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;; sustainable development of Agricultural and pastoral areas

breed [people person] ;; sheep is its own plural: we use "a-sheep" as the singular.
breed [cows cow]

globals [
  attendance        ;; the current attendance at the bar
  history           ;; list of past values of attendance
  agr-patches     ;; agentset of green patches representing the residential area
  pas-patches       ;; agentset of blue patches representing the bar area
  ;; crowded-patch     ;; patch where we show the "CROWDED" label
]

turtles-own [
  strategies      ;; list of strategies
  best-strategy   ;; index of the current best strategy
  attend?         ;; true if the agent currently plans to attend the bar
  prediction      ;; current prediction of the bar attendance
  value           ;; value of each production
]

to setup
  clear-all
  set-default-shape people "plant"
  set-default-shape cows "cow"

  ;; create the 'homes'
  set agr-patches patches with [(pxcor < 0 and pycor < 0) or (pxcor > 0 and pycor > 0 )]
  ask agr-patches[ set pcolor green ]

  ;; create the 'bar'
  set pas-patches patches with [pxcor > 0 and pycor < 0 or (pxcor < 0 and pycor > 0 )]
  ask pas-patches [ set pcolor blue ]

  ;; initialize the previous attendance randomly so the agents have a history
  ;; to work with from the start
  set history n-values (memory-size * 2) [random 100]
  ;; the history is twice the memory, because we need at least a memory worth of history
  ;; for each point in memory to test how well the strategies would have worked

  set attendance first history

  ;; use one of the patch labels to visually indicate whether or not the
  ;; bar is "crowded"
  ;;ask patch (0.75 * max-pxcor) (0.5 * max-pycor) [
  ;;  set crowded-patch self
  ;;  set plabel-color red
  ;;]

  ;; create the agents and give them random strategies
  ;; these are the only strategies these agents will ever have though they
  ;; can change which of this "bag of strategies" they use every tick
  create-people 100 [
    set color brown
    set size 1.5
    set value 5 + 5 * random 2
    set strategies n-values number-strategies [random-strategy]
    set best-strategy first strategies
    move-to-empty-one-of agr-patches
    update-strategies
  ]

  create-cows 100 [
    set color yellow
    set size 1.2
    set value 10 + 10 * random 3
    set strategies n-values number-strategies [random-strategy]
    set best-strategy first strategies
    move-to-empty-one-of pas-patches
    update-strategies
  ]

  ;; start the clock
  reset-ticks
end


to go
  ;; update the global variables
  ;;ask crowded-patch [ set plabel "" ]
  ;; each agent predicts attendance at the bar and decides whether or not to go
  ask turtles [
    set prediction predict-attendance best-strategy sublist history 0 memory-size
    set attend? (prediction <= overcrowding-threshold)  ;; true or false
  ]
  ;; depending on their decision, the agents go to the bar or stay at home
  ask people [
    ifelse attend?
      [ move-to-empty-one-of pas-patches
        set attendance attendance + 1 ]
      [ ;;move-to-empty-one-of agr-patches
      ]
      set value value - 1
    
  ]

  ask cows [
    ifelse attend?
      [ move-to-empty-one-of agr-patches
        set attendance attendance + 1 ]
      [ ;;move-to-empty-one-of pas-patches
      ]
      set value value - 2
  ]

  ;; if the bar is crowded indicate that in the view
  set attendance count turtles-on pas-patches
  ;;if attendance > overcrowding-threshold [
  ;;  ask crowded-patch [ set plabel "CROWDED" ]
  ;;]
  ;; update the attendance history
  ;; remove oldest attendance and prepend latest attendance
  set history fput attendance but-last history
  ;; the agents decide what the new best strategy is
  ask turtles [ update-strategies ]
  ;; advance the clock
  tick
end

;; determines which strategy would have predicted the best results had it been used this round.
;; the best strategy is the one that has the sum of smallest differences between the
;; current attendance and the predicted attendance for each of the preceding
;; weeks (going back MEMORY-SIZE weeks)
;; this does not change the strategies at all, but it does (potentially) change the one
;; currently being used and updates the performance of all strategies
to update-strategies
  ;; initialize best-score to a maximum, which is the lowest possible score
  let best-score memory-size * 100 + 1
  foreach strategies [ the-strategy ->
    let score 0
    let week 1
    repeat memory-size [
      set prediction predict-attendance the-strategy sublist history week (week + memory-size)
      set score score + abs (item (week - 1) history - prediction)
      set week week + 1
    ]
    if (score <= best-score) [
      set best-score score
      set best-strategy the-strategy
    ]
  ]
end

;; this reports a random strategy. a strategy is just a set of weights from -1.0 to 1.0 which
;; determines how much emphasis is put on each previous time period when making
;; an attendance prediction for the next time period
to-report random-strategy
  report n-values (memory-size + 1) [1.0 - random-float 2.0]
end

;; This reports an agent's prediction of the current attendance
;; using a particular strategy and portion of the attendance history.
;; More specifically, the strategy is then described by the formula
;; p(t) = x(t - 1) * a(t - 1) + x(t - 2) * a(t -2) +..
;;      ... + x(t - MEMORY-SIZE) * a(t - MEMORY-SIZE) + c * 100,
;; where p(t) is the prediction at time t, x(t) is the attendance of the bar at time t,
;; a(t) is the weight for time t, c is a constant, and MEMORY-SIZE is an external parameter.
to-report predict-attendance [strategy subhistory]
  ;; the first element of the strategy is the constant, c, in the prediction formula.
  ;; one can think of it as the agent's prediction of the bar's attendance
  ;; in the absence of any other data
  ;; then we multiply each week in the history by its respective weight
  report 100 * first strategy + sum (map [ [weight week] -> weight * week ] butfirst strategy subhistory)
end

;; In this model it doesn't really matter exactly which patch
;; a turtle is on, only whether the turtle is in the home area
;; or the bar area.  Nonetheless, to make a nice visualization
;; this procedure is used to ensure that we only have one
;; turtle per patch.
to move-to-empty-one-of [locations]  ;; turtle procedure
  move-to one-of locations
  while [any? other turtles-here] [
    move-to one-of locations
  ]
end


; Copyright 2007 Uri Wilensky.
; See Info tab for full copyright and license.