;;this highlights the user who is doing the searching
  globals [user knowledge-from-friends total-knowledge total-SNdiversity combinedNetworkSize
  number-SNresponses number-frndResponses totalReplies got-answer 
  easy-answered easy-unanswered hard-answered hard-unanswered totalTrials] 

  ;;two breeds of "turtles" for people and online socialNetworks
  breed [ socialNetworks ]            ;some entities are social networks
  breed [ friends ]                   ;some entities are friends (whole individuals)
  turtles-own [ knowledge diversity question ] ;friends & user has knoweldge; SNs have diversity
                                               ;user has knowledge & a question
                              

;;PROCEDURES FOR INITIAL SETUP;;
to setup-onlineSpace                  ;segments a space (where online entities live)
   if (pxcor <= -2) 
     [ set pcolor gray + 4.1 ]  ;87 light blue for online world
end ;setup-onlineSpace

to setup-socialNetworks
  set-default-shape socialNetworks "orbit 5";"social-network";"orbit 5"
  create-socialNetworks random maxSocialNetworks + 1        ;num initial SNs is random 0-2
    [ set color 103  ;103 dark blue
      set size 5
      set diversity random 10                          ;SNs have random diversity (0-9)
      setxy -6 random 9 ]                              ;set to appear in onlineSpace [ random 10+1 ]
    
   ask socialNetworks [
     ifelse show-diversity?  
     [ set label diversity         
       set label-color black ] [ set label "" ]
   ]                    
end ;setup-socialNetworks

to setup-friends
   set-default-shape friends "person modified"
   let nFriends random maxFriends + 1        ;num initial friends is random 1 - (maxFriends), default = 50
   create-friends nFriends                 
    [ set color green
      ;set size 1
      set knowledge random 10               ;friends have random dist of knowledge (0-9)
      ;set knowledge random-normal 5 1.5    ;friends have normal dist of knowledge (mean=5, std=1.5)
      ;setxy random 10 + 1 random -10 ]           ;friends appear outside of onlineSpace
      setxy random 10  random -10 ]           ;friends appear outside of onlineSpace
 
      set user one-of friends 
        ask user 
        [ set color red                               ;this defines our central user, in red
          setxy 5 6
          
          set question random 2 ]         ;question will be set to 0 = easy; or 1 = hard.          
   ask friends [
     ifelse show-knowledge? [ set label round knowledge ] [ set label "" ] 
   ]
   
   ask user [ set label question set label-color pink]
end ;setup-friends

;;PROCEDURES FOR INFORMATION SEEKING;;
to setup
  clear-all
  ask patches [ setup-onlineSpace ]       
  reset  
end ;setup (initial)

to reset                  ;reset sets or resets the socialNetwork & friend configurations
  clear-turtles
  clear-output
  setup-socialNetworks
  setup-friends
  watch user       
end ;reset

to runMultipleVersions    ;when you hit "Go", it will run the question-answer part for
;  let totalTrials 0

  repeat run-this-many-times [             ;this many iterations: grabs variable run-this-many-times
  reset 
  get-replies
  tick
  set totalTrials (totalTrials + 1)
  do-plots
  does-question-get-answered
  
  tally-answer-outcome
  plot-histogram
  ]
end ;runMultipleVersions

to run-nTimes                   ;hitting Go runs mult. iterations with new SN configs, and plots it
  runMultipleVersions
end ;go

to get-replies            ;this function calculates the number of replies a question receives
   calculateNetworksSize
   calculateFriendsReplies
   countReplies
end ;get-answer

