reflectiveliving_Multiple-Diffusion-Behavior-Epidemic/second-run/multi-behavior-pref-att.nlogo
extensions [array profiler]

globals [
  costs  ;; costs of each of the behaviors
  utilities ;; intrinsic benefits from each of the behaviors
  behav-id-list ;; list of behavior ids
  all-zeros ;; a list of length num-behaviors containing zeros

  roundActiveCounts  ;; stores the number of agents who turned active in the previous round for each behavior
  
  total-active-count ;; total number of active agents
  total-unique-active-count ;; total number of unique active agents
  active-counts ;; holds number of active agents per behavior
  old-active-counts ;; temp variable for calculating roundActiveCount
  total-resource ;; total resource available to the agents after seed selection
  utilization ;; network wide resource utilization
  
  num-seeds-per-behavior ;; vector for storing number of seeds per behavior
  
  ;; coloring variables ;;
  neutral
  base
  step
  scaling
]

turtles-own
[
  resource   ;; total resource available to this agent for behavior adoption 
  thresholds ;; thresholds of adoption for each possible behavior
  actives?   ;; whether this agent is active or not for the particular behavior
  weight-sums ;; used to sum up influence weight from active neighbors
  payoffs    ;; payoff from each of the the behaviors
  consider?  ;; boolean array for indicating whether a behavior will be considered for adoption or not
  ;; turtle variable required for seed selection algorithms
  one-step-spreads ;; vector containing the expected one-step adoption of the turtle
]

links-own
[
  end1-weight ;; influence weight exerted by the end2 on end1
  end2-weight ;; vice versa
]

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to setup
  clear-all
  
  set-coloring-variables
  
  random-seed 12345 + rand-seed-network
  setup-preferential-attachment-network
  
  set-behaviors
  
  set-edge-weights
  
  random-seed 123456 + rand-seed-resource
  set-resource
  
  random-seed 1234567 + rand-seed-threshold
  set-thresholds
  
  set-actives
  set-weight-sums
  set-payoffs
  set-consider 
  
  random-seed 12345 + rand-seed-network
  select-seeds
  
  setup-indicators
  
  reset-ticks
end

to set-coloring-variables
  set neutral white
  set base red
  set step 10
  set scaling (base + num-behaviors * step) - (base + base + step)
end

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;;Preferential Attachment Network;;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to setup-preferential-attachment-network
  set-default-shape turtles "person"
  
  ;; make the initial network of two turtles and an edge
  make-node nobody        ;; first node, unattached
  make-node turtle 0
  
  ;; now add rest of the nodes
  repeat number-of-nodes - 2 [
    make-node find-partner
    layout-preferential-attachment-network
  ]
end

;; used for creating a new node
to make-node [old-node]
  crt 1
  [
    set color neutral
    if old-node != nobody
      [ create-link-with old-node 
        ;[ 
        ;  set color neutral - 2 
        ;]
        ;; position the new node near its partner
        move-to old-node
        fd 8
      ]
  ]
end

;; This code is borrowed from Lottery Example (in the Code Examples
;; section of the Models Library).
;; The idea behind the code is a bit tricky to understand.
;; Basically we take the sum of the degrees (number of connections)
;; of the turtles, and that's how many "tickets" we have in our lottery.
;; Then we pick a random "ticket" (a random number).  Then we step
;; through the turtles to figure out which node holds the winning ticket.
to-report find-partner
  let total random-float sum [count link-neighbors] of turtles
  let partner nobody
  ask turtles
  [
    let nc count link-neighbors
    ;; if there's no winner yet...
    if partner = nobody
    [
      ifelse nc > total
        [ set partner self ]
        [ set total total - nc ]
    ]
  ]
  report partner
end

;; lays out the preferential attachment network in aesthetic way
to layout-preferential-attachment-network
  ;; the number 3 here is arbitrary; more repetitions slows down the
  ;; model, but too few gives poor layouts
  repeat 3 [
    ;; the more turtles we have to fit into the same amount of space,
    ;; the smaller the inputs to layout-spring we'll need to use
    let factor sqrt count turtles
    ;; numbers here are arbitrarily chosen for pleasing appearance
    layout-spring turtles links (1 / factor) (7 / factor) (1 / factor)
    display  ;; for smooth animation
  ]
  ;; don't bump the edges of the world
  let x-offset max [xcor] of turtles + min [xcor] of turtles
  let y-offset max [ycor] of turtles + min [ycor] of turtles
  ;; big jumps look funny, so only adjust a little each time
  set x-offset limit-magnitude x-offset 0.1
  set y-offset limit-magnitude y-offset 0.1
  ask turtles [ setxy (xcor - x-offset / 2) (ycor - y-offset / 2) ]
end

to-report limit-magnitude [number limit]
  if number > limit [ report limit ]
  if number < (- limit) [ report (- limit) ]
  report number
end

