Extraversion.nlogo

globals [
  MAX-BASIC-NEEDS
  MIN-BASIC-NEEDS
  total-resource-level
  avg-high-resources
  avg-med-resources
  avg-low-resources
  high-links
  med-links
  low-links
  avg-high-links
  avg-med-links
  avg-low-links
]

turtles-own [
  age                      ; how old a turtle is
  life-expectancy          ; maximum age that a turtle can reach
  resource-level           ; level of resources on the patch: (min-0 max 80) also the resources a turtle is born with
  extraversion-level       ; Low, Med, High levels of extraction - agents with higher extraversion level have greater chance of exchanging resources
  matches                  ; list of the id of turtles that each turtle has encountered
  matched?                 ; has the turtle already matched
  move-likelihood          ; probability of an agent to move based on resource level
  exchange-likelihood      ; probability of an agent making an exchange of resources based on extraversion-level
  num-links                ; the number of links an individual turtle has
]

patches-own [
  basic-needs-surface      ;amount of resources on this patch
  max-basic-needs-surface  ;maximum amount of resources on this patch
  gain-value               ;amount of resources that moving turtles can gain
]

;Setup Procedures

to setup
  clear-all
  setup-globals
  setup-patches
  setup-turtles
  reset-ticks
end

to setup-globals
  set MAX-BASIC-NEEDS 80   ;the maximum resource level available on the patch
  set MIN-BASIC-NEEDS 0    ;the minimum resource level available on a patch
end


to setup-patches                    ;changes the depth of the patches in a manner that reflects the distribution of resources
  let satisfied-range               ;initialize the resource level on each patch randomly
  MAX-BASIC-NEEDS - MIN-BASIC-NEEDS ;ranges between 0 to 80.
  ask patches [
    set basic-needs-surface
    (MIN-BASIC-NEEDS + random ( satisfied-range ) )
  ]
  ; now smooth the values for a gradient of the distribution
  repeat 2 [
    ask patches [
      set basic-needs-surface mean
      [basic-needs-surface] of neighbors
      ]
    ]
  colorize-patches
  ask patches [set gain-value basic-needs-surface / 10] ;every turtle gains one tenth of resource level the patch
end

to colorize-patches
ask patches [
  set pcolor scale-color
  gray basic-needs-surface         ;darker patches have greater level resources compared to lighter patches
  MAX-BASIC-NEEDS MIN-BASIC-NEEDS
]
end

to setup-turtles ;;turtle procedure
   create-turtles number [ setxy random-xcor random-ycor
   set resource-level random-normal basic-needs-surface 5 ;patches are given a layer of agents with the same level of resource that the patch holds given to the new turtle
   set extraversion-level one-of ["Low" "Med" "High"]
    if extraversion-level = "High" [set color 12 set exchange-likelihood random-normal  Exchange_Likelihood 5]          ;the higher extraverted agents will have an Exchange_Likelihood parameter give or take 5, which is the maximimum possible value
    if extraversion-level = "Med"  [set color 15 set exchange-likelihood random-normal (Exchange_Likelihood * 0.75) 5]  ;the medium range extraverted agents will have an 75% of Exchange_Likelihood parameter give or take 5
    if extraversion-level = "Low"  [set color 18 set exchange-likelihood random-normal (Exchange_Likelihood * 0.5) 5]   ;;the lower range extraverted agents will have an 50% of Exchange_Likelihood parameter give or take 5, which is the minumum possible value
   set age 0
   set life-expectancy random-normal Life_Expectancy 10   ;agents will have a life span of the parameter Life_expectancy give or take 10
   set matches []
   set matched? false
   set num-links 0
   face one-of neighbors4] ;each turtle turns towards neighbouring four patches
end

