r-andre_abm-social_complexity/into_research-socom_replica.v02.nlogo
;; IR_II-Andre-SS2018-D3.2.v02-TargetModel.RC.nlogo
;; NetLogo version 6.0.4

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;; introduction ;;
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;; author information

;; raffael andre
;; university of vienna, mei:cogsci
;; into research ii project, summer semester 2018
;; project supervisor: dr. paolo petta (ofai)

;; summary

;; this model is a replication of the model described in [1], a social
;;   simulation that tests the circumscription theory using agent-based
;;   modelling. according to this theory, the emergence of states (autonomous
;;   political units of higher social complexity) is a predictable response to
;;   environmental circumscription, which is supposed to entail warfare of
;;   social groups over available land resources [2].
;; for the purpose of this model, social complexity is defined as political
;;   hierarchy, implicitly emerging when social groups subjugate each other
;;   through warfare. the highest level of social complexity is achieved when
;;   one of the initial social groups subjugates all the others in the
;;   simulation. using this model, environmental circumscription can be
;;   simulated by decreasing the amount of land resources (habitable land),
;;   while leaving the number of competing social groups constant.
;; the model was used to test the following hypothesis: an increase of
;;   habitable land in the modelled artificial landscape also increases the time
;;   required for one social group to subjugate all other groups in the
;;   simulation, and for social complexity to emerge.
;; a short summary of the research project, that this model is the result of,
;;   can be found in [3].
;; [1] s. scott, “environmental circumscription and the emergence of social
;;   complexity,” in computational social science society of america (csssa)
;;   annual conference 2011, washington, dc: computational social science
;;   society of america, 2011.
;; [2] r. l. carneiro, “a theory of the origin of the state,” science, new
;;   series, vol. 169, pp. 733-738, 1970.
;; [3] r. andre, "simulating the emergence of social complexity using
;;   agent-based modelling," in proceedings of the mei:cogsci conference 2018,
;;   bratislava: comenius university, p. 64, 2018


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;; version history ;;
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;; version 1 (2018-06-11)

;; implemented all key principles and mechanisms of the original model
;; model runs until only one ethnicity remains in the simulation
;; no errors were observed during test runs
;; groups grow, split, and migrate visibly faster than in the original model
;; primary issue that was observed is that when a group splits,
;;   both groups migrate to neighbours
;;   what should be is that only one group migrates, the other stays
;; on the 'total-ethnicities' monitor, the number occasionally jumps
;;   down to 1 or 0 for seemingly no reason for a split second

;; first test runs
;; (results collected manually)
;; changed only habitability, 10 runs each
;; init-g 10, car-cap 100, re 4%, loss 1%
;; eos = ticks at the end of the simulation (average)
;; (1) hab  5, eos  1000
;; (2) hab 10, eos  2800
;; (3) hab 15, eos  3600
;; (4) hab 20, eos  5500
;; (5) hab 25, eos  8300
;; (6) hab 30, eos 11200
;; these results do not corroborate those of the original model
;;   instead they indicate that environmental circumscription does
;;   have an effect on the time it takes for social complexity to emerge
;; (also, I did not test this extensively, but in the few samples I took
;; 'loss-severity' only had a significant effect on eos when hab was sub 20%)

;; version 2 (2018-07-06)

;; translated model from NetLogo version 4.1.1 to 6.0.4
;;   the only thing to do was setting the interface up again from scratch
;;   plotting total-population also does not work in this version
;;   however since it does not add anythig valuable I simply omitted it
;;   because total-population is not a critical parameter for the simulation
;; refactored comments, added 'whys' to 'whats'
;; impemented a mechanism to account for population losses in warfare
;; filled out the 'Info' tab


;;;;;;;;;;;;;;;;;;;;;;;;;
;; declaring variables ;;
;;;;;;;;;;;;;;;;;;;;;;;;;


;; declaring global variables

globals [
  initial-ethnicities    ;; number of initial ethnicities present at the
                         ;;   beginning of the simulation
  total-ethnicities      ;; count of all ethnicities*
  total-groups           ;; count of all groups*
                         ;; *present in the simulation at the end of
                         ;;   one time-step
;  total-population       ;; sum of all populations, omitted in version 3
                          ;;   because it is not relevant for the results
]

;; declaring own variables for turtles

turtles-own [
  ethnicity     ;; indicated by colour of squares, a number between 1 and
                ;;   the initial number of ethnicities
  population    ;; indicated by size of squares
  split-up      ;; variable indicating whether a turtle has recently hatched
]

