napsternxg_netlogo-models/The Number Game.nlogo
breed [people person] ; create people

globals [
  all-words
  total-words
  mean-words
  std-words
  current-word
  num-success
  num-failure
  selected-sender
  selected-reciever
  neighbors-in-radius
]

people-own [
  words
  num-words
  interactions
  role
]

to setup
  clear-all
  initialize-people
  initialize-system
  reset-ticks
  ;; create-network
end

to initialize-system
  set selected-sender -1
  set selected-reciever -1
  set current-word ""
  set num-success 0
  set num-failure 0
  update-globals
end

to update-globals
  set all-words remove-duplicates [words] of people
  set total-words length all-words
  set mean-words mean [length words] of people
  set std-words standard-deviation [length words] of people
end

to initialize-people
  create-people num-agents [
    setxy random(world-width) random(world-height)
    set color black
    set shape "person"
    set size 1
    set interactions 0
    set words []
    set role "none"
    set num-words length words
  ]
end

to go-once
  sample-sender-reciever
  send-message
  update-globals
  tick
end


to sample-sender-reciever
  ; unset last sender and reciever
  if selected-sender != -1 [
    ask person selected-sender [
      set role "none"
      set color grey
    ]
  ]
  if selected-reciever != -1 [
    ask person selected-reciever [
      set role "none"
      set color grey
    ]
  ]
  ; select new sender and reciever
  set selected-sender random(num-agents)
  ifelse sample-neighbor [
    if is-agentset? neighbors-in-radius [
      ask neighbors-in-radius [
        if who != selected-reciever [set color grey]
      ]
    ]
    ask person selected-sender [
      set neighbors-in-radius people in-radius neighbor-radius
      ask neighbors-in-radius [
        set color green
      ]
      ask (one-of neighbors-in-radius) [
        set selected-reciever who
      ]
    ]
  ] [
    set selected-reciever random(num-agents)
    while [selected-reciever = selected-sender] [
      set selected-reciever random(num-agents)
    ]
  ]
  ; set new sender and reciever
  ask person selected-sender [
    set role "sender"
    set color red
    set interactions interactions + 1
  ]
  ask person selected-reciever [
    set role "reciever"
    set color blue
    set interactions interactions + 1
  ]
end


to-report sample-new-word
  let word-length random(max-word-length - 1) + 1
  let alphabet "abcdefghijklmnopqrstuvwxyz"
  let new-word reduce word n-values word-length [item random(length alphabet) alphabet]
  report new-word
end


to send-message
  let sender-word ""
  ask person selected-sender [
    ifelse length words < 1 [
      ;; sample new word as inventory is empty
      set sender-word sample-new-word
      set words lput sender-word words
    ] [
      ifelse shortest-word [
        let sorted-words sort-by [ [string1 string2] -> length string1 < length string2 ] words
        set sender-word first sorted-words
      ][
        set sender-word one-of words
      ]
    ]
  ]
  set current-word sender-word
  ask person selected-reciever [
    ifelse length words < 1 [
      ;; add word to reciever inventory
      set words lput sender-word words
      set num-success num-success + 1
    ] [
      ifelse member? sender-word words [
        ;; word agreement
        set words (list sender-word)
        set num-success num-success + 1
      ] [
        ;; failure
        set words lput sender-word words
        set num-failure num-failure + 1
      ]
    ]

  ]
end
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setup
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People
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Total Unique Words
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Words
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"value-1" 1.0 0 -2674135 true "" "plot 1"
"active-people" 1.0 0 -14454117 true "" "plot count (people with [interactions > 0])"

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"mean-minus-std" 1.0 0 -1604481 true "" "plot mean-words - std-words"
"max-num-words" 1.0 0 -14070903 true "" "plot max [length words] of people"
"min-num-words" 1.0 0 -8275240 true "" "plot min [length words] of people"

BUTTON
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go
;; go-once\nifelse any? people with [length words != 1] [go-once][stop]
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SWITCH
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sample-neighbor
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PLOT
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Inventory Size
Frequency
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@#$#@#$#@
## WHAT IS IT?