;;;;;;;;;;;;;;;;;;;;;;;
;;;Parameterization;;;;
;;;;;;;;;;;;;;;;;;;;;;;

to set-behaviors
  ;set costs array:from-list n-values num-behaviors [random-float 1]
  ;set utilities array:from-list n-values num-behaviors [random-float 1]
  
  set costs read-from-string behavior-costs
  ifelse is-list? costs [
    set costs array:from-list costs
  ]
  [
    user-message "Input behaivor costs as a list i.e. within []"
    stop
  ]
  
  set utilities read-from-string behavior-utilities
  ifelse is-list? utilities [
    set utilities array:from-list utilities
  ]
  [
    user-message "Input behavior utilities as a list i.e. within []"
    stop
  ]
  
  set behav-id-list n-values num-behaviors [?]
  set all-zeros n-values num-behaviors [0]
end

;; sets edge weights such that sum of incoming edge weights equal to 1,
;; and each incoming edge for an agent has same weight
to set-edge-weights
  ask turtles [
    if count my-links != 0 [
      let weight 1.0 / count my-links
      ask my-links [
        set-weight myself weight
      ]
    ]
  ]
end

;; link procedure
to set-weight [node weight]
  ifelse end1 = node [
    set end1-weight weight
  ]
  [
    set end2-weight weight
  ]
end 

;; set individual resources for each agent
to set-resource
  ask turtles [
    set resource random-float 1
  ]
end

;; set individual active states for each agent
to set-actives
  ask turtles [
    set actives? array:from-list n-values num-behaviors [false]
  ]
end
   
;; set agent thresholds before every run 
to set-thresholds 
  ifelse matched-threshold? [
    ask turtles [
      let thresh random-float 1
      set thresholds array:from-list n-values num-behaviors [thresh]
    ]
  ]
  [   
    ask turtles [
      set thresholds  array:from-list n-values num-behaviors [random-float 1]
    ]
  ]
end

; set weight-sum for the behaviors to zero
to set-weight-sums
  ask turtles [
    set weight-sums array:from-list all-zeros
  ]
end

; set agent payoffs for all the behaviors
to set-payoffs
  ask turtles [
    set payoffs array:from-list all-zeros
  ]
end

; set the consider? boolean array
to set-consider
  ask turtles [
    set consider? array:from-list n-values num-behaviors [false]
  ]
end


;;;;;;;;;;;;;;;;;;;;;;;
;;;Seed Selection;;;;;;
;;;;;;;;;;;;;;;;;;;;;;;

to select-seeds 
  
  ;; Determine the seed distribution i.e. number of seeds per behavior
  ifelse seed-distribution = "uniform" [
    set-uniform-seed-distribution
  ][
  ifelse seed-distribution ="proportional to cost" [
    set-proportional-to-cost-seed-distribution
  ][
  ifelse seed-distribution = "inversely proportional to cost" [
    set-inversely-proportional-to-cost-seed-distribution
  ][
  ifelse seed-distribution = "highest cost behavior only" [
    set-highest-cost-behavior-only
  ][
  ifelse seed-distribution = "lowest cost behavior only" [
    set-lowest-cost-behavior-only
  ][
  ifelse seed-distribution = "in ratio" [
    set-in-ratio
  ][
  user-message "Specify the seed distribution"
  ]]]]]]
  
  ;; select seeds
  ifelse seed-selection-algorithm = "randomly-unlimited-seed-resource-batched" [
    randomly-unlimited-seed-resource-batched
  ][
  ifelse seed-selection-algorithm = "randomly-unlimited-seed-resource-incremental" [
    randomly-unlimited-seed-resource-incremental
  ][
  ifelse seed-selection-algorithm = "randomly-with-available-resource-batched" [
    randomly-with-available-resource-batched
  ][
  ifelse seed-selection-algorithm = "randomly-with-available-resource-incremental" [
    randomly-with-available-resource-incremental
  ][
  ifelse seed-selection-algorithm = "randomly-with-knapsack-assignment" [
    randomly-with-knapsack-assignment
  ][
  ifelse seed-selection-algorithm = "randomly-with-random-tie-breaking"[
    randomly-with-random-tie-breaking
  ][
  ifelse seed-selection-algorithm = "naive-degree-ranked-with-knapsack-assignment" [
    naive-degree-ranked-with-knapsack-assignment
  ][
  ifelse seed-selection-algorithm = "naive-degree-ranked-with-random-tie-breaking-no-nudging" [
    naive-degree-ranked-with-random-tie-breaking-no-nudging
  ][
  ifelse seed-selection-algorithm = "naive-degree-ranked-with-random-tie-breaking-with-nudging" [
    naive-degree-ranked-with-random-tie-breaking-with-nudging
  ][
  ifelse seed-selection-algorithm = "degree-and-resource-ranked-with-knapsack-tie-breaking" [
    degree-and-resource-ranked-with-knapsack-tie-breaking
  ][
  ifelse seed-selection-algorithm = "degree-and-resource-ranked-with-random-tie-breaking" [
    degree-and-resource-ranked-with-random-tie-breaking
  ][
  ifelse seed-selection-algorithm = "one-step-spread-ranked-with-random-tie-breaking" [
    one-step-spread-ranked-with-random-tie-breaking
  ][
  ifelse seed-selection-algorithm = "one-step-spread-hill-climbing-with-random-tie-breaking" [
    one-step-spread-hill-climbing-with-random-tie-breaking
  ][
  ifelse seed-selection-algorithm = "ideal-all-agent-adoption-without-network-effect" [
    ideal-all-agent-adoption-without-network-effect
  ][
  user-message "Specify the seed selection algorithm"
  ]]]]]]]]]]]]]]
end

