breed [symptoms symptom]

symptoms-own
[ 
  symptom-present?    ;; if true, the symptom is a present symptom
  b                   ;; the threshold of regression 
  a                   ;; the steepness of the probability function  
  activation          ;; =0 if symptom-absent, =1 if symptom-present
  total-activation    ;; total activation from network
  chance-to-become-activated ;; calculates the likelihood with logistic function to become active
  individual-stress
  Act
]

links-own 
[
  weight-edge
]

globals 
[
  mood
  shock
]


;;;;;;;;;;;;;;;;;;;;;;;;
;;; Setup Procedures ;;;
;;;;;;;;;;;;;;;;;;;;;;;;

to setup
  clear-all
  setup-symptoms
  ask symptom 0 [set b 0.8473446 set a 2.191685 set label "Depmood"] 
  ask symptom 1 [set b 1.1534109 set a 1.908611 set label "Lossint"]
  ask symptom 2 [set b 1.7241530 set a 1.803834 set label "Wloss"]
  ask symptom 3 [set b 2.1434912 set a 1.423235 set label "Wgain"]
  ask symptom 4 [set b 1.7898552 set a 1.489656 set label "Dapp"]
  ask symptom 5 [set b 1.7924182 set a 1.692587 set label "Iapp"]
  ask symptom 6 [set b 2.0968795 set a 1.219107 set label "Insom"]
  ask symptom 7 [set b 1.7218456 set a 1.856848 set label "Hypersom"]
  ask symptom 8 [set b 1.4080463 set a 1.712685 set label "Pagit"]
  ask symptom 9 [set b 2.0744478 set a 1.532739 set label "Pretar"]
  ask symptom 10 [set b 1.6176604 set a 1.519489 set label "Fatigue"]
  ask symptom 11 [set b 2.4087651 set a 1.449846 set label "Worthless"]
  ask symptom 12 [set b 2.4090998 set a 1.385965 set label "Conc"]
  ask symptom 13 [set b 1.8460433 set a 2.006484 set label "Death"]
  setup-network
  set mood []
  reset-ticks
end

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Setup symptoms: creates symptoms with a nice layout    ;; 
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
to setup-symptoms
  create-symptoms 14 [ set shape "circle"]
  layout-circle (sort symptoms) max-pxcor - 1
  ask symptoms 
    [
      setxy (xcor * 0.70) (ycor * 0.70)
      become-symptom-absent
    ]
end

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Setup network: creates fully connected network, assigns  ;; 
;; weights to the links                                     ;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
to setup-network
ask symptom 0  [create-link-with symptom 1]
ask symptom 0  [create-link-with symptom 6]
ask symptom 0  [create-link-with symptom 10]
ask symptom 0  [create-link-with symptom 11]
ask symptom 1  [create-link-with symptom 11]
ask symptom 1  [create-link-with symptom 12]
ask symptom 2  [create-link-with symptom 4]
ask symptom 3  [create-link-with symptom 5]
ask symptom 4  [create-link-with symptom 6]
ask symptom 6  [create-link-with symptom 8]
ask symptom 6  [create-link-with symptom 10]
ask symptom 7  [create-link-with symptom 10]
ask symptom 8  [create-link-with symptom 12]
ask symptom 9  [create-link-with symptom 10]
ask symptom 9  [create-link-with symptom 12]
ask symptom 11 [create-link-with symptom 12]
ask symptom 11 [create-link-with symptom 13]

