globals [
  AP
]

patches-own [
  p-receive-comm             ; probability that communication will be received
  gamma                      ; sensitivity to communication 
  lambda                     ; absolute risk aversion
  nu                         ; preference for operational excellence
  S                          ; satisfaction about operational excellence
  U                          ; current utility
  S1                         ; expected satisfaction in case of changed behavior
  U1                         ; expected utility in case of changed behavior
  p-action-is-effective      ; subjective probability of action efficacy
  network                    ; set of other farms in the farm's social network 
  network-degree             ; number of farms in the network (cardinality of network)
  action-in-network          ; fraction of food safety actors in the farmer's network
  delivery-size              ; delivery size per tick
  attitude                   ; attitude toward food safety action
  norm                       ; believed social norm
  omega                      ; opennes to observed behavior of others
  PBC                        ; perceived behavioral control
  I                          ; intention toward food safety action
  wattitude                  ; weight of attitude in intention formation
  wnorms                     ; weight of norms in intention formation
  wPBC                       ; weight of PBC in intention formation
  A                          ; food sefety action; A=1: act; A=-1: don't act
  At-1                       ; action in previous period
  alpha5
  prev-X                     ; normal random variable for prevalence generation
  prev-X-mu
  prev-X-sigma
  prev-X-1                   ; prev-X value previous period
  prev                       ; actual prevalence at time t
  prevt-1                    ; previous prevalence (at t - 1)
  prevt-2                    ; prevalence at t - 2
  prev1                      ; expected prevalence by changed behaviour
  MA                         ; moving average of actual prevalence
  pen                        ; actual penalty
  pen1                       ; expected penalty by changed behaviour
  pen2                       ; expected penalty in case of action
  saving                     ; actual saving realised
  B-prev-reduction           ; producer's belief about prevalence reduction
  B-exp-saving                 ; producer's belief about additional saving
  cost                       ; cost of food safety actions taken
  B-additional-cost             ; believed additional cost
]

to set-up
  clear-all
  ask patches [
    ;;set background color
    set pcolor gray
    ;; cache patch networks and their numbers
    set network (patches in-radius network-radius)
    set network-degree (count network - 1)  ;; exclude self
    ;; generate producer characteristics
    set lambda (min-risk-aversion + random-float (max-risk-aversion - min-risk-aversion))
    set gamma (min-com-openness + random-float (max-com-openness - min-com-openness))
    set nu (min-OE-preference + random-float (max-OE-preference - min-OE-preference))
    set omega (min-netw-openness + random-float (max-netw-openness - min-netw-openness))
    set wattitude TPB-w-attitude
    set wnorms TPB-w-norms
    set wPBC TPB-w-PBC
    set B-prev-reduction 0.0
    set B-exp-saving 0.0
    set B-additional-cost 10.0
    set p-action-is-effective 0.0
    set attitude 0
    set norm 0
    set pbc 0
    set A -1
    set prev-X-sigma sqrt( ln(1.0 + prev-cv ^ 2))
    set prev-X-mu (ln(prev-E)) - 0.5 * ln(1.0 + prev-cv ^ 2)
    set prev-X random-normal prev-X-mu prev-X-sigma
    set prevt-1 exp(prev-X)
    set prev-X-sigma sqrt( ( (1.0 + prev-p) / (1.0 - prev-p) ) * ln(1.0 + prev-cv ^ 2))
    set prev-X-1 prev-X
    set prev-X prev-p * prev-X-1 + (1.0 - prev-p) * (random-normal prev-X-mu prev-X-sigma)
    set prev exp(prev-X)
  ]
  set AP mean [prev] of patches
  ask patches [ deliver ]
  update-plots
end

to go
  if (ticks = 0)
  [set-up]
  tick
  ask patches [  
    process-information
    observe-network
    decide-about-fsa
    deliver
  ]
  set AP mean [prev] of patches
  update-plots
end

to process-information
  ifelse ((random-float 1) < p-receive-com)
  [     set p-action-is-effective p-action-is-effective + (1.0 - p-action-is-effective ) * gamma]
  ;; else decay information
  [     set p-action-is-effective 0.95 * p-action-is-effective]
  set B-prev-reduction p-action-is-effective * prev-reduction
  set B-exp-saving exp-saving 
  set B-additional-cost additional-cost
end

to observe-network
  ;; see how many farms in network acted for food safety
  let total-action-in-network sum [(A + 1.0) / 2.0] of network
  ;; compute fraction ; exclude self
  set action-in-network ((total-action-in-network - (A + 1.0) / 2.0) / network-degree)
  ;; update belief about social norm
  set norm ((1 - omega) * norm + omega * action-in-network)
end

to decide-about-fsa
      set At-1 A
      ;; compute expected satisfaction and utility in case of no change
      set S nu * (AP - prev) / AP
      ifelse (MA > alpha1) [
        set pen1 alpha2 + alpha3 * pen
        if (pen1 > alpha4) [set pen1 alpha4]
      ]
      ;else 
      [ set pen1 0 ]
      set U (1 - exp (- lambda * (saving - cost - pen1)))
      