;;;;;;;;;;;;;;;;;;;;;;;
;;;Seed distribution;;;
;;;;;;;;;;;;;;;;;;;;;;;

to set-uniform-seed-distribution  
  let seeds-per-behavior (round  (total-num-seeds / num-behaviors))
  
  set num-seeds-per-behavior array:from-list n-values num-behaviors [seeds-per-behavior]
end

to set-proportional-to-cost-seed-distribution
  set num-seeds-per-behavior array:from-list all-zeros
  
  let sum-behav-costs sum array:to-list costs
  
  foreach behav-id-list [
    array:set num-seeds-per-behavior ? (round ((array:item costs ?) / sum-behav-costs * total-num-seeds)) 
  ]
end

to set-inversely-proportional-to-cost-seed-distribution
  set num-seeds-per-behavior array:from-list all-zeros
  
  let sorted-behaviors sort-by sort-by-cost (behav-id-list)
  
  let sum-behav-costs sum array:to-list costs
  
  foreach behav-id-list [
    array:set num-seeds-per-behavior (item ? sorted-behaviors) (round ((array:item costs (item (num-behaviors - 1 - ?) sorted-behaviors)) / sum-behav-costs * total-num-seeds))
  ]
end

to set-highest-cost-behavior-only
  set num-seeds-per-behavior array:from-list all-zeros
  let sorted-behaviors sort-by sort-by-cost (behav-id-list)
  
  array:set num-seeds-per-behavior (first sorted-behaviors) total-num-seeds
end

to set-lowest-cost-behavior-only
  set num-seeds-per-behavior array:from-list all-zeros
  let sorted-behaviors sort-by sort-by-cost (behav-id-list)
  
  array:set num-seeds-per-behavior (last sorted-behaviors) total-num-seeds
end  

to-report sort-by-cost [first-behav-id second-behav-id]
  report array:item costs first-behav-id > array:item costs second-behav-id
end

to set-in-ratio
  set num-seeds-per-behavior array:from-list all-zeros
  let ratio read-from-string final-ratio
  let sum-ratio sum ratio
  foreach behav-id-list [
    array:set num-seeds-per-behavior ? (round ((item ? ratio) / sum-ratio * total-num-seeds))
  ]
end

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;;Maximum Utilization Computation;;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
to ideal-all-agent-adoption-without-network-effect
  ask turtles [
        foreach behav-id-list [
          array:set consider? ? true
          array:set payoffs ? array:item utilities ?
        ]
        
        let opt knapsack-decide
        
        foreach opt [
          array:set actives? ? true
        ]
        
        if not empty? opt [
          set-color
        ]
  ]
end

;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;;Random Seed Selection;;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;

;; selects seeds uniformly at random from the set of all agents
;; increase resource if necessary (unlimited resource of seed nodes), seeds selected in batches
to randomly-unlimited-seed-resource-batched
  foreach behav-id-list [
    let seeds n-of array:item num-seeds-per-behavior ? turtles
    ask seeds [
      let used used-resource
      if used + array:item costs ? > resource [
        set resource (used + array:item costs ?)
      ]
      ; set resource 1.0
      array:set actives? ? true
      set-color
    ]
  ]
end

;; same as the previous one but seeds selected one at a time
to randomly-unlimited-seed-resource-incremental
;  repeat number-of-seeds-per-behavior [
;     foreach behav-id-list [
;       ask one-of turtles with [array:item actives? ? = false] [force-adopt ?]
;     ]
;  ]
end

;; turtle procedure
to force-adopt [behav-id] 
  let used used-resource
  if used + array:item costs behav-id > resource [
    set resource (used + array:item costs behav-id)
  ]
  array:set actives? behav-id true
  set-color
end

;; seeds randomly selected from the nodes with available resource
;; ordering of the given behaviors matter
to randomly-with-available-resource-batched
  foreach behav-id-list [
    let pop turtles with [(resource - used-resource) >= array:item costs ?]
    let seeds nobody 
    ifelse array:item num-seeds-per-behavior ? > count pop [
      set seeds pop
    ]
    [
      set seeds n-of array:item num-seeds-per-behavior ? pop
    ]
      
    ask seeds [
      array:set actives? ? true
      set-color
    ]
  ]
end

to randomly-with-available-resource-incremental
;  repeat number-of-seeds-per-behavior [
;     foreach behav-id-list [
;       let seed one-of turtles with [((resource - used-resource) >= array:item costs ?) and (array:item actives? ? = false)]
;       if seed != nobody [
;         ask seed [
;           array:set actives? ? true
;           set-color
;         ]
;       ]
;     ]
;  ]
end
  
to randomly-with-knapsack-assignment
  let seeds-required array:from-list array:to-list num-seeds-per-behavior
  let pop turtles
  while [more-seeds-required? seeds-required] [
    let candidate-seed one-of pop
    ifelse candidate-seed = nobody [
      stop
    ]
    [ 
      ask candidate-seed [
        set pop other pop  
        
        foreach behav-id-list [
          if array:item seeds-required ? > 0 [
            array:set consider? ? true
            array:set payoffs ? (array:item utilities ?)
          ]
        ]
        
        let opt knapsack-decide
        
        foreach behav-id-list [
          array:set consider? ? false
          array:set payoffs ? 0
        ]
        
        foreach opt [
          array:set actives? ? true
          array:set seeds-required ? (array:item seeds-required ? - 1)
        ]
        
        if not empty? opt [
          set-color
        ]
      ]
    ]
  ]
end 

to-report more-seeds-required? [seeds-required]
  foreach behav-id-list [
    if array:item seeds-required ? > 0 [
      report true
    ]
  ]
  report false
end   
           
