stop – rich a local hilltop – • a patch higher than all its neighboringpatches • quantify width of the corridor – all bfs • #pathces visited – any bf divided by • mean distance – starting and edning locations – all bfs

30. quantifying width • lower bound 1.0 when all bfs follow a streigth line • increases as bfs diverge • Analysis: • plot q v.s. corridor width

31. First modifications • slider for q • from 0.0 to 1.0 with increments 0.01 • modify setup • create 50 bfs starting from same position • experiment with different q values • Programming Notes: moving variables to the interface • remove from globals • remove initialization in setup procedure • indicate with a comment

32. modifying move • bfs stop when they rich a local hill • a patch with an elevation higher than its neighbors • stop rest of the move procedure • code – start of the move if elevation >= [elevation] of max-one-of neighbors [elevation] [stop] • if condition ; turtle context [stop] move - in turtle context turtles get patch veriable - elevation

33. right side of condition • of and max-one-of commands: • of: [reporter or agent variable] of agent or agentset

34. right side of condition • agent variable: elevation • agent: agent in the neighborhood of the current turtle with maximum elevation max-one-of agentset [reporter or agent variable] • report an agent from the agentset based on the reporters value

35. width of the bf population • a - # of patches visited • b- mean distanc between bfs starting and ending positions • two new state variables • for each patch – keep track of whether a turtle ever visited it or not • for each turtle – store its starting patch

36. width of the bf population • Add a boolean variable to pathces – used? patchs-own [used?] • turn to true is the patch is ever visited

37. width of the bf population • Add a variable to turtles – start-patch turtles-own [start-patch] • set to the start patch when inilizing bfs

38. initilize in setup ask patches [ ... set used? false ] create-turtles [ ... set start-patch patch-here ] • patch-here: reports the patch the turtle is currently on • Programming note: initializing variables • all variables has an initial value of 0

39. move and go procedure • When a bf moves to a patch • set the patch variable to true • at the end of move • in the go procedure before the program stops let final-corridor-width corridor-width • a laocal variable is assigned the value of the corridor width computed by another procedure (reporter) to-report corridor-width • calculate and report corridor width • print the value of final-corridor-width to an output

40. go procedure to go ask turtles [move] tick if ticks >= 1000 [ let final-corridor-width corridor-width output-print word "corridor width " final-corridor-width stop ] end

41. corridor-width reporter to-report corridor-width let patches-visited count patches with [used?] let mean-distance mean [distance start-patch] of turtles report patches-visited / mean-distance end

42. move to move if elevation >= [elevation] of max-one-of neighbors [elevation] [stop] ifelse random-float 1 < q [uphill elevation] [move-to one-of neighbors] set used? true end

43. 5.3 Analazing the Model • How corridor width output is affected from q • plot corridor width v.s. q • as q increases – corridor width falls as expected • but when q=1.0 corridor widthis <1.0 • How can this be?

44. 5.4 Time Series Resutls: Adding Plots and File Output to go ask turtles [move] plot corridor-width if ticks >= 1000 [ ... stop] end add a ploter to tthe interface add an output to the interface

45. add a ploter to tthe interface give plot name “corridor width” export-plot “corridor width” word “corridor-output-for-q ” q

46. exporting plots to a file • write the results of plots to a file • add the command to the end of go before the program stops export-plot “corridor width” word “corridor-output-for-q” q export-plot ploter_name filr_name

47. write coridor width to output • print the final coridor width to an output widow in the interface • create and add an output to the interface • add the command to the end of go before the program stops output-print word "corridor width " final-corridor-width

48. before program stops if ticks >= 1000 [ let final-corridor-width corridor-width output-print word "corridor width " final-corridor-width export-plot "corridor width" word "corridor-output-for-q" q stop ]

49. go procedure to go ask turtles [move] plot corridor-width tick if ticks >= 1000 [ let final-corridor-width corridor-width output-print word "corridor width " final-corridor-width export-plot "corridor width" word "corridor-output-for-q" q stop ] end

50. 5.5 A Real Landscape • real data from “ElevationData.txt” • from books web side • Programming Note: • grid-based: x-coordinate, y-coordinate and a value • one data line for each grid point