Implementation of **The Naming Game** model mentioned in the paper: 


> Baronchelli, A., Felici, M., Loreto, V., Caglioti, E., & Steels, L. (2006). Sharp transition towards shared vocabularies in multi-agent systems. Journal of Statistical Mechanics: Theory and Experiment, 2006(6), P06014–P06014. https://doi.org/10.1088/1742-5468/2006/06/p06014

This model is a way to study the evolution of words referring to an object in a closed world where a pair of people try to name an object by acting as a sender and reciever.
The sender selects a word from its inventory or generates a new word. The reciever compares the word to its own inventory. If the reciever adds the word to its inventory if the word is not present, otherwise it resets its inventory to only keep the sendor's word. The interaction is a success if the word selected by the sender is present in the reciever's inventory, otherwise it is a failure. The goal of the model is track how many unique words exist in the world, and on average how words are used by the people. 

## HOW IT WORKS


1. The model starts with `num-agents` number of people. Each person in initialized with an empty list of words. 
2. At each time step a random person is selected to be a sender.
3. Another person is selected as a reciever who is different from the sender. The selection of the reciever is based on two possible selection strategies. 
	- Random: Select a random person from the world as a reciever. 
	- Sample-neighbor: Select a random person from the neighborhood radius of size `neighbor-radius` of the sender. 
4. The sender selects a sender-word to send as a message to the reciever. This message is the word used by the sender to describe the object in the world. The word selection is based on the following strategies:
	- If the word inventory for the sender is empty create a new sender-word by sampling a random `word-length` between 1 and `max-word-length`. Then a word of length `word-length`is created by sampling a character between "a" and "z" for each position of the word. 
	- If the word inventory for the sender is non-empty, then the sender-word is selected by:
		* shortest word for the sender's word inventory, if `shortest-word` is ON
		* random word from the sender's word inventory, otherwise
5. Once the reciever recieves the message. It updates its inventory in the following ways:
	- If the word exists in the reciever's word inventory then the inventory is emptied and the sender-word is added to the inventory. This interaction is also considered a success, and `num-success` is incremented. 
	- If the word does not exists in the reciever's word inventory then the inventory the sender-word is added to the inventory. This interaction is also considered a failure, and `num-failure` is incremented. 
6. The global `all-words` list is set to all the unique words across all people. `total-words` is updated with the length of `all-words`. `mean-words` and `std-words` is also updated with the mean and standard deviation of number of words in each person's inventory.  


## HOW TO USE IT

SETUP button — sets up the model by creating the people.
GO button — runs the model
GO ONCE button — runs the model for one tick

The following inputs control how the model is setup:

SAMPLE-NEIGHBOR switch — weather to sample reciever from neighborhood of radius `neighbor-radius` of a sender
SHORTEST-WORD switch — weather to select the shortest word for the message by the sender or use a random word from the available word inventory of the sender
NUM-AGENTS slider - number of agents in the model
MAX-WORD-LENGTH slider - maximum allowed length for word used in a message


## THINGS TO NOTICE

Notice how the total unique words first rise to a max value and then converges to a very low value. If the `neighbor-radius` is very low then total unique words may converge to something small but not equal to 1. Also, notice that the mean number of words also rises  

## THINGS TO TRY

* Try different settings of `sample-neighbor`, `neighbor-radius`, `shortest-word`, `max-word-length`, and `num-agents`. 


## EXTENDING THE MODEL

* You may want to implement different ways of: sampling neighbors and sampling words for the sender
* The current world only has a single object. It would be interesting to change the code to allow multiple objects in the world. This will require keeping a seperate word list for each object within each person. 

## NETLOGO FEATURES

`in-radius` to sample from radius. 
`word` to create a new word. 

## CREDITS AND REFERENCES

If you are using this netlogo model file then please cite: 

Shubhanshu Mishra (2019). The Number Game. https://github.com/napsternxg/netlogo-models



The model was first presented in the paper: Baronchelli, A., Felici, M., Loreto, V., Caglioti, E., & Steels, L. (2006). Sharp transition towards shared vocabularies in multi-agent systems. Journal of Statistical Mechanics: Theory and Experiment, 2006(6), P06014–P06014. https://doi.org/10.1088/1742-5468/2006/06/p06014


## COPYRIGHT AND LICENSE

Copyright 2019 Shubhanshu Mishra.

![CC BY-NC-SA 3.0](http://ccl.northwestern.edu/images/creativecommons/byncsa.png)

This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License.  To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-sa/3.0/ or send a letter to Creative Commons, 559 Nathan Abbott Way, Stanford, California 94305, USA.

Commercial licenses are also available. To inquire about commercial licenses, please contact Shubhanshu Mishra at https://twitter.com/TheShubhanshu.
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