;;This calculate social network size based on "diversity" in the social networks.
to calculateNetworksSize      
    set total-SNdiversity 0
    set combinedNetworkSize 0
    
    ask socialNetworks [                               
      set total-SNdiversity (total-SNdiversity + diversity) ;this totals all social network's diversity #s
    ]
    
    ifelse count socialNetworks >= 2        ;if the user has 2 social networks, give greater weighting to "diversity" of network (based purely on presumed greater access to people)
    [ set combinedNetworkSize (total-SNdiversity * 150) ] 
    [ set combinedNetworkSize (total-SNdiversity * 50) ]   ;this creates a fake social network size based on diversity with it
end ;calculateNetworkSize

to calculateFriendsReplies
  set number-frndResponses 0 
  
  ask friends [
    if random 1 < 0.85 and knowledge = 9               ;friends with knowledge 9 give CORRECT reply 85% of time
    [ set number-frndResponses ( number-frndResponses + 1 ) ]
  
     if random 1 < 0.7 and knowledge = 8              ;friends with knowledge 8 give CORRECT reply 70% of time 
    [ set number-frndResponses ( number-frndResponses + 1 ) ] ]
end ;calculateFriendsReplies


;;This computes number of replies a question might receive given size & diversity
;;of social networks and of available friends.
to countReplies                                     ;#replies from a SN (given certain size)
  set number-SNresponses 0
  set totalReplies 0
   
  if combinedNetworkSize >= 150   [ set number-SNresponses 1 ] 
  if combinedNetworkSize >= 425   [ set number-SNresponses 2 ]    
  if combinedNetworkSize >= 648   [ set number-SNresponses 3 ]    
  if combinedNetworkSize >= 790   [ set number-SNresponses 4 ]    
  if combinedNetworkSize >= 2300  [ set number-SNresponses 5 ]    ;max SNsize is 2700
  if combinedNetworkSize >= 4890  [ set number-SNresponses 12 ]
  if combinedNetworkSize >= 7389  [ set number-SNresponses 19 ] 
  if combinedNetworkSize >= 8888  [ set number-SNresponses 23 ]  
  if combinedNetworkSize >= 10000 [ set number-SNresponses 27 ]

  set totalReplies ( number-SNresponses + number-frndResponses )      ;tallies num SN & friend replies
end  ;countReplies

to does-question-get-answered
  set got-answer 1

  ask user
  [ ifelse question = 0          ;for easy questions (0),
    [ if totalReplies >= 2       ;if there are more than 2 totalReplies,
      [ set got-answer 2 ] ]     ;then set got-answer to YES!
    
    
    [ ifelse knowledge > 7       ;for hard questions (if not easy), if user knowledge = 8 or 9
       [ if totalReplies >= 3    ;if there are more than 3 totalReplies
         [ set got-answer 2 ] ]  ;then set got-answer to YES!
       
       [ if totalReplies >= 5    ;else for hard questions, with low user knowledge
         [ set got-answer 2 ] ] ] ] ;if there are more than 5 totalReplies, got-answer = YES
end ;does-question-get-answered

to tally-answer-outcome
 ask user [
  if question = 0 [
    ifelse got-answer = 2
      [ set easy-answered (easy-answered + 1) ]
      [ set easy-unanswered (easy-unanswered + 1) ] ] ]

 ask user [   
  if question = 1 [
    ifelse got-answer = 2
     [ set hard-answered (hard-answered + 1) ]  
     [ set hard-unanswered (hard-unanswered + 1) ] ] ]
end ;tally-answer-outcome

to do-plots
  set-current-plot "#Replies from friends"
  set-current-plot-pen "totalReplies"
  plot totalReplies
end ;do-plots

to plot-histogram
  set-current-plot "Is the question answered?"
  set-plot-pen-color green
  plotxy 1 easy-answered
  plotxy 4 hard-answered
  
  set-plot-pen-color red
  plotxy 2 easy-unanswered
  plotxy 5 hard-unanswered
  
  set-plot-pen-color white         ;this is because there is an artifact of a pen trace (with color) without it
  plotxy 3 1
end ;plot-histogram
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@#$#@#$#@
WHAT IS IT?
-----------
This is a question-answering model. It tries to simulate the scenario where a user poses a question to his social network. Will the friends and online social networks available be able to answer his question? In the model, this will depend on who the user has access to face-to-face, how knowledgeable they are about your topic, the size and diversity of online social networks, how difficult the topic is to answer, and how knowledgeable the user is about the topic. In practice, there are many other factors that would also matter, such as the user's relationship with people in the network and the last time they interacted, which are not modeled here.