        
;; turtle procedure to report total used resouces by the already
;; adopted behaviors        
to-report used-resource
  let used 0
  foreach behav-id-list [
    if array:item actives? ? [
      set used used + array:item costs ?
    ]
  ]
  report used
end

to randomly-with-random-tie-breaking
  let seeds-required array:from-list array:to-list num-seeds-per-behavior
  let pop turtles
  
  while [more-seeds-required? seeds-required] [
    let seedsets map [n-of (array:item seeds-required ?) pop] (behav-id-list)
    
    let seeds reduce [(turtle-set ?1 ?2)] seedsets
    
    set pop pop with [not member? self seeds]
    
    ask seeds [
      let candidates filter [member? self (item ? seedsets)] (behav-id-list)
      let winner item (random length candidates) candidates
      array:set actives? winner true
      array:set seeds-required winner ((array:item seeds-required winner) - 1)
      ;; with resource nudging
      if array:item costs winner > resource [
        set resource array:item costs winner
      ]
      set-color
    ] 
  ]
end

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;;Degree Centrality Based;;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to naive-degree-ranked-with-knapsack-assignment
  let seeds-required array:from-list array:to-list num-seeds-per-behavior
  let pop turtles
  while [more-seeds-required? seeds-required] [
    let candidate-seed max-one-of pop [count link-neighbors]
    ifelse candidate-seed = nobody [
      stop
    ]
    [ 
      ask candidate-seed [
        set pop other pop  
        
        foreach behav-id-list [
          if array:item seeds-required ? > 0 [
            array:set consider? ? true
            array:set payoffs ? (array:item utilities ?)
          ]
        ]
        
        let opt knapsack-decide
        
        foreach behav-id-list [
          array:set consider? ? false
          array:set payoffs ? 0
        ]
        
        foreach opt [
          array:set actives? ? true
          array:set seeds-required ? (array:item seeds-required ? - 1)
        ]
        
        if not empty? opt [
          set-color
        ]
      ]
    ]
  ]
end

to naive-degree-ranked-with-random-tie-breaking-no-nudging
  let seeds-required array:from-list array:to-list num-seeds-per-behavior
  let pop turtles
  while [more-seeds-required? seeds-required] [
    let candidate-seed max-one-of pop [count link-neighbors]
    ifelse candidate-seed = nobody [
      stop
    ]
    [
      ask candidate-seed [
        set pop other pop
        let candidate-behavs filter [array:item seeds-required ? != 0] behav-id-list 
        let winner item (random length candidate-behavs) candidate-behavs
        if array:item costs winner <= resource [
          array:set actives? winner true
          array:set seeds-required winner ((array:item seeds-required winner) - 1)
          set-color
        ]
      ]
    ]
  ]
end

to naive-degree-ranked-with-random-tie-breaking-with-nudging
  let seeds-required array:from-list array:to-list num-seeds-per-behavior
  let pop turtles
  while [more-seeds-required? seeds-required] [
    let candidate-seed max-one-of pop [count link-neighbors]
    ifelse candidate-seed = nobody [
      stop
    ]
    [
      ask candidate-seed [
        set pop other pop
        let candidate-behavs filter [array:item seeds-required ? != 0] behav-id-list 
        let winner item (random length candidate-behavs) candidate-behavs
        array:set actives? winner true
        array:set seeds-required winner ((array:item seeds-required winner) - 1)
        ;; with resource nudging
        if array:item costs winner > resource [
          set resource array:item costs winner
        ]
        set-color
      ]
    ]
  ]
end
      

to degree-and-resource-ranked-with-knapsack-tie-breaking
  let seeds-required array:from-list array:to-list num-seeds-per-behavior
  let pops array:from-list n-values num-behaviors [turtles]
  while [continue-seed-selection? seeds-required] [
    let seedsets []
    foreach behav-id-list [
      let num-required-seeds ((array:item seeds-required ?) - (count turtles with [array:item actives? ?]))
      let seedset nobody
      ifelse num-required-seeds > count array:item pops ? [
        set seedset array:item pops ?
        array:set seeds-required ? 0 ;; to set continue-seed-selection? false
      ][
      ifelse num-required-seeds >= 0 [
        set seedset max-n-of num-required-seeds (array:item pops ?) [count link-neighbors with [resource >= (array:item costs ?)]]
      ][
      set seedset turtle-set nobody
      ]]
      set seedsets sentence seedsets seedset
      array:set pops ? ((array:item pops ?) with [not member? self seedset])
    ]
    
    let seeds turtle-set nobody
    foreach seedsets [
      set seeds (turtle-set seeds ?)
    ]
    