;;Runtime Procedures

to go
  if ticks >= 40 [stop]
  ask turtles [move]
  find-match
  gain-resources
  kill-turtles
  update-globals
  tick

end

to move              ;consider moving to unoccupied patches towards nearest neighbours
  move-to one-of neighbors
end

to find-match        ;consider matching with another turtle
  ask turtles [ifelse matched? = false and any? turtles in-radius 3 with [matched? = false] [Match] [move]] ;if it has not been matched, it looks around to find turtles not matched in radius 3. If they're all matched, the turtle moves.
  ask turtles [ask my-links [ifelse [resource-level] of other-end <= 50 [die] [set color blue]]] ;
  ask turtles [set num-links count my-links]
end

to Match              ;match with neighbouring turtle
  if any? other turtles in-radius 3 [create-link-to one-of other turtles in-radius 3] ;[move]; with [not matched?]
  set matches lput [who] of link-neighbors matches
  ask link-neighbors [set matched? true]
  exchange-resources
  ask turtles with [matched? = true] [set matched? false]
end

to exchange-resources ;exchange resources with matched turtle
    if random 100 < Exchange_Likelihood [
      if matched? = true [if resource-level > 50 and [resource-level] of link-neighbors < 50 [
        set resource-level resource-level - 5]]
      if matched? = true [if resource-level < 50 and [resource-level] of link-neighbors > 50 [
        set resource-level resource-level + 5]]]
end

to kill-turtles
  if ticks mod 4 = 0 [
    ask turtles [set age age + 1]
  ]
  ask turtles with [age = 100] [hatch 1 set age 0 ask my-links [die]] ;give "birth" to child inheriting resources
  ask turtles with [age > 100] [die]          ;consider dying if age is above maximum
  ask turtles [if resource-level <= 0 [die]]  ;consider dying if resource level is below minimum
end

to gain-resources
  if ticks mod 4 = 0 [
  if random 100 < Gain_Likelihood [ ;the turtle is allowed to gain the resource on the patch
    ask turtles [set resource-level resource-level + [gain-value] of patch-here]]
  ask turtles [if resource-level >= 100 [set resource-level 100]] ;maximum resources that can be accumulated is set at 100
  ]
end

;;Visualization Procedures

to update-globals
  ;average of resource level
  set avg-high-resources mean [resource-level] of turtles with [extraversion-level = "High"]
  set avg-med-resources  mean [resource-level] of turtles with [extraversion-level = "Med"]
  set avg-low-resources  mean [resource-level] of turtles with [extraversion-level = "Low"]

  ;sum total number of links
  set high-links sum [num-links] of turtles with [extraversion-level = "High"]
  set med-links sum [num-links] of turtles with [extraversion-level = "Med"]
  set low-links sum [num-links] of turtles with [extraversion-level = "Low"]

  ;average number of links
  set avg-high-links mean  [num-links] of turtles with  [extraversion-level = "High"]
  set avg-med-links  mean  [num-links] of turtles with  [extraversion-level = "Med"]
  set avg-low-links  mean  [num-links] of turtles with  [extraversion-level = "Low"]
end
@#$#@#$#@
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@#$#@#$#@
## WHAT IS IT?

This is a model simulation of agents of varying levels of extraversion, interacting with each other, and their environment thereby exhibiting emergence.

The gradient indicates the resource level available. The darker the color, the higher the number of resources on the patch.The dark red coloured agents are the agents with high extraversion levels, the red coloured agents are agents with medium extraversion levels, while the pink agents are the agents with low extraversion levels. They pick up the resource level from patches they are initially visible on; they are “born” on, so to speak. On matching, agents exchange resources. O matching more than once, agents form a link. They can also gain resources from patches as they move. They die and their children take their place. 

## HOW IT WORKS
In order to investigate the relation between extraversion and positive incentive motivation, a sample size of agents is simulated against a backdrop of a gradient patch environment. The turtles represent people of three different levels of extraversion – Low extraversion level, Mid extraversion level, and High extraversion level. They pick up the resource level from patches they are initially visible on, as if they were born on that given patch. All the agents can move, exchange resources, and die. 

As the agents move, they gain resources from the patches to simulate real-world behavior of resource distribution. Agents start with the age of zero that increases every 4 ticks, where every tick is indicative of 3 calendar months. They are also assigned a life expectancy and if agent exceed that life expectancy, then the agent dies. The surface itself contains a distribution in the quantity of resources available and each agent is born with the amount of resources corresponding to the patch where they generated. Each step doesn’t cost the agent resources, but agents will die if their resources run out. Resources possessed by agents are restricted to values between 0 and 100, where 100 is the maximum any agent can possess at a given time. 