  ask link 0 1   [set weight-edge 1 set color 1]
  ask link 0 6   [set weight-edge 1 set color 1]
  ask link 0 10  [set weight-edge 1 set color 1]
  ask link 0 11  [set weight-edge 1 set color 1]
  ask link 1 11  [set weight-edge 1 set color 1]
  ask link 1 12  [set weight-edge 1 set color 1]
  ask link 2 4   [set weight-edge 1 set color 1]
  ask link 3 5   [set weight-edge 1 set color 1]
  ask link 4 6   [set weight-edge 1 set color 1]
  ask link 6 8   [set weight-edge 1 set color 1]
  ask link 6 10  [set weight-edge 1 set color 1]
  ask link 7 10  [set weight-edge 1 set color 1]
  ask link 8 12  [set weight-edge 1 set color 1]
  ask link 9 10  [set weight-edge 1 set color 1]
  ask link 9 12  [set weight-edge 1 set color 1]
  ask link 11 12 [set weight-edge 1 set color 1]
  ask link 11 13 [set weight-edge 1 set color 1]
  ask links
  [
    if weight-edge = 1
    [
    set color 3
    ]
  ]
end  

to go  
  spread-activation
  list-mood
  tick
  update-plot 
  set shock 1
  make-histogram
end

to administer-shock
  set shock 0
;  ask links
;  [
;    set weight-edge 0
;  ]
end



;;;;;;;;;;;;;;;;;;;;;;;
;;; Main Procedures ;;;
;;;;;;;;;;;;;;;;;;;;;;;


;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Spread activation: The likelihood is calculated and drawing a random number between a uniform distribution of 0 and 1 ;;
;; determines whether the symptom will actually be activated or deactivated                                              ;; 
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to spread-activation
  ask symptoms with [symptom-present?]
    [
      calculate-chance-to-become-activated
      if random 1000 / 1000 >  chance-to-become-activated
        [
          become-symptom-absent
        ]
    ] 
  ask symptoms with [not symptom-present?] 
    [
      calculate-chance-to-become-activated
      if random 1000 / 1000 < chance-to-become-activated
        [
          become-symptom-present
        ]
    ]
end

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Calculates the likelihood with a logistic function                ;;
;; Makes a list with the actual activation values of the symptoms    ;;
;; Makes a matrix from list-activation-all to perform matrix algebra ;;
;; and calculates the chance to become activated in a random order   ;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
to calculate-chance-to-become-activated
  ind-stress
  ask symptoms
  [
    set total-activation 0 
    let j 0
    while [j < count symptoms]
    [
      if link who j != nobody
      [
        if who < j
        [
          let temptot-activation ([weight-edge] of link who j * [activation] of symptom j * connection-strength) 
          set total-activation total-activation + temptot-activation
        ]
        if who > j
        [
          let temptot-activation ([weight-edge] of link j who * [activation] of symptom j * connection-strength) 
          set total-activation total-activation + temptot-activation
        ]
      ]
      set j j + 1
      ]
    set Act total-activation + external-activation + individual-stress
    set chance-to-become-activated (1 / (1 + exp (a * (b - Act)))) * shock
  ]
end

to ind-stress
ask symptom 0  [set individual-stress Depmood]
ask symptom 1  [set individual-stress Lossint]
ask symptom 2  [set individual-stress Wloss]
ask symptom 3  [set individual-stress Wgain]
ask symptom 4  [set individual-stress Dapp]
ask symptom 5  [set individual-stress Iapp]
ask symptom 6  [set individual-stress Insom]
ask symptom 7  [set individual-stress Hypersom]
ask symptom 8  [set individual-stress Pagit]
ask symptom 9  [set individual-stress Pretar]
ask symptom 10 [set individual-stress Fatigue]
ask symptom 11 [set individual-stress Worthless]
ask symptom 12 [set individual-stress Conc]
ask symptom 13 [set individual-stress Death]
end


;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; procedures to activate and deactivate symptoms ;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
to become-symptom-absent
  set symptom-present? false
  set activation 0
  set shape "circle 2"
  set size 1
end

to become-symptom-present 
  set symptom-present? true
  set activation 1  
  set shape "circle"
  set color red
  set size 2
end

to list-mood
  set mood lput (count symptoms with [symptom-present?]) mood
  if length mood > 1000
  [
    set mood butfirst mood
  ]
end