    ask seeds [
      foreach behav-id-list [
        if (member? self (item ? seedsets)) or array:item actives? ? [
          array:set consider? ? true
          array:set payoffs ? (array:item utilities ?)
        ]
      ]
      
      let opt knapsack-decide

      foreach behav-id-list [
        array:set consider? ? false
        array:set payoffs ? 0
      ]
      
      foreach behav-id-list [
        array:set actives? ? false
      ]
      
      foreach opt [
        array:set actives? ? true
      ]
      
      if not empty? opt [
        set-color
      ]
    ]      
  ]
end  

to-report continue-seed-selection? [seeds-required]
  foreach behav-id-list [
    if (array:item seeds-required ?) - (count turtles with [array:item actives? ?]) > 0 [
      report true
    ]
  ]
  report false
end

to degree-and-resource-ranked-with-random-tie-breaking
  let seeds-required array:from-list array:to-list num-seeds-per-behavior
  let pop turtles
  
  while [more-seeds-required? seeds-required] [
    let seedsets map [max-n-of (array:item seeds-required ?) pop [count link-neighbors with [resource >= array:item costs ?]]] (behav-id-list)
    
    let seeds reduce [(turtle-set ?1 ?2)] seedsets
    
    set pop pop with [not member? self seeds]
    
    ask seeds [
      let candidates filter [member? self (item ? seedsets)] (behav-id-list)
      let winner item (random length candidates) candidates
      array:set actives? winner true
      array:set seeds-required winner ((array:item seeds-required winner) - 1)
      ;; with resource nudging
      if array:item costs winner > resource [
        set resource array:item costs winner
      ]
      set-color
    ] 
  ]
end

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;;Expected one step adoption;;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

;; one step spread is the expected adoption in one time step
to one-step-spread-ranked-with-random-tie-breaking
  calculate-expected-one-step-adoption
  
  let seeds-required array:from-list array:to-list num-seeds-per-behavior
  let pop turtles
  
  while [more-seeds-required? seeds-required] [
    ;let seedsets map [max-n-of (array:item seeds-required ?) (pop with [resource >= array:item costs ?]) [array:item one-step-spreads ?]] (behav-id-list)
    ;; with resource nudging
    let seedsets map [max-n-of (array:item seeds-required ?) pop [array:item one-step-spreads ?]] (behav-id-list) 
    
    let seeds reduce [(turtle-set ?1 ?2)] seedsets
    
    set pop pop with [not member? self seeds]
    
    ask seeds [
      let candidates filter [member? self (item ? seedsets)] (behav-id-list)
      let winner item (random length candidates) candidates
      array:set actives? winner true
      array:set seeds-required winner ((array:item seeds-required winner) - 1)
      ;; with resource nudging
      if array:item costs winner > resource [
        set resource array:item costs winner
      ]
      set-color
    ] 
  ] 
end

to one-step-spread-hill-climbing-with-random-tie-breaking
  let seeds-required array:from-list array:to-list num-seeds-per-behavior
  let pop turtles
  let seedsets array:from-list n-values num-behaviors [turtle-set nobody]
  
   while [more-seeds-required? seeds-required] [
     let new-seedsets map [compute-optimal-seedset-one-step-spread ? (array:item seeds-required ?) (array:item seedsets ?) pop] (behav-id-list) 
     
     foreach behav-id-list [
       if array:item seeds-required ? > count (item ? new-seedsets) [
         array:set seeds-required ? 0
       ]
     ]
     
     let new-seeds reduce [(turtle-set ?1 ?2)] new-seedsets
     
     set pop pop with [not member? self new-seeds]
     
     ask new-seeds [
      let candidates filter [member? self (item ? new-seedsets)] (behav-id-list)
      let winner item (random length candidates) candidates
      array:set actives? winner true
      array:set seeds-required winner ((array:item seeds-required winner) - 1)
      array:set seedsets winner (turtle-set array:item seedsets winner self)
      ;; with resource nudging
      if array:item costs winner > resource [
        set resource array:item costs winner
      ]
      set-color
    ]   
   ]  
end

to-report compute-optimal-seedset-one-step-spread [b-id num-seeds seedset remaining-pop]
  let pop turtles with [not member? self seedset]
  
  let new-seedset (turtle-set nobody)
  
  repeat num-seeds [
    ;; additional one-step adoption calculation
    init-one-step-spreads
    ask pop [
      let num-neighbors (count link-neighbors)
      let prob 0
      if (num-neighbors != 0) [
        set prob 1 / num-neighbors
      ]
      if [resource] of self >= array:item costs b-id [
        ask link-neighbors [
          array:set one-step-spreads b-id ((array:item one-step-spreads b-id) + prob)
        ]
      ]
    ]
    
    let newseed max-one-of remaining-pop [array:item one-step-spreads b-id]
    set new-seedset (turtle-set new-seedset newseed)
    ifelse newseed = nobody [
      report new-seedset
    ]
    [
      ask newseed [
        set pop other pop
        set remaining-pop other remaining-pop
      ]
    ]
  ]
  
  report new-seedset       
end

to calculate-expected-one-step-adoption
  init-one-step-spreads
  
  ask turtles [
    let num-neighbors (count link-neighbors)
    let prob 0
    if (num-neighbors != 0) [
      set prob 1 / num-neighbors
    ]
    ask link-neighbors [
      foreach behav-id-list [
        if [resource] of myself >= array:item costs ? [
          array:set one-step-spreads ? ((array:item one-step-spreads ?) + prob)
        ]
      ]
    ]
  ]
end

to init-one-step-spreads
  ask turtles [
    set one-step-spreads array:from-list all-zeros
  ]
end

to reset-one-step-spreads
  ask turtles [
    foreach behav-id-list [
      array:set one-step-spreads ? 0
    ]
  ]
end
  

