MIS 643 Agent-Based Modeling Chapter 5 The Components of Agent-Based Modeling

1. MIS 643 Agent-Based Modeling Chapter 5 The Components of Agent-Based Modeling

2. Outline Overview Agents Environments Interactions Observer/User Interface Schedule Wrapping It All Up Summary

3. The Components of Agent-Based Modeling • Individual components of ABMs • Overview of components of ABM • Discuss each components • How components come together for the construction of ABMs

4. Outline Overview Agents Environments Interactions Observer/User Interface Schedule Wrapping It All Up Summary

5. Overview • Complex systems – described • agents, environment • behavior - agent rules • Basic components of ABMs • agents – basic ontological units • environment – world in which agents live • interactions – between agents and agents and environments

6. Overview • agent actions – internanlly • directly affacting agents internal state • E.g.: in Segregation – agents move • The environment may not be passive • E.g.: grass regrows • Two more:

7. Overview • Observer/User Interface: an agent but can access all agents and environment • ask agents to carry out specific tasks • users interact with ageents with UI • Schedule: the observer tells agents when to do

8. Trafic Basic Model • Trafic basic model –a simple model of trafic flow • in Social Science section • to explore how trafic jams form • they thought to include • trafic accidents, radar traps • but without such hindrances • trafic jams form as • cars approach to cars in front of them • they slow down, • cars behind slow down and so on... • a ripple effect backwords – trafic jam moves backward • emergence

9. Outline Overview Agents Environments Interactions Observer/User Interface Schedule Wrapping It All Up Summary

10. Agents • Properties • Behavior (Actions) • Collections of Agents • The Granularity of an Agent • Agents Cognition • Other Kind of Agents

11. Agents • basic building blocks of ABM • properties – they have • internal and external states • actions – behavior • what they do • Several issues: • agents grin-size – most effective • e.g.: political model: actors, institutions • agent cognition- capability to observe and decision about world • proto agnets and meta-agents

12. Properties • describe agent’s current state • individual agents - inspected by monitors: • patch, turtle, link • Figure 5.2 • Two sets of properties • Standard turtle properties • WHO, COLOR, HEADING, XCOR, YCOR, SHAPE, LABEL, LABEL-COLOR, BREED, HIDEN?, SIZE, PEN-SIZE, PEN-MODE

13. Properties (cont.) • Standard patch properties: • PXCOR, PYCOR, PLABEL, PLABEL-COLOR • Standard link properties: • END1, END2, COLOR, LABEL, LABEL-COLOR, HIDEN?, BREED, THICKNESS, SHAPE, THE-MODE • turtles can directly access patch properties they are currently on • sit on exactly one patch at a time

14. accessing turtles from patches: • turtles-here: set of turtles on the patch • turtles-on: set of turtles on a specified patch

15. user defined prrperties • author defined properties: • E.g.: for Simple Trafic model: • SPEED, SPEED-LIMIT, SPEED-MIN • should be described in info tab • SPEED: curent speed of the car • SPEED-LIMIT: maximum speed of the car • SPEED-MIN: its minimum speed

16. set in the SETUP-CARS procedure at the srart of the model set speed .1 + random-float .9 set speed-limit 1 set speed-min 0

17. uniformly distributed random variable random-float value • 0 <= random number < value • normally distributed random variables random-normal mean standard-dev • E.g.: random-normal .5 .1 • mean and starndard deviation of a normal distribution

18. Initialize agent properties from • lists or files • emprical data • Example: from file: • Higths of patches in the Butterfly model are red from a file • Example: from lists: • colors of people are assigned from red or blue set color one-of [red blue]

19. agent properties can be changed during the running of the model • E.g.: in Trafic Basic model • speed propertiy can be increased or decreased in the SPEED-UP-CAR procedure set speed speed + acceleration

20. Behaviors (Actions) • how the agents can behave – actions it can take • the agent can change the state of the • environment, other agents’ or it self • List of all predefined behavior • NetLogo dictionary: • turtles, patches, links FORWARD/BACKWARD RIGHT/LEFT HATCH/DIE

