OrgComms.nlogo

extensions [ CSV ]
globals [ data messages-total informed-before informed-after ]
turtles-own [ id manager message? messages-received signal-quality message-quality sum-message-quality mean-message-quality ]

to setup
  clear-all
  file-close-all
  reset-ticks
  reset-timer
  ifelse empty? input-file [ set data csv:from-file "./org.csv" ] [ set data csv:from-file input-file ]
  foreach data [ datapoint -> create-agent-from-datapoint datapoint ]
  print timer
  ask links [ set color 3 ]
  set messages-total 1 ;; start with 1 message org-wide, receved by all
  set informed-before 0
  set informed-after 0
  clear-all-plots
end

to load-world
  ifelse empty? input-file [ import-world "./world.csv" ] [ import-world input-file ]
end

to go
  ;; export-view (word ticks ".png") ; exporting current view to PNG - can make animated GIF with Giphy
  ifelse ( informed-before = informed-after ) [ start-messaging ] [ ask turtles with [ not message? ] [ communicate ] ]
  set informed-before informed-after
  set informed-after count turtles with [ message? = true ]
  tick
end

to create-agent-from-datapoint [ nm ] ;; nm is a list [ name manager ] for a tree structure
  let one ( word first nm )
  let two ( word last nm )
  type one type " " print two ;; debug output
  if ( not any? turtles with [ id = one ] ) [
    create-turtles 1 [
      set size 3
      set shape "dot"
      set messages-received 1 ;; start with 1 message org-wide, receved by all
      set mean-message-quality 1
      set sum-message-quality 1
      set id one
    ]
  ]
  if ( one != two ) [
    if ( not any? turtles with [ id = two ] ) [
      create-turtles 1 [
        set size 3
        set shape "dot"
        set messages-received 1 ;; start with 1 message org-wide, receved by all
        set mean-message-quality 1
        set sum-message-quality 1
        set id two
      ]
    ]
    ask turtles with [ id = one ] [
      create-links-with turtles with [ id = two ]
      layout-spring turtles links 0.2 2.0 0.5
    ]
  ]
  ;; tick
end

to start-messaging ;; observer procedure - resets state of messaging and injects new message with random agent
  ask turtles [
    set message? false
    set color black
  ]
  ask one-of turtles [
    set message? true
    set signal-quality 1
    set message-quality 1
    set color white
  ]
  set messages-total messages-total + 1
end

to communicate ;; turtle procedure
  let source one-of link-neighbors with [ message? ] ;; there can be only one informed neighbor in the tree structure
  if ( source != nobody ) [
    set signal-quality signal-decay-factor * ( [ signal-quality ] of source )
    if ( signal-quality > perception-threshold ) [
      set message? true
      set message-quality quality-decay-factor * ( [ message-quality ] of source )
      set sum-message-quality sum-message-quality + message-quality
      set messages-received messages-received + 1
      set mean-message-quality sum-message-quality / messages-received
      set color scale-color white ( message-quality * 50) 0 50
    ]
  ]
end
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@#$#@#$#@
## WHAT IS IT?

This is simple information dissemination model in tree-like hierarchical organization, with signal and information quality decay.

## HOW IT WORKS

The network is loaded from a CSV file, containing "node_id,parent_id" pairs. Think of it as employee/manager pairs.

The first node_id in the CSV set becomes "CEO".

Every communication cycle starts with a message (of quality 1) generated by a random agent in the organization. Subsequently, the massage is sent along the links by agents (turtles) who have the message, with signal and quality decay applied. The way it's implemented is that agents currently without message will ask neighbors if there is new message, and will pull the message. If signal falls below perception threshold, the message is not registered. When message transmission within the organization stops (whether due to decaying signal, or when all agents have registered new message), new messaging cycle begins. Message spread is visualised, with quality rendered as gray scale (white = 1).

## HOW TO USE IT

**Setup** loads the org structure from a file specified in the **input-file** input. If none specified, the default is *org.csv* in the model directory. 

With large number of data points, loading and layout take long time, so it makes sense to export the world (by running export-world as an observer). Loading of exported wold is supported via **load world** button, defaulting to *world.csv* in the current directory.

There are three slider inputs:
* **signal-decay-factor** specifies the signal power on the receiving end, as a fraction of that with the sender/relay. The signal power at the point of origination is always 1;
* **quality-decay-factor** - the ["Chinese whispers"](https://en.m.wikipedia.org/wiki/Chinese_whispers) factor. Similar to the previous one, but defines the message quality decay, as 100% credible information can become less so as it's passed on through the communication lines;
* **perception-threshold** - if the signal power falls below that, the message is not received down the comms line.

There are two plots:
* One plots percentage of messages reaching agents in the org - CEO (green) v mean in the organization;
* The other one plots message _quality_ - again, average for the CEO v mean in the organization.

## THINGS TO NOTICE

In strictly hierarchical organization, the CEO (the top node in the org tree) is the most informed entity. The CEO may not have the best quality information though - depending on levels of hierarchy and the parameters!

## THINGS TO TRY

Experiment with the values. For example, see how lowering perception threshold in a hierarchical organization may result in CEO being relatively well-informed, while the rest of the organization receives low percentage of message and is much less informed.

## EXTENDING THE MODEL

There are many ways to extend the model - add building of the organizational network defined by parameters, and add/change the information dissemination parameters

## NETLOGO FEATURES

The model is using CSV extension and layout features in attempt to render the organizational structure nicely.

## RELATED MODELS

Communication-T-T Example in the NetLogo Models Library provided basis and inspiration for this model.

## CREDITS AND REFERENCES

The model is created in 2018 by Svyatoslav Pidgorny. It is free for you to use, modify and distribute; attribution or feedback to the author would be nice. There is no warranty - use results or make conclusions at your own risk!

[GitHub](https://github.com/SP3269/Agent-Based-Models)
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