;;;;;;;;;;;;;;;;;;;;;;
;;;Linear Threshold;;;
;;;;;;;;;;;;;;;;;;;;;;

to go
  if no-new-adoption? [stop]
  
  reset-roundActiveCounts
  reset-weight-sums
  reset-consider
  reset-payoffs
  
  propagate-influence
  make-adoption-decision
  
  update-indicators
  
  tick
end

to-report no-new-adoption?
  let new-adoption reduce + array:to-list roundActiveCounts
  ifelse new-adoption = 0
  [report true]
  [report false]
end 

to reset-roundActiveCounts
  set roundActiveCounts array:from-list all-zeros
end

to reset-weight-sums
  ask turtles [
    set weight-sums array:from-list all-zeros
  ]
end

to reset-consider
  ask turtles [
    foreach behav-id-list [
      array:set consider? ? (array:item actives? ?)   ;; previously adopted behaviors are automatically considered
      ;array:set consider? ? false ; only behaviors crossing the threshold are considered
    ]
  ]
end

to reset-payoffs
  ask turtles [
    set payoffs array:from-list all-zeros
  ]
end    

to propagate-influence 
  ask turtles [
    foreach behav-id-list [
      if array:item actives? ?
      [
        ask my-links [
          influence myself ?
          ;set color (base + ? * step)
        ]
      ]
    ]
  ]
end

;; link procedure; influencer influences the other end of the link; link procedure
to influence [influencer behav-id]
  ifelse end1 = influencer [
    let weight end2-weight
    ask end2 [
      let t array:item weight-sums behav-id
      array:set weight-sums behav-id ( t + weight )
    ]
  ]
  [
    let weight end1-weight
    ask end1 [
      let t array:item weight-sums behav-id
      array:set weight-sums behav-id ( t + weight )
    ]
  ]
end

;; decision to turn active or not
to make-adoption-decision
  ask turtles [
    calculate-payoffs
    new-consider
    
    let opt knapsack-decide
    
    foreach behav-id-list [
     array:set actives? ? false
    ]
  
    foreach opt [
     array:set actives? ? true
    ]
    
    ifelse empty? opt [
      set color neutral
    ]
    [
      set-color
    ]
  ]
end

;; turtle procedure; calculate the present payoffs for each behavior
to calculate-payoffs
  let w 0.5
  let extra 0
  if switching-cost? [
    set extra benefit-of-inertia
  ]
  
  foreach behav-id-list [
    array:set payoffs ? w * (array:item utilities ? + extra) + (1 - w) * (array:item weight-sums ?)
  ]
end

;; turtle procedure to check which behaviors cross the node thresholds
to new-consider
   foreach behav-id-list [
     if (array:item weight-sums ?) >= (array:item thresholds ?) [
       array:set consider? ? true
     ]
   ]
end

;; turtle procedure for adoption decision making
;; employs brute-force search for the knapsack problem
;; ; should implement dynamic programing version of the knapsack algorithm
to-report knapsack-decide 
  let consider-behav-list filter [array:item consider? ?] behav-id-list
  
  let power-set compute-power-set consider-behav-list
  
  let opt [] 
  let max-payoff 0
  foreach power-set [
    let total-cost 0
    foreach ? [
      set total-cost total-cost + (array:item costs ?)
    ]
    if total-cost <= resource [
      let total-payoff 0
      foreach ? [
        set total-payoff total-payoff + (array:item payoffs ?)
      ]
      if total-payoff > max-payoff [
        set max-payoff total-payoff
        set opt ?
      ]
    ]
  ]
  
  report opt
end

to-report compute-power-set [list-of-items]
;  ifelse empty? list-of-items [
;    report [[]]
;  ]
;  [
;    let head first list-of-items
;    let rest but-first list-of-items
;   let rest-power-set compute-power-set rest
;    let new-subsets []
;    foreach rest-power-set [
;      set new-subsets sentence new-subsets (list (sentence ? head))
;   ]
;    report sentence rest-power-set new-subsets
;  ]
  
  report compute-power-set-tail-recursive [[]] list-of-items
end

to-report compute-power-set-tail-recursive [list-of-subsets list-of-items]
  ifelse empty? list-of-items [
    report list-of-subsets
  ]
  [
    let head first list-of-items
    let list-of-new-subsets map [sentence ? head] list-of-subsets
    report compute-power-set-tail-recursive (sentence list-of-subsets list-of-new-subsets) (but-first list-of-items)
  ]
end
    
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;;Reporters for User Interface;;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to setup-indicators 
  set total-resource sum [resource] of turtles
  
  set total-active-count count turtles with [active?]
  