21. Behaviors (Actions) • author defined behavior • E.g.: in TBM, SPEED-UP-CAR, SLOW-DOWN-CAR to slow-down-car ;; turtle procedure set speed speed - deceleration end to speed-up-car ;; turtle procedure set speed speed + acceleration end

22. initialization globals [ sample-car ] turtles-own [ speed speed-limit speed-min ] to setup clear-all ask patches [ setup-road ] setup-cars watch sample-car reset-ticks end to setup-road ;; patch procedure if (pycor < 2) and (pycor > -2) [ set pcolor white ] end

23. setup-cars to setup-cars if number-of-cars > world-width [ user-message (word "There are too many cars for the amount of road. Please decrease the NUMBER-OF-CARS slider to below " (world-width + 1) " and press the SETUP button again. The setup has stopped.") stop ] set-default-shape turtles "car" crt number-of-cars [ set color blue set xcor random-xcor set heading 90 ;;; set initial speed to be in range 0.1 to 1.0 set speed 0.1 + random-float .9 set speed-limit 1 set speed-min 0 separate-cars ] set sample-car one-of turtles ask sample-car [ set color red ] end

24. seperate-cars • this procedure is needed so when we click "Setup" we • don't end up with any two cars on the same patch to separate-cars ;; turtle procedure if any? other turtles-here [ fd 1 separate-cars ] end

25. go • adjust their speed according to speed limits to go ;; if there is a car right ahead of you, match its speed then slow down ask turtles [ let car-ahead one-of turtles-on patch-ahead 1 ifelse car-ahead != nobody [ set speed [speed] of car-ahead slow-down-car ] ;; otherwise, speed up [ speed-up-car ] ;;; don't slow down below speed minimum or speed up beyond speed limit if speed < speed-min [ set speed speed-min ] if speed > speed-limit [ set speed speed-limit ] fd speed ] tick end

26. slow-down-car/speed-up-car to slow-down-car ;; turtle procedure set speed speed - deceleration end to speed-up-car ;; turtle procedure set speed speed + acceleration end

27. agents - cars can change their speed and indirectly affect the speed of other cars • can chnge environment properties • E.g.: speed of a car makes the road – the patches worm-up by a precedure WEAR • E.g.: sheep eat grass , change the amount of grass in a place

28. Collections of Agents • Three types • mobile agents – turtles in NetLogo • stationary agents – ptches in NetLogo • connecting agents: links in NetLogo

29. Collections of Agents • mobile agents – turtles in NetLogo • shapeless; arealess points • even shape and size, • when created: • x,y coordinats - at center • headings - random • coordinates : xcor,ycor

30. Collections of Agents • stationary agents – ptches in NetLogo • acted upon by turtles; pasive environments • take actions and perform operations • defined space/area

31. Collections of Agents • connecting agents: links in NetLogo • link two or more agents • relation between turtles • friendship, communiciation, • envirnonments: roads

32. Breeds of Agents • particular set of agents with their own preperties and actions • breed of an agent: class or category to which the agent belongs • if different agents have different properties or actions • E.g.: sheep and wolves has the same properties but actions are different tutles-own [energy] sheep-own [wooliness] volves-own [fang-strength] sheep – energy and wooliness volves - energy and fang-strength

33. Sets of Agents • NetLogo – agentset: unordered collection of agents • collecting agents something in common • or randomly selecting subset of another agentset let fast-cars turtles with [speed > 0.5] ask fast-cars [set size 2.0] • or without let ask turtles with [speed > 0.5] [set size 2.0]

34. all turn to size 2 • slow turtels sizes 1 ask turtles with [speed > 0.5] [set size 2.0] ask turtles with [speed <= 0.5] [set size 1.0] • another way of doing ask turtles [ ifelse [speed > 0.5] [set size 2.0] [set size 1.0] ] :: end ask

35. collection of agents based on location let car-ahead one-of turtles-on patch-ahead 1 ifelse car-ahead != nobody [ set speed [speed] of car-ahead slow-down-car ] • TURTLES-ON PATCH-AHEAD 1 • turtles on the patch 1 unit ahead of a turtles • accessing collections agents based on their locations • TURTLES-HERE, TURTLES-AT, NEIGHBORS, IN-RADIUS