;;;;;;;;;;;;;;;;;;;;;;;
;;;     Plots       ;;;
;;;;;;;;;;;;;;;;;;;;;;;  
to update-plot
  set-current-plot "Network Status"
       set-current-plot-pen "cut-off"
  ;; we don't want the "auto-plot" feature to cause the
  ;; plot's x range to grow when we draw the axis.  so
  ;; first we turn auto-plot off temporarily
  auto-plot-off
  plotxy 0 7
  plotxy 1000000000 7
  auto-plot-on
  set-current-plot-pen "symptom-present"
  plot (count symptoms with [symptom-present?]) 
  set-current-plot-pen "external-activation"
  plot ((external-activation + 8) / 2) 
  set-current-plot-pen "connection-strength"
  plot (connection-strength * 5) 

  set-current-plot "Hysteresis plot"
  set-current-plot-pen "symptom-present"
  plotxy (external-activation) (count symptoms with [symptom-present?]) 
end

to make-histogram
  set-current-plot "Histogram" 
  set-current-plot-pen "mood"
  histogram  mood
end

; *** NetLogo 4.0.3 Model Copyright Notice ***
;
; Copyright 2008 by Uri Wilensky.  All rights reserved.
;
; Permission to use, modify or redistribute this model is hereby granted,
; provided that both of the following requirements are followed:
; a) this copyright notice is included.
; b) this model will not be redistributed for profit without permission
;    from Uri Wilensky.
; Contact Uri Wilensky for appropriate licenses for redistribution for
; profit.
; *** End of NetLogo 4.0.3 Model Copyright Notice ***
@#$#@#$#@
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Network Status
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"" ""
PENS
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BUTTON
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administer-shock
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OBSERVER
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SLIDER
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SLIDER
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Lossint
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SLIDER
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SLIDER
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HORIZONTAL

SLIDER
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Dapp
Dapp
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HORIZONTAL

SLIDER
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Iapp
Iapp
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SLIDER
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Insom
Insom
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SLIDER
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Hypersom
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Pagit
Pagit
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HORIZONTAL

SLIDER
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Pretar
Pretar
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1
NIL
HORIZONTAL

SLIDER
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Fatigue
Fatigue
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1
NIL
HORIZONTAL

SLIDER
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Worthless
Worthless
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1
1
NIL
HORIZONTAL

SLIDER
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Conc
Conc
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1
1
NIL
HORIZONTAL

SLIDER
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292
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Death
Death
-5
5
0
1
1
NIL
HORIZONTAL

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

This model is a representation of major depression. The nodes in this model represent the symptoms of major depression. According to the DSM-IV (APA, 2000) there are nine symptoms: (1) depressed mood, (2) loss of interest, (3) weight loss, (4) weight gain, (5) decreased appetite, (6) increased appetite, (7) insomnia, (8) hypersomnia, (9) psychomotor agitation, (10) psychomotor retardation, (11) fatigue, (12) worthlessness or guilt, (13) concentration problems, and (14) suicidal thoughts.   
In this model, a recently emerging view on the relations between symptoms is illustrated. It is based on the hypothesis that symptoms of mental disorders have direct causal relations with one another and is called the causal network perspective (Borsboom, 2008; Cramer, Waldorp, van der Maas & Borsboom, 2010; Schmittmann, Cramer, Waldorp, Epskamp, Kievit & Borsboom, 2011; Cramer, Borsboom, Aggen, & Kendler, 2012). For instance, if one develops a symptom of major depression (e.g., insomnia) then this increases the likelihood of developing other symptoms (e.g., fatigue, lack of concentration). Conversely, if one of the symptoms disappears, this increases the likelihood that other symptoms disappear as well. 