Furthermore, agents can match with their neighbors, if their neighbors have not matched already. The extraverted agents also exchange resources based on their position as a result of movement, extraversion levels, and inherent resource levels. Depending on the parameter settings, the exchange likelihood of each of the turtles is higher for agents of higher extraversion levels (random-normal  Exchange_Likelihood 5), slightly lesser for agents for medium extraversion (random-normal (Exchange_Likelihood * 0.75) 5), and significantly lesser (random-normal (Exchange_Likelihood * 0.5) 5) for agents with lower range of extraversion. 

For intelligibility, the agents “exchange” resources of the value 5. Based on the number of times the exchange occurs between two specific extraverted agents, a link is formed. This acts as a bond between the agents which acts as a proxy for positive incentive motivation. 

## HOW TO USE IT

The setup button creates a gradient patch environment and individual agents with particular behavioral tendencies for extraversion. The board accepts the values from the sliders (described below). Once the simulation has been setup, you are now ready to run it, by pushing the go button. go starts the simulation and runs it continuously until 40 ticks pass or go is pushed again. During a simulation initiated by go, adjustments in sliders can affect the behavioral tendencies of the population.

In this model each time-step is considered three months.

Here is a summary of the sliders in the model.

- number: The count of people simulation begins with.
- Exchange_Likelihood: The probability of resources being exchanged between the turtles.
- Gain_Likelihood: The probability of turtles accumulating resources as they move across the environment.
- Life_expectancy: The average of normal distribution of the turtles’ life span.

The slider "number" ranges between 0 and 1000 and increments by 1.
The slider "Exchange_likelihood" ranges 0 and 1000 and increments by 1.
The slider "Gain_likelihood" ranges 0 and 1000 and increments by 1.
The slider "Life_expectancy" ranges 0 and 100 and increments by 1.

The model's plot shows the average resource level per extraversaion level, total number of links per extraversaion level, and average number of links per extraversaion level.


## THINGS TO TRY

Run a number of experiments with the GO button to find out the effects of different variables on the spread of HIV.  Try using good controls in your experiment.  Good controls are when only one variable is changed between trials.  For instance, to find out what effect the average duration of a relationship has, run four experiments with the AVERAGE-COMMITMENT slider set at 1 the first time, 2 the second time, 10 the third time, and 50 the last.  How much does the prevalence of HIV increase in each case?  Does this match your expectations?

Are the effects of some slider variables mediated by the effects of others? For example, decreasing Exchange_Likelihood should reduce the number of links formed and vice versa. Increasing Life_Expectancy should increase the duration of the links present in the model and vice versa. Increasing the Gain_Likelihood reduces the number of links, and vice versa.

## EXTENDING THE MODEL

Like all computer simulations of human behaviors, this model has necessarily simplified its subject area substantially. The model therefore provides numerous opportunities for extension:

*Show effects of extraversion by making extravert agents react separately in a society of agents.
*Links give us who exchanges with whom with end1 and end2 function.
*Exchange resources based on the difference of resource level between extravert and agent. 

## NETLOGO FEATURES

Notice that the patches generate many small random numbers as the resource level they hold. Similarly, the agents each gain resources with some small seemingly random numbers. This produces a normal distribution of tendency values. 

Notice the smoothly varying color gradients, indicating variation in resource level.

Notice the Extraversion_level of various extraversion level agents. 

Notice that the links are directed giving the direction of the agent end1 giving the resources to end2 agent.

## CREDITS AND REFERENCES

We greatly thank Drs. Andrew Crooks, Bill Kennedy, and Jim Thompson for their encouragement and helpful comments and Mr. Dwayne Smith for his technical assistance with NetLogo.

