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
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 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: uphill: highest neigboring patch
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 elev distancexy 30 30
let elev distancexy
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 at a height of 100
hill2 at 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 with a size of 2

21. Process Schedule to go ask turtles [move] tick
if ticks >= 1000 [stop]
end
to move
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
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 modify setup
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
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

51. add to setup file-open “ElevationData.txt” while [not file-at-end?] [
let next-x file-read
let next-y file-read
let next-elevation file-read
ask patch next-x next-y [set elevation next-elevation] ]
file-close

52. next do determine dimensions of the world examining the data file
adjust the scale of the color for the new max and min values of the elevations
initial positions of bfs in a 10x10 area
randomly asign xcor and ycor of bfs
setxy (80 + radnom 10) (90 + radnom 10)

53. scaling color let min-elevation min [elevation] of patches
let max-elevation max [elevation] of patches
ask patches [
set pcolor scale-color green elevation min-elevation max-elevation
set used? false ]

54. 5.6 Summary and Conclusions
NetLogo for agent-based science
Modeling a system of multiple agents
quantitative variables
analyzing ouputs
simulation experiments
real spatial data

55. 5.6 Summary and Conclusions (cont.)
butterfly model – simple but...
other environments
movement of ideas people
social networks
economic or political lanscapes