It is hypothesized that if someone is vulnerable to depression (i.e., symptoms are strongly connected), mild stress could be enough to trigger a cascade of symptoms that can eventually lead to a full-blown depressive episode (Cramer et al., submitted). This model is made to illustrate this effect of vulnerability. This means that this model predicts that a person that is vulnerable and develops a depression due to, for example, severe marital problems, will not recover automatically when the marital problems are solved. More is needed to trigger recovery from depression. Conversely, for someone that is resilient to depression (i.e., symptoms are weakly connected), mild stress cannot trigger a cascade of symptoms. Severe stress can lead to a full-blown depression, but when the stress subsides, the depression will subside too. This effect is also known as the hysteresis effect.

## HOW IT WORKS 

The model is based on two parameters for the whole network that can be controlled by the sliders: CONNECTION-STRENGTH, EXTERNAL-ACTIVATION.  Furthermore, the stress levels can be varied per symptom with the sliders on the right of the monitor. 
The network architecture is based on partial correlations between symptoms of a dataset on psychiatric and substance use disorders (VATSPUD; Prescott, Aggen & Kendler, 2000; Kendler & Prescott, 2006).
At each time step (tick), the probability of a symptom being developed (turning red), is calculated for each symptom. This probability depends on certain parameters as well as the total activation of its neighbors at the previous tick. These parameters are regression parameters (a threshold and a slope) for each symptom by fitting a logistic regression model on the VATSPUD data in which each symptom was regressed on the total score of its neighbors). The probability to become activated for symptom i is represented as: 

1 / 1 + e^(a(b - A))

Here, A is the total amount of stress on symptom i. The amount of stress consists of the individual stress level of symptom i (the value on the slider of symptom i), the amount of external activation (the amount of stress on the whole network, indicated by the slider EXTERNAL-ACTIVATION) and the influence of the activation of the neighbors of symptom i. The influence of the neighbors depends on whether or not they are activated and on the strength of the connection between the activated neighbor and symptom i. The strength of the connections determines the degree to which the activation signal of a symptom is sent to the other symptoms and is controlled by the CONNECTION-STRENGTH slider. The external activation can be seen as influences from the environment (e.g., stressful life events like a romantic breakup or the loss of a loved one), controlled by the EXTERNAL-ACTIVATION slider.
Parameter a is a symptom-specific parameter that controls the steepness of the probability function. When a is high, a change in A results larger change in the probability of becoming infected. 
Parameter b is a symptom-specific parameter for the threshold of a symptom; a symptom with a higher threshold needs more activation (from its neighbors for example) to become infected than symptoms with a lower threshold.

## HOW TO USE IT 

Use the sliders CONNECTION-STRENGTH and EXTERNAL-ACTIVATION to choose the initial settings for the model. Press SETUP to create the network. To run the model, press the GO button. If you want to start a new simulation press SETUP again. The CONNECTION-STRENGTH and EXTERNAL-ACTIVATION sliders can be adjusted before pressing GO, or while the model is running.   
The NETWORK STATUS plot shows the number of activated symptoms over time (per tick). As the EXTERNAL-ACTIVATION slider is adjusted, the green line moves up or down accordingly. The same goes for the CONNECTION_STRENGTH slider and the grey line.
In the HYSTERESIS PLOT, the hysteresis effect can be demonstrated. At a certain fixed connection-strength and changing external activation, it will be made visible that the shifts from depressed to healthy states and vice versa generally follow a non-linear pattern (hysteresis).
The HISTOGRAM represents the frequency of the number of activated symptoms per tick of the last 1000 ticks.

## THINGS TO NOTICE 

The network can be regarded as disordered when the total number of active symptoms is larger than 7. In the NETWORK STATUS plot this is when the network is above the black line. Conversely, the network is regarded healthy when there are 7 or less symptoms activated (below the black line). 
	
## THINGS TO TRY 

Press SETUP and then GO. When you let the network run for a while, notice that, with the initial settings (CONNECTION-STRENGTH 1.20, EXTERNAL-ACTIVATION 0.0 and stress levels of all symptoms 0), the activation of the network is around the black line in the NETWORK STATUS plot. Change the EXTERNAL-ACTIVATION and see how that affects the activity of the network. Then increase the CONNECTION-STRENGTH and see if the influence of the EXTERNAL-ACTIVATION is altered.