      ;; compute expected prevalence in case of change
      ifelse (A = 1) ; if action has been taken in the previous period
        [ set prev1 MA / (1.0 - B-prev-reduction) ] ; prevalence increase is expected
        ; else change implies action 
        [ set prev1 MA * (1.0 - B-prev-reduction) ] ; prevalence decrease is expected
          
      ;; compute expected satisfaction and utility in case of change
      set S nu * (AP - prev) / AP
      set U (1 - exp (- lambda * (saving - cost - pen)))
      set S1 nu * (AP - prev1) / AP      
      ifelse (prev1 > alpha1) [
        set pen2 alpha2 + alpha3 * pen
        if (pen2 > alpha4) [set pen2 alpha4]
      ]
      ;else 
      [ set pen2 0 ]
      set U1 (1 - exp (- lambda * ((B-exp-saving * (prev-E - prev1) / prev-E) - (B-additional-cost * (1.0 - A) / 2.0) - pen2)))
      
      ;; determine attitude toward food safety behavior
      set attitude attitude * attitude-p + A * (U - U1 + S - S1) * (1.0 - attitude-p)
      
      ;; compute perceived behavioral control
      set PBC B-exp-saving * B-prev-reduction - B-additional-cost
      
      ;; INTENTION TOWARD FOOD SAFETY ACTION
      set I (TPB-w-attitude * attitude + TPB-w-norms * norm + TPB-w-PBC * PBC)
      
      ;; TAKE FOOD SAFETY ACTION???
      ifelse (I > 0.0) and (PBC >= pbc-threshold)
      [ set A 1.0 ] ; act
      ;; else
      [ set A -1.0 ] ; no action
end
  
to deliver
    set prevt-2 prevt-1
    set prevt-1 prev
    set prev-X-sigma sqrt( ( (1.0 + prev-p) / (1.0 - prev-p) ) * ln(1.0 + prev-cv ^ 2))
    set prev-X-mu (ln(prev-E * (1 - (prev-reduction * (A + 1) / 2)))) - 0.5 * ln(1.0 + prev-cv ^ 2)
    set prev-X-1 prev-X
    set prev-X prev-p * prev-X-1 + (1.0 - prev-p) * (random-normal prev-X-mu prev-X-sigma)
    set prev exp(prev-X)
    ;set MA alpha5 * prev + (1 - alpha5) * (prevt-1 + prevt-2) / 2
    set MA prev ^ alpha5 * prevt-1 ^ (1.0 - alpha5)
    ifelse (MA > alpha1) [
      set pen alpha2 + alpha3 * pen
      if (pen > alpha4) [set pen alpha4]
    ]
    ;else 
    [ set pen 0 ]
    set saving exp-saving * (prev-E - MA) / prev-E
    set cost (additional-cost * (A + 1) / 2)
    paint-patch
end

to paint-patch
  ifelse (A > 0) 
  [ ifelse (MA > alpha1) [set pcolor pink] [set pcolor green]
  ]
  ;;else
  [ ifelse (MA > alpha1) [set pcolor red] [set pcolor gray]
  ]
end

to update-plots
  let action-count count patches with [A > 0]
  let positive-test-count count patches with [MA > alpha1]
  let mean-prev mean [prev] of patches

  set-current-plot "Food safety actions"
  plot action-count

  set-current-plot "# Penalties"
  plot positive-test-count

  set-current-plot "Mean prevalence"
  plot mean-prev
end
@#$#@#$#@
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@#$#@#$#@
WHAT IS IT?
-----------
This section could give a general understanding of what the model is trying to show or explain.


HOW IT WORKS
------------
This section could explain what rules the agents use to create the overall behavior of the model.


HOW TO USE IT
-------------
This section could explain how to use the model, including a description of each of the items in the interface tab.


THINGS TO NOTICE
----------------
This section could give some ideas of things for the user to notice while running the model.


THINGS TO TRY
-------------
This section could give some ideas of things for the user to try to do (move sliders, switches, etc.) with the model.


EXTENDING THE MODEL
-------------------
This section could give some ideas of things to add or change in the procedures tab to make the model more complicated, detailed, accurate, etc.


NETLOGO FEATURES
----------------
This section could point out any especially interesting or unusual features of NetLogo that the model makes use of, particularly in the Procedures tab.  It might also point out places where workarounds were needed because of missing features.


RELATED MODELS
--------------
This section could give the names of models in the NetLogo Models Library or elsewhere which are of related interest.


CREDITS AND REFERENCES
----------------------
This section could contain a reference to the model's URL on the web if it has one, as well as any other necessary credits or references.
@#$#@#$#@
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@#$#@#$#@
NetLogo 4.1.2
@#$#@#$#@
@#$#@#$#@
@#$#@#$#@
@#$#@#$#@
@#$#@#$#@
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