36. choosing agents randomly • choosing agents randomly • N-OF, ONE-OF ;; create turtles on random patches ask n-of number patches [ sprout 1 [set color one-of [red green]] ] ;; end ask

37. Agentsets and Lists • creating an empty list set a-list [] • add items to list set a-list fput 1 a-list set a-list fput “and” a-list set a-list fput turtle 0 a-list show a-list [(turtle 0) “and” 1]

38. an agentsets hold same type of agents • empty agentsets • no-turtles, no-patches, no-links • creates an empty agentset of turtles set an-agentset no-trutles set an-agentset (turtle-set turtle 0) set an-agentset (turtle-set an-agentset turtle 1 turtle 2) :: an-agentset has three turtles

39. agentsets can be asked to do something • lists cannot be asked • agentsets are unordered • when printing they are printed randomly turtles-own [information] let a-list [] set a-list sort-on [information] turtles • a-list: list of turtles in ascending order by information

40. foreach a-list [ ask ? [] ;; do something ] • ? as a special variable takes on each value of the list elements

41. let n 0 foreach sort patches [ ask ? [ set plabel n set n n + 1 ] ] ;; patches are labeled with numbers in left-to-right, ;; top-to-bottom order

42. Agentsets and Computation • when an agentset is asked to perform an action, agents collected at that moment are asked to do • agent A, agent B, are in the collection • agent C not in the collection • if agent A action causes agent B not satisfy collection criteria but agent B is also asked • if agent A action causes agent C satisfy collection criteria but agent C is not asked • E.g.: Agentset Ordering Model (AOM) in IABM Ch 05

43. setup ;; create 100 blue turtles of size between 0.0 and 2.0 to setup clear-all create-turtles 100 [ set size random-float 2.0 forward 10 set color blue ] reset-ticks end

44. go to go ;; ask all turtles with size < 1 to ask a larger turtle to decrease its size, and then turn themselves red ask turtles with [ size < 1.0 ] [ ask one-of turtles with [size > 1.0][ set size size - 0.5 ] set color red ] print count turtles with [color = red] print count turtles with [size < 1.0] end

45. Computational Efficiency • Sometimes it is better to form an agentset before performing operations on it • E.g.: Agent Efficiency Model (AEM) in IABM Ch 05 • trade off between efficiency and code readability

46. setup ;; SETUP colors the patches so that roughly half are red and half are green to setup clear-all ask patches [ set pcolor one-of [red green] ] reset-ticks end

47. go-1 ;; GO-1 sets the labels of red patches to a small random number (0-4) ;; and the labels of green patches to a larger random number (5-9) to go-1 if any? patches with [pcolor = red] [ ask patches with [ pcolor = red ] [ set plabel random 5 ] ] if any? patches with [pcolor = green] [ ask patches with [ pcolor = green ] [ set plabel 5 + random 5 ] ] tick end

48. go-2 ;; GO-2 has the same behavior as GO-1 above, but it is more ;; efficient as it computes each of the agentsets only once. to go-2 let red-patches patches with [ pcolor = red ] let green-patches patches with [ pcolor = green ] if any? red-patches [ ask red-patches [ set plabel random 5 ] ] if any? green-pathces [ ask green-patches [ set plabel 5 + random 5 ] ] tick end

49. go-3 ;; GO-3 explores what happens if patch colors are changed on the fly. ;; GO-3 results in the entire world becoming red to go-3 if any? patches with [pcolor = red] [ ask patches with [ pcolor = red ] [ set pcolor green ] ] if any? patches [with pcolor = green] [ ask patches with [ pcolor = green ] [ set pcolor red ] ] tick end

50. go-4 ;; GO-4 explores what happens if you first keep track ;; of which patches are red and which are green. ;; GO-4 results in the patches swapping their colors. to go-4 let red-patches patches with [ pcolor = red ] let green-patches patches with [ pcolor = green ] if any? red-patches [ ask red-patches [ set pcolor green ] ] if any? green-patches [ ask green-patches [ set pcolor red ] ] tick end