;; declaring own variables for patches (there are none)

;patches-own [
;]


;;;;;;;;;;;;;;;;;;;;;;;
;; setting things up ;;
;;;;;;;;;;;;;;;;;;;;;;;

;; variables

;; how to set up turtles and the artificial landscape

to setup
  clear-all

  ;; setting up patches

  ;; artificial landscape consists of patches only
  ;; patches can be green = habitable, or blue = unhabitable
  ;; first all patches are set to blue
  ;; then the set percentage of habitable patches is set to green

  let max-percentage 100    ;; the maximum amout of patches that can be green
                            ;;   is also the maximum possible, namely 100%

  ask patches [ set pcolor blue ]
  let total-patches (count patches)
  let habitable-patches round(total-patches * (habitability / max-percentage))
  ask n-of habitable-patches patches [
    set pcolor green ]

;; original draft, that gave each individual patch a chance to be green
;  ask patches [
;    let chance-of-habitability random-float 100
;        ;; create a chance for patches to be habitable based on habitability
;    if chance-of-habitability <= habitability [ set pcolor green ]
;        ;; colour patches that are habitable green
;    if chance-of-habitability > habitability [set pcolor blue ]
;        ;; colour patches that are habitable blue
;  ]

  ;; setting up turtles

  ;; turtles are sprouted instead of created
  ;; because this was the first solution that came to mind and worked

  ask n-of initial-groups patches with [ pcolor = green ] [ sprout 1 ]

  ;; turtles set their ethnicity by counting how many of them
  ;;   there are with the help of the global variable 'initial-ethnicities'
  ;; however this solution does not seem very elegant to me
  ;; each turtle sets their population to a random number between 10 and 75
  ;; their shape is set to 'square' and their colour to shades of grey
  ;; contemplated setting the shape to 'person' and the colour to pink
  ;;   but it simply did not look good
  ;; their size positively corresponds to their population
  ;; however at this point there are visibility issues with small populations
  ;; lastly a variable is defined that later keeps track of
  ;;   whether a turtle recently 'split-up'

  let pop-upper-limit 65    ;; set variables for calculating initial sizes of
  let pop-lower-limit 10    ;;   of populations, limits were chosen arbitrarily
  let turtle-size-limiter 100    ;; variable used to make sure the size of
                                 ;;   of turtles stays below 1

  ask turtles [
    set initial-ethnicities (initial-ethnicities + 1)
    set ethnicity initial-ethnicities
    set population (random pop-upper-limit + pop-lower-limit)
;    set population random 100    ;; changed, unrealistic
;    set shape "person"    ;; changed this due to visibility issues
    set shape "square"
;    set color (139.9 - ((ethnicity) * 0.4))  ;; pink suits shape "person"
    set color scale-color gray ethnicity 0 initial-groups
    set size ((population / turtle-size-limiter))
    set split-up 0
  ]

  ;; the number of total ethnicities is set for the interface monitor only
  ;; same goes for the turtle count representing groups in the simulation

  set total-ethnicities initial-ethnicities
  set total-groups (count turtles)
  reset-ticks
end


;;;;;;;;;;;;;;;;;;;;;;;;;
;; defining what to do ;;
;;;;;;;;;;;;;;;;;;;;;;;;;


;; how populations grow

;; populations grow by a predefined logistics model (see documentation)
;; it also takes care of making sure turtle size does not scale past
;;   the 'carrying-capacity' because once it is reached groups
;;   split up anyway, so having a huge turtle serves no purpose

to grow
  let turtle-size-limiter 100    ;; variable used to make sure the size of
                                 ;;   of turtles stays below 1

  set population (population * exp((reproduction / 100)
      * (1.0 - (population / carrying-capacity))))

;  set population (round population)    ;; stops growth, INVESTIGATE

  ;; option A: turtles that exceeds the carrying-capacity are as big
  ;;   as those that match it
  ifelse population <= carrying-capacity [
    set size ((population / turtle-size-limiter)) ] [ set size 1 ]

  ;; option B: turtles that exceed the carrying-capacity still grow bigger
  ;;   this was useful to find out how many groups surpass the
  ;;   carrying-capacity of their land area, and to adjust for realism
  ;set size ((population / 100))
end

