;; text, like this, that start with semi-colons are comments and do not effect anything
;;
;; First we have lists of general and individual properties/slots

globals [av-value]

;; only extra attribute is amount of value gained in the rounds of giving
;; the colour of agents is used for whether it is a giver (green) or not (red)
;; its shape for whether it is gaining above average value (happy) or below (sad)

turtles-own [value]


;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Next we have the procedure to initialise the simulation
;; this is executed when one presses the "setup" button

to setup 
  clear-all ;; this clears everything at the start - a clean slate
  
  create-turtles population  [
      set shape "face neutral"
      ifelse with-probability initial-prob-coop 
        [set color green] 
        [set color red]
      set size 1.5
      setxy random-xcor random-ycor
    ]
  
    ;; link up to random nodes
    ask turtles [
      create-links-with n-of num-links other turtles
    ]
  
  spread-nodes
  
  reset-ticks                            ;; this initialises the simulation time system and graphs
end

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Next we have the procedure to progress the simulation one time step
;; this is executed when one presses the "step" button or repeatedly
;; if one presses the "go" button

to go
  let chosen nobody
  ask turtles [set value 0]

  ;; give some value away if you are green (altruistic)
  ask turtles with [color = green] [
    if any? link-neighbors [
      repeat num-give [
        ask one-of link-neighbors [set value value + 1]   
        set value value - 0.1  
      ]
    ]
  ]
  
  ;; compare oneself with a random other and if its doing better 
  ;; then drop links, link to it, copy its strategy and link
  ;; to its friends
  ask turtles [
    if with-probability prob-compare [
      if any? link-neighbors [
        set chosen one-of other turtles
        if value < [value] of chosen [
          set color [color] of chosen
          ask my-links [die]
          create-link-with chosen
        ]
      ]
    ]
  ]
  
  ;; random reset of links
  ask turtles [
    if with-probability prob-rand-reset [
      ask my-links [die]
      create-link-with one-of other turtles
      set shape "face neutral"
    ]
  ]
  
  ;; if too many links drop one
  ask turtles [
    if count my-links > num-links [
      ask one-of my-links [die]
    ]
  ]
  
  ;; if not enough links link to one FOF
  ;; turtle-set turns a list of agents into a agent set
  ask turtles [
    if count my-links < num-links [
      if any? other turtle-set [link-neighbors] of link-neighbors [
        create-link-with one-of other turtle-set [link-neighbors] of link-neighbors
      ]
    ]
  ]
  
  ;; swap colors
  ask turtles [
    if with-probability prob-change [
      ifelse color = green 
        [set color red] 
        [set color green]
    ]
  ]
  
  set av-value mean [value] of turtles
  
  ask turtles [
    if value > av-value [set shape "face happy"]
    if value < av-value [set shape "face sad"]
  ]
  
  spread-nodes
  
  tick                                   ;; this progresses the tick counter by one
end

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Finally we have definitions of the various action words/commands we use in the above
;; this makes the code easier to read and so that chunks of code can be reused at will
;; DO NOT WORRY about the detail of these (yet)

to spread-nodes
  repeat 100 [ layout-spring turtles links 0.2 5 3 ]  ;; spreads nodes out
end

to-report with-probability [prob]
  ;; returns value "TRUE" with probability determined by input
  report random-float 1 < prob
end
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@#$#@#$#@
## WHAT IS IT?

This is an example model, used as part of the "2-day Introduction to Agent-Based Modelling".  

It is a model where cooperative agents self-organise to avoid non-cooperators. Agents connect to each other and change their links. Some agents freely give to those they are linked with (at a small cost to themselves), the rest do not at start only 10% are givers, and all linked randomly. After that all agents follow same rules (apart from giving/not giving). But after a while the system self-organises to avoid non-givers and givers predominate.

## BEHAVIOURAL RULES

Each time click:

1. Givers cause their neighbours to receive units of value at random
1. With a probability (prob-compare) an agent picks another at random.  If its value (from gifts) is less than the one picked it imitates its strategy (giving/not), kills links and links to it 
1. With a probability (prob-rand-reset) kill all links and link to a random agent
1. If it has too many links, drop one at random
1. If it has too few link to someone agent is linked to (so called “friend of a friend”)
1. With a probability (prob-change) swap colours

## HOW TO USE IT

Press...
"setup" initialises simulation, creating random nodes and network
"step" makes it progress one time tick
"go" runs the simulation

Sliders:
population - how many agents in the population
initial-prob-coop - initial probability of a node being a giver
num-links - initial number of links of each node and the number nodes tend to if they can
prob-rand-reset - probability of a random reset (see above)
prob-compare - probability of comparing self against another node (see above)
prob-change - probability that a node will flip from giver to non-giver (or vice versa)
num-give - number of rounds of giving by each giving node each tick (see above)

## THINGS TO NOTICE

Givers eventually organise into groups, largely excluding non-givers.  Eventually givers predominate.

## THINGS TO TRY

* Try different settings to settings to see how robust this self-organising effect is
* Try “commenting out” rules and see what happens.

## EXTENDING THE MODEL

* Try changing the code to make it even tougher against givers, and see what it takes to stop them eventually dominating
* Add additional graphs and statistics to understand the process better

## NETLOGO FEATURES

Note use of NetLogo features for each making, breaking and using networks!

## CREDITS AND REFERENCES

The algorithm is loosely based upon the SLAC algorithm, e.g.

Hales, D. & Arteconi, S. (2006) SLACER: A Self-Organizing Protocol for Coordination in P2P Networks. IEEE Intelligent Systems, 21(2):29-35. (version at: http://cfpm.org/~david/papers/slacer-final.pdf)

Model by Bruce Edmonds, Feb 2013
http://cfpm.org/simulationcourse
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