HOW IT WORKS
------------
When you hit setup, you get a random scenario. Each scenario has a user (circled) with either a "hard" or "easy" question. Easy questions are well-formed, fairly straight forward queries such as: What is the local DMV's phone number? Hard questions are ill-formed, open-ended queries that tend to require a good bit more reformulation and iteration to solve, such as: What are the latest regulations in enlisting army personnel with GED credentials?

A user has two types of peers in his world: friends who he can talk to face-to-face (shown in the black part of the world) and digital friends who exist in online social networks (shown in the yellow part of the world). Friends in these different networks are treated slightly differently in the model. 

Real-life friends have an attribute of knowledge that is supposed to be related to their expertise on the user's topic. When they have high knowledge, the provide a correct answer to the user 85% of the time (when knowledge = 9) or 70% of the time (when knowledge = 8). By only providing an answer part of the time, the model controls for incorrect responses. This does no take into consideration friends' availability, interest in the topic, relationship with the user, etc.

Social networks have an attribute of diversity, but the number of social networks available to the user is also important. When 2 or more social networks exist, the diversity scores are converted into large (projected) social network sizes. When fewer than 2 social networks exist, diversity scores are converted into slightly smaller social network sizes. Ultimately, the number of people projected to be in these combined social networks will determine how many replies the user receives.

Replies from all individuals and networks are tallied, and the sum total is used to determine if the question actually gets answered. The two types of questions require different numbers of replies to be answered with confidence. Easy questions need two corroborating replies to be answered. (We assume that all replies are on-target.) Hard questions need between 3 and 5 replies depending on the user's expertise on the topic. The user has an attribute of knowledge too (not depicted visually in the model); when the user is knowledgeable about the topic of his question  (knowledge >= 8), only 3 replies are needed to answer it. Otherwise, 5 replies are needed.

On each trial a different random scenario is created and the social networks are polled to provide an responses. For each question in a trial, the ultimate outcome is whether the question was, indeed, answered. This is plotted in the histogram: the green bar represents answered questions, the red bar unanswered questions. Since the point of the model is to see which network configurations are best suited for question-answering, many trials are run to see how different networks respond. The relevant variables are saved to text file which can be analyzed offline. (This text file is not saved when the model is embedded on a website).


HOW TO USE IT
-------------
To view random different configurations of the model, hit setup. Setup only generates a new random social network layout.

To see if a question gets answered, hit the run-n-times button. The slider control box under the run button specifies how many trials you want to run. By default this is set to 100 to help reveal patterns across multiple trials (max = 250).


THINGS TO NOTICE
----------------
Hard and easy questions are posed with equal probability, approximately alternating between trials. Easy questions tend to get answered more often than not.

There are 2 plots: on shows the number of total replies received per trial; the other shows whether the question was subsequently answered.

Real-life friends tend to supply the most answers in this model. By increasing the max number of friends, the number of answered questions should also go up.


EXTENDING THE MODEL
-------------------
This is just a model. Question-answering and social information seeking is much more complicated in practice. Therefore a natural extension of this model would be to include some additional factors: relationship between the user and his real-life friends; last time they interacted; friends' availability; etc. Right now, average number of replies from friends is 5.2. This seems unusually high, especially when compared to the average number of replies from social networks (2.2). I would expect that larger social networks would (more reliably) produce replies than individual face-to-face contacts alone. This may be an area to work on within the model.


CREDITS AND REFERENCES
----------------------
Brynn M. Evans
March 12, 2009
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