;; how populations are split up

;; populations split once they almost reach the set 'carrying-capacity'
;;   minus 1 because population growth otherwise stops before they reach it
;; they do not split evenly 50:50 but anywhere between 30:70 and 70:30
;; this was arbitrarily set, because the original model had no
;;   documentation of this process
;; a new turtle hatches, with the rest-population of the split
;; its ethnicity matches that of the turtle it hatched from
;; the hatched turtle is also marked as such with 'split-up'
;; lastly the original population is adjusted according to the split

to split
  let pop-split-random 40  ;; used to randomly pick a number between
                                 ;;   0 and 40 and add it to the minimum
  let pop-split-minimum 30 ;; the minimum size of a split population
  ;; both together create a population split anywhere between 30:70 and 70:30

  if population >= (carrying-capacity - 1) [
    let population-split (population *
      ((random pop-split-random + pop-split-minimum) / 100))
    let hatch-ethnicity ethnicity
    let hatch-population (population - population-split)
    hatch 1 [
      set population hatch-population
      set split-up 1
;      set ethnicity hatch-ethnicity    ;; this is already a given with the
                                        ;;   hatch function
    ]
    set population population-split
  ]
end

;; how turtles migrate to new patches

;; CAREFUL the following part should only affect recently hatched turtles
;;   indicated by the 'split-up' variable being set to '1' during 'to split'
;;   but it affects all of them, with all turtles migrating after a split

;; if the turtle just hatched (or rather, if split-up is 1), it sets a
;;   preference for a neighbour patch that is green and without any
;;   other turtles on it
;; if there is such a patch, it becomes the target and it moves there
;; else any green patch is selected as target at random
;; finally the turtles move to its target and are marked as no longer hatched
;; the exact mechanism by wich migration in the original model worked was not
;;   detailled in its documentation, other than there being a preference for
;;   neighbouring patches, hence I kept it as minimalistic as I could

to migrate
  if split-up = 1 [
    let migration-preference neighbors with [
      (pcolor = green) and (not any? turtles-here) ]

    ifelse any? migration-preference [
      let migration-target one-of migration-preference
      move-to migration-target ] [

      let migration-target one-of patches with [
        (pcolor = green) ]
      move-to migration-target ]
    set split-up 0
  ]
end

;; how turtles conduct warfare

;; turtles compare their own ethnicity and population to that of others on
;;   on the same patch, in order to find out who has the bigger population
;; the turtle with the bigger population is considered superior and on the
;;   winning side of warfare
;; population losses occur on both sides, namely 5-25% on the winning side,
;;   and 25-45% on the losing sides, chosen arbitrarily
;; a superior enemy is detected when there is another turle
;;   with a different ethnicity and a higer population than the own
;; if the turtle detects a superior enemy, it dies, because it no longer
;;   serves a function in the simulation, with its population being
;;   incorporated in another turtle and hence no longer representing a group
;; the dead turtles population minus warfare losses is added to the
;;   population of its enemy turtle

;; version 1 also included the following mechanism, but it was omitted
;;   because it turned out to be useless when the previous one already
;;   accounted for the same things
;; likewise an inferior enemy is detected when there is another turtle
;;   with a different ethnicity but a lower population than the own
;; if the turtle detects an inferior enemy, it adds the reduced enemy
;;   population to its own, and the enemy turtle dies

to warfare
  let own-ethnicity ethnicity
  let own-population population
  let superior-enemy other turtles-here with [
    (ethnicity != own-ethnicity) and (population > own-population) ]
;  let inferior-enemy other turtles-here with [    ;; omitted in version 3
;    (ethnicity != own-ethnicity) and (population <= own-population) ]

;; option A: no population losses during warfare
;   if any? superior-enemy [
;     let enemy one-of superior-enemy
;     ask enemy [ set population (population + own-population) ]
;;     set population 0    ;; leftover code, does not seem necessary
;     die
;   ]
;
;   if any? inferior-enemy [
;     let enemy one-of inferior-enemy
;     set population (population + ([ population ] of enemy))
;;     ask enemy [ set population 0    ;; leftover code, does not seem necessary
;;       die ]
;     ask enemy [ die ]
;   ]

;; option B: working with population losses here
;;   however this results in "snowballing" of winning ethnicities
;;   and one ethnicity quickly defeating all the others after a certain time
;;   nonetheless it is the realistic option corresponding to the original model
  let loss-random 20
  let loss-sup-minimum 5
  let loss-inf-minimum 25
  let superior-loss ((random loss-random + loss-sup-minimum) / 100)
  let inferior-loss ((random loss-random + loss-inf-minimum) / 100)

   if any? superior-enemy [
     let enemy one-of superior-enemy
     let enemy-population [ population ] of enemy
     