To demonstrate a hysteresis effect, choose a CONNECTION-STRENGTH. Now, increase the EXTERNAL-ACTIVATION slowly at a constant pace. At what level of external activation does the network switch to a depressed state? And when you decrease the external activation at the same pace: at what level does the network switch into a healthy state? Go back and forth with the EXTERNAL-ACTIVATION slider a couple of times. Can you find a way to make the hysteresis effect bigger?

With the ADMINISTER-SHOCK button, you can deactivate all symptoms at once. It is as if you give the network an electric shock that resets all the symptoms. Try to find a setting of the CONNECTION-STRENGTH and EXTERNAL-ACTIVATION that creates a disordered network (above the black line in the NETWORK STATUS plot) whereby administering a shock, makes the system healthy again.

With the stress sliders per symptom, you can intervene on a specific symptom by ‘curing’ that symptom (by lowering the stress level). Can you make a disordered network and make it healthy again by focusing your treatment on a few symptoms? What symptoms are most effective to intervene on?

## EXTENDING THE MODEL 

The model could be extended by incorporating the kindling effect (Kraepelin, 1921). The kindling effect is the phenomenon that stressful life events play the greatest role in the first onset of Major Depression. Subsequent episodes are elicited by less and less severe life events (Monroe, Torres, Guillaumot, Harkness, Roberts, Frank & Kupfer, 2006). This could be incorporated in the model by making the connections between the symptoms stronger after every depressive episode. So, each time the network status goes above the black line (or after a certain amount of times in a certain period), the connections become a bit stronger.

## NETLOGO FEATURES 


## CREDITS AND REFERENCES 
APA (2000). Diagnostic and Statistical Manual of Mental Disorders, 4th edition, text revision. American Psychiatric Association: Washington, DC.

Borsboom, D. (2008). Psychometric perspectives on diagnostic systems. Journal of Clinical Psychology, 64, 1089-1108.

Cramer, A. O. J., Waldorp, L. J., Van der Maas, H. L. J., and Borsboom, D. (2010). Comorbidity: A network perspective. Behavioral and Brain Sciences, 33, 137-193.

Cramer, A. O. J., Giltay, E. J., Van Borkulo, C. D., Van der Maas, H. J., Kendler, K. S., Scheffer, M., and Borsboom, D. (submitted). I feel sad therefore I do not sleep: Major depression as a complex system. 

Kendler, K. S., & Prescott, C. A. (2006). Genes, environment, and psychopathology: Understanding the causes of psychiatric and substance use disorders. Guilford Press.

Kraepelin E: Manic-depressive insanity and paranoia. Edinburgh, Scotland: E. & S. Livingstone, 1921

Monroe SM, Torres LD, Guillaumot J, Harkness KL, Roberts JE, Frank E, Kupfer D: Life stress and the long-term treatment course of recurrent depression: III. nonsevere life events predict recurrence for medicated patients over 3 years. J Consult Clin Psychol 2006; 74:112-120

Prescott CA, Aggen SH, Kendler KS: Sex-specific genetic influences on the comorbidity of alcoholism and major depression in a population-based sample of US twins. Arch Gen Psychiatry 2000; 57:803-811

Prescott, C. A., Aggen, S. H., & Kendler, K. S. (2000). Sex-specific genetic influences on the comorbidity of alcoholism and major depression in a population-based sample of US twins. Archives of General Psychiatry, 57, 803-811.

Schmittman, V. D., Cramer, A. O. J., Waldorp, L. J., Epskamp, S., Kievit, R. A., and Borsboom, D. Deconstructing the construct: A network perspective on psychological phenomena. New Ideas in Psychology (2011), doi:10.1016/j.newideapsych.2011.02.007

## HOW TO CITE

If you mention this model in an academic publication, we ask that you include these citations for the model itself and for the NetLogo software:   
- Van Borkulo, C.D., Van der Maas, H.L.J., Borsboom, D., and Cramer, A.O.J. (2013). NetLogo Vulnerability_to_Depression.  
http://ccl.northwestern.edu/netlogo/models/community/Vulnerability_to_Depression. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.   
- Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. 