* Kornhauser, D., Wilensky, U., & Rand, W. (2009). Design guidelines for agent based model visualization. Journal of Artificial Societies and Social Simulation (JASSS), 12(2), 1. http://ccl.northwestern.edu/papers/2009/Kornhauser,Wilensky&Rand_DesignGuidelinesABMViz.pdf.

* Li J, Wilensky U. NetLogo Sugarscape 3 Wealth Distribution model. Center for Connected Learning und Computer-Based Modeling, Northwestern University, Evanston, IL. http://ccl.northwestern.edu/netlogo/models/Sugarscape3WealthDistribution. 2009.

* Stonedahl, F., Wilensky, U., Rand, W. NetLogo Heroes and Cowards model.
Center for Connected Learning and Computer-Based Modeling, Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL. http://ccl.northwestern.edu/netlogo/models/HeroesandCowards. 2014.

## COPYRIGHT AND LICENSE

Copyright 1997 Uri Wilensky.

![CC BY-NC-SA 3.0](http://ccl.northwestern.edu/images/creativecommons/byncsa.png)

This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License.  To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-sa/3.0/ or send a letter to Creative Commons, 559 Nathan Abbott Way, Stanford, California 94305, USA.

Commercial licenses are also available. To inquire about commercial licenses, please contact Uri Wilensky at uri@northwestern.edu.

This model was created as part of the project: CONNECTED MATHEMATICS: MAKING SENSE OF COMPLEX PHENOMENA THROUGH BUILDING OBJECT-BASED PARALLEL MODELS (OBPML).  The project gratefully acknowledges the support of the National Science Foundation (Applications of Advanced Technologies Program) -- grant numbers RED #9552950 and REC #9632612.

This model was converted to NetLogo as part of the projects: PARTICIPATORY SIMULATIONS: NETWORK-BASED DESIGN FOR SYSTEMS LEARNING IN CLASSROOMS and/or INTEGRATED SIMULATION AND MODELING ENVIRONMENT. The project gratefully acknowledges the support of the National Science Foundation (REPP & ROLE programs) -- grant numbers REC #9814682 and REC-0126227. Converted from StarLogoT to NetLogo, 2001.

<!-- 1997 2001 -->
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@#$#@#$#@
NetLogo 6.1.0
@#$#@#$#@
@#$#@#$#@
@#$#@#$#@
<experiments>
  <experiment name="experiment" repetitions="1" runMetricsEveryStep="true">
    <setup>setup</setup>
    <go>go</go>
    <metric>count turtles</metric>
    <enumeratedValueSet variable="number">
      <value value="100"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="Gain_Likelihood">
      <value value="100"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="Exchange_Likelihood">
      <value value="100"/>
    </enumeratedValueSet>
  </experiment>
  <experiment name="experiment" repetitions="1" runMetricsEveryStep="true">
    <setup>setup</setup>
    <go>go</go>
    <metric>ask turtles with [extraversion-level = "High"] [show count link-neighbors with [extraversion-level = "High"]</metric>
    <metric>ask turtles with [extraversion-level = "High"] [show count link-neighbors with [extraversion-level = "High"]</metric>
    <enumeratedValueSet variable="Life_Expectancy">
      <value value="76"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="number">
      <value value="100"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="Gain_Likelihood">
      <value value="100"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="Exchange_Likelihood">
      <value value="100"/>
    </enumeratedValueSet>
  </experiment>
  <experiment name="FinalExpt" repetitions="10" runMetricsEveryStep="true">
    <setup>setup</setup>
    <go>go</go>
    <metric>sum [num-links] of turtles with  [extroversion-level = "High"]</metric>
    <metric>sum [num-links] of turtles with  [extroversion-level = "Med"]</metric>
    <metric>sum [num-links] of turtles with  [extroversion-level = "Low"]</metric>
    <steppedValueSet variable="number" first="100" step="100" last="500"/>
    <steppedValueSet variable="Gain_Likelihood" first="25" step="25" last="100"/>
    <steppedValueSet variable="Exchange_Likelihood" first="25" step="25" last="100"/>
  </experiment>
</experiments>
@#$#@#$#@
@#$#@#$#@
default
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
@#$#@#$#@
1
@#$#@#$#@