  set active-counts array:from-list n-values num-behaviors [count turtles with [array:item actives? ?]]
  
  set old-active-counts array:from-list all-zeros
  
  set total-unique-active-count sum array:to-list active-counts
  
  let utilized-resource 0
  foreach behav-id-list [
    set utilized-resource utilized-resource + (array:item active-counts ? * array:item costs ?)
  ]
  
  set utilization utilized-resource / total-resource
  
  set roundActiveCounts array:from-list array:to-list active-counts
end

to update-indicators
  set total-active-count count turtles with [active?]
  
  foreach behav-id-list [
    array:set old-active-counts ? array:item active-counts ?
  ]
  
  foreach behav-id-list [
    array:set active-counts ? count turtles with [array:item actives? ?]
  ]
  
  foreach behav-id-list [
    array:set roundActiveCounts ? (array:item active-counts ? - array:item old-active-counts ?)
  ]
  
  set total-unique-active-count sum array:to-list active-counts
  
  let utilized-resource 0
  foreach behav-id-list [
    set utilized-resource utilized-resource + (array:item active-counts ? * array:item costs ?)
  ]
  
  set utilization utilized-resource / total-resource
end

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;;Auxiliary Procedures;;;;;;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

;; turtle procedure
to set-color
;  set color 0
;  let num 0
;  foreach behav-id-list [
;    if array:item actives? ? [
;      set color color + (base + ? * step)
;      set num num + 1
;    ]
;  ]
;  if num > 1 [
;    set color color + scaling
;  ]
end
  

;; turtle procedure
to-report active?
  foreach behav-id-list [
    if array:item actives? ? [
      report true
    ]
  ]
  report false
end
     
    
  
@#$#@#$#@
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0
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ticks
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SLIDER
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number-of-nodes
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2000
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1
1
NIL
HORIZONTAL

SLIDER
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337
total-num-seeds
total-num-seeds
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number-of-nodes
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1
1
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SLIDER
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rand-seed-network
rand-seed-network
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BUTTON
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setup
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T
OBSERVER
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go
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MONITOR
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total-unique-active-count
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MONITOR
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PLOT
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per-behavior-adoption
time
active-count
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10.0
0.0
10.0
true
true
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PENS

PLOT
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813
454
total-adoption
time
active-counts
0.0
10.0
0.0
10.0
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true
"" ""
PENS
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PLOT
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utilization
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INPUTBOX
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INPUTBOX
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behavior-utilities
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TEXTBOX
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TEXTBOX
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TEXTBOX
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TEXTBOX
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TEXTBOX
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Diffusion Model
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SWITCH
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switching-cost?
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SWITCH
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-1000

SLIDER
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benefit-of-inertia
benefit-of-inertia
0
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0.2
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HORIZONTAL

CHOOSER
22
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206
445
seed-selection-algorithm
seed-selection-algorithm
"randomly-unlimited-seed-resource-batched" "randomly-unlimited-seed-resource-incremental" "randomly-with-available-resource-batched" "randomly-with-available-resource-incremental" "randomly-with-knapsack-assignment" "randomly-with-random-tie-breaking" "naive-degree-ranked-with-knapsack-assignment" "naive-degree-ranked-with-random-tie-breaking-no-nudging" "naive-degree-ranked-with-random-tie-breaking-with-nudging" "degree-and-resource-ranked-with-knapsack-tie-breaking" "degree-and-resource-ranked-with-random-tie-breaking" "one-step-spread-ranked-with-random-tie-breaking" "one-step-spread-hill-climbing-with-random-tie-breaking" "ideal-all-agent-adoption-without-network-effect"
12

SLIDER
19
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rand-seed-resource
rand-seed-resource
0
10000
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1
NIL
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SLIDER
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rand-seed-threshold
rand-seed-threshold
0
10000
5997
1
1
NIL
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CHOOSER
22
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seed-distribution
seed-distribution
"uniform" "proportional to cost" "inversely proportional to cost" "highest cost behavior only" "lowest cost behavior only" "in ratio"
2

INPUTBOX
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final-ratio
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1
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String

@#$#@#$#@
## WHAT IS IT?

This a model of social behavior diffusion with multiple behaviors diffusing in the social network simultaneously. Each behavior has an associated cost and utility. Each agent has a finite resource for adoption of different behaviors. Agents further derive local network utility based on how many of its neighbors has adopted the behavior. Adoption decision is based on a social influence based triggering mechanism controlled by an agent spefic random threshold, and utility maximization mechanism at each individual agent level.

## HOW IT WORKS

At each time step each agent looks around its neighborhood and watches what behaviors are adopted by its neighbors and the influence weight of how many of them crosses its threshold. Then it calculates the total utility of each of the behaviors for which the total influence weight crosses the threhsold and computes the set of the behaviors for which the total utility is maximized subject to the resource constraint.

## HOW TO USE IT

Set the network parameters for network generation. For example set number-of-nodes to 500, and average-node-degree to 10. Specify the number of behaviors and the associated cost. For example if num-behaviors is set to zero, then specify behavior-costs as - "[0.2 0.3 0.4]" (without the quotes) and the behavior-utilities as - "[0.2 0.3 0.4]" (again without the quotes). Set a value for number-of-seeds-per-behavior. Then press setup button to initialize the model and go to run it.