In other publications, please use:   
- Copyright 2013 Claudia D. van Borkulo, Han L. J. van der Maas, Denny Borsboom, and Angelique O. J. Cramer. All rights reserved. See http://ccl.northwestern.edu/netlogo/models/community/Vulnerability_to_Depression for terms of use.
@#$#@#$#@
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Circle -16777216 true false 180 75 60
Polygon -16777216 true false 150 168 90 184 62 210 47 232 67 244 90 220 109 205 150 198 192 205 210 220 227 242 251 229 236 206 212 183

fish
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0
Polygon -1 true false 44 131 21 87 15 86 0 120 15 150 0 180 13 214 20 212 45 166
Polygon -1 true false 135 195 119 235 95 218 76 210 46 204 60 165
Polygon -1 true false 75 45 83 77 71 103 86 114 166 78 135 60
Polygon -7500403 true true 30 136 151 77 226 81 280 119 292 146 292 160 287 170 270 195 195 210 151 212 30 166
Circle -16777216 true false 215 106 30

flag
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0
Rectangle -7500403 true true 60 15 75 300
Polygon -7500403 true true 90 150 270 90 90 30
Line -7500403 true 75 135 90 135
Line -7500403 true 75 45 90 45

flower
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0
Polygon -10899396 true false 135 120 165 165 180 210 180 240 150 300 165 300 195 240 195 195 165 135
Circle -7500403 true true 85 132 38
Circle -7500403 true true 130 147 38
Circle -7500403 true true 192 85 38
Circle -7500403 true true 85 40 38
Circle -7500403 true true 177 40 38
Circle -7500403 true true 177 132 38
Circle -7500403 true true 70 85 38
Circle -7500403 true true 130 25 38
Circle -7500403 true true 96 51 108
Circle -16777216 true false 113 68 74
Polygon -10899396 true false 189 233 219 188 249 173 279 188 234 218
Polygon -10899396 true false 180 255 150 210 105 210 75 240 135 240

house
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0
Rectangle -7500403 true true 45 120 255 285
Rectangle -16777216 true false 120 210 180 285
Polygon -7500403 true true 15 120 150 15 285 120
Line -16777216 false 30 120 270 120

leaf
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0
Polygon -7500403 true true 150 210 135 195 120 210 60 210 30 195 60 180 60 165 15 135 30 120 15 105 40 104 45 90 60 90 90 105 105 120 120 120 105 60 120 60 135 30 150 15 165 30 180 60 195 60 180 120 195 120 210 105 240 90 255 90 263 104 285 105 270 120 285 135 240 165 240 180 270 195 240 210 180 210 165 195
Polygon -7500403 true true 135 195 135 240 120 255 105 255 105 285 135 285 165 240 165 195

line
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0
Line -7500403 true 150 0 150 300

line half
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0
Line -7500403 true 150 0 150 150

pentagon
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0
Polygon -7500403 true true 150 15 15 120 60 285 240 285 285 120

person
false
0
Circle -7500403 true true 110 5 80
Polygon -7500403 true true 105 90 120 195 90 285 105 300 135 300 150 225 165 300 195 300 210 285 180 195 195 90
Rectangle -7500403 true true 127 79 172 94
Polygon -7500403 true true 195 90 240 150 225 180 165 105
Polygon -7500403 true true 105 90 60 150 75 180 135 105

plant
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0
Rectangle -7500403 true true 135 90 165 300
Polygon -7500403 true true 135 255 90 210 45 195 75 255 135 285
Polygon -7500403 true true 165 255 210 210 255 195 225 255 165 285
Polygon -7500403 true true 135 180 90 135 45 120 75 180 135 210
Polygon -7500403 true true 165 180 165 210 225 180 255 120 210 135
Polygon -7500403 true true 135 105 90 60 45 45 75 105 135 135
Polygon -7500403 true true 165 105 165 135 225 105 255 45 210 60
Polygon -7500403 true true 135 90 120 45 150 15 180 45 165 90

sheep
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0
Rectangle -7500403 true true 151 225 180 285
Rectangle -7500403 true true 47 225 75 285
Rectangle -7500403 true true 15 75 210 225
Circle -7500403 true true 135 75 150
Circle -16777216 true false 165 76 116

square
false
0
Rectangle -7500403 true true 30 30 270 270

square 2
false
0
Rectangle -7500403 true true 30 30 270 270
Rectangle -16777216 true false 60 60 240 240

star
false
0
Polygon -7500403 true true 151 1 185 108 298 108 207 175 242 282 151 216 59 282 94 175 3 108 116 108

symptom1
true
1

symptom2
true
1

symptom3
true
1

target
false
0
Circle -7500403 true true 0 0 300
Circle -16777216 true false 30 30 240
Circle -7500403 true true 60 60 180
Circle -16777216 true false 90 90 120
Circle -7500403 true true 120 120 60

tree
false
0
Circle -7500403 true true 118 3 94
Rectangle -6459832 true false 120 195 180 300
Circle -7500403 true true 65 21 108
Circle -7500403 true true 116 41 127
Circle -7500403 true true 45 90 120
Circle -7500403 true true 104 74 152

triangle
false
0
Polygon -7500403 true true 150 30 15 255 285 255

triangle 2
false
0
Polygon -7500403 true true 150 30 15 255 285 255
Polygon -16777216 true false 151 99 225 223 75 224

truck
false
0
Rectangle -7500403 true true 4 45 195 187
Polygon -7500403 true true 296 193 296 150 259 134 244 104 208 104 207 194
Rectangle -1 true false 195 60 195 105
Polygon -16777216 true false 238 112 252 141 219 141 218 112
Circle -16777216 true false 234 174 42
Rectangle -7500403 true true 181 185 214 194
Circle -16777216 true false 144 174 42
Circle -16777216 true false 24 174 42
Circle -7500403 false true 24 174 42
Circle -7500403 false true 144 174 42
Circle -7500403 false true 234 174 42

turtle
true
0
Polygon -10899396 true false 215 204 240 233 246 254 228 266 215 252 193 210
Polygon -10899396 true false 195 90 225 75 245 75 260 89 269 108 261 124 240 105 225 105 210 105
Polygon -10899396 true false 105 90 75 75 55 75 40 89 31 108 39 124 60 105 75 105 90 105
Polygon -10899396 true false 132 85 134 64 107 51 108 17 150 2 192 18 192 52 169 65 172 87
Polygon -10899396 true false 85 204 60 233 54 254 72 266 85 252 107 210
Polygon -7500403 true true 119 75 179 75 209 101 224 135 220 225 175 261 128 261 81 224 74 135 88 99

wheel
false
0
Circle -7500403 true true 3 3 294
Circle -16777216 true false 30 30 240
Line -7500403 true 150 285 150 15
Line -7500403 true 15 150 285 150
Circle -7500403 true true 120 120 60
Line -7500403 true 216 40 79 269
Line -7500403 true 40 84 269 221
Line -7500403 true 40 216 269 79
Line -7500403 true 84 40 221 269

x
false
0
Polygon -7500403 true true 270 75 225 30 30 225 75 270
Polygon -7500403 true true 30 75 75 30 270 225 225 270

@#$#@#$#@
NetLogo 5.0.4
@#$#@#$#@
@#$#@#$#@
@#$#@#$#@
@#$#@#$#@
@#$#@#$#@
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

link
0.0
-0.2 0 0.0 1.0
0.0 1 1.0 0.0
0.2 0 0.0 1.0
link direction
true
0
Line -7500403 true 150 150 90 180
Line -7500403 true 150 150 210 180

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