## THINGS TO NOTICE

(suggested things for the user to notice while running the model)

## THINGS TO TRY

(suggested things for the user to try to do (move sliders, switches, etc.) with the model)

## EXTENDING THE MODEL

(suggested things to add or change in the Code tab to make the model more complicated, detailed, accurate, etc.)

## NETLOGO FEATURES

(interesting or unusual features of NetLogo that the model uses, particularly in the Code tab; or where workarounds were needed for missing features)

## RELATED MODELS

(models in the NetLogo Models Library and elsewhere which are of related interest)

## CREDITS AND REFERENCES

(a reference to the model's URL on the web if it has one, as well as any other necessary credits, citations, and links)
@#$#@#$#@
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<experiments>
  <experiment name="seed-selection-heuristic-comparison-extra-switching-cost-threshold-average" repetitions="1" runMetricsEveryStep="false">
    <setup>setup</setup>
    <go>go</go>
    <timeLimit steps="100"/>
    <metric>utilization</metric>
    <metric>total-active-count</metric>
    <metric>total-unique-active-count</metric>
    <metric>array:item active-counts 0</metric>
    <metric>array:item active-counts 1</metric>
    <metric>array:item active-counts 2</metric>
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    <enumeratedValueSet variable="total-num-seeds">
      <value value="51"/>
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    <enumeratedValueSet variable="seed-selection-algorithm">
      <value value="&quot;naive-degree-ranked-with-knapsack-assignment&quot;"/>
      <value value="&quot;naive-degree-ranked-with-random-tie-breaking-no-nudging&quot;"/>
      <value value="&quot;naive-degree-ranked-with-random-tie-breaking-with-nudging&quot;"/>
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    <steppedValueSet variable="rand-seed-threshold" first="1000" step="1" last="5999"/>
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    <enumeratedValueSet variable="benefit-of-inertia">
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    <enumeratedValueSet variable="behavior-utilities">
      <value value="&quot;[0.2 0.5 0.7]&quot;"/>
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    <enumeratedValueSet variable="num-behaviors">
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    <enumeratedValueSet variable="seed-distribution">
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  <experiment name="seed-distribution-switching-cost-threshold-average" repetitions="1" runMetricsEveryStep="false">
    <setup>setup</setup>
    <go>go</go>
    <timeLimit steps="100"/>
    <metric>utilization</metric>
    <metric>total-active-count</metric>
    <metric>total-unique-active-count</metric>
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      <value value="&quot;highest cost behavior only&quot;"/>
      <value value="&quot;lowest cost behavior only&quot;"/>
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  </experiment>
  <experiment name="seed-distribution-switching-cost-network-average" repetitions="1" runMetricsEveryStep="false">
    <setup>setup</setup>
    <go>go</go>
    <timeLimit steps="100"/>
    <metric>utilization</metric>
    <metric>total-active-count</metric>
    <metric>total-unique-active-count</metric>
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  <experiment name="max-utilization-threshold-average" repetitions="1" runMetricsEveryStep="false">
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    <go>go</go>
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    <metric>total-active-count</metric>
    <metric>total-unique-active-count</metric>
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    <setup>setup</setup>
    <go>go</go>
    <timeLimit steps="100"/>
    <metric>utilization</metric>
    <metric>total-active-count</metric>
    <metric>total-unique-active-count</metric>
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  </experiment>
  <experiment name="num-seed-vs-utilization-threshold-average" repetitions="1" runMetricsEveryStep="false">
    <setup>setup</setup>
    <go>go</go>
    <timeLimit steps="100"/>
    <metric>utilization</metric>
    <metric>total-active-count</metric>
    <metric>total-unique-active-count</metric>
    <metric>array:item active-counts 0</metric>
    <metric>array:item active-counts 1</metric>
    <metric>array:item active-counts 2</metric>
    <metric>array:item num-seeds-per-behavior 0</metric>
    <metric>array:item num-seeds-per-behavior 1</metric>
    <metric>array:item num-seeds-per-behavior 2</metric>
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      <value value="200"/>
      <value value="300"/>
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    <go>go</go>
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    <metric>total-unique-active-count</metric>
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      <value value="&quot;[0.2 0.5 0.7]&quot;"/>
    </enumeratedValueSet>
    <steppedValueSet variable="rand-seed-threshold" first="1000" step="1" last="5999"/>
    <enumeratedValueSet variable="number-of-nodes">
      <value value="500"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="switching-cost?">
      <value value="true"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="matched-threshold?">
      <value value="false"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="seed-selection-algorithm">
      <value value="&quot;one-step-spread-hill-climbing-with-random-tie-breaking&quot;"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="total-num-seeds">
      <value value="51"/>
    </enumeratedValueSet>
  </experiment>
</experiments>
@#$#@#$#@
@#$#@#$#@
default
0.0
-0.2 0 1.0 0.0
0.0 1 1.0 0.0
0.2 0 1.0 0.0
link direction
true
0
Line -7500403 true 150 150 90 180
Line -7500403 true 150 150 210 180

@#$#@#$#@
0
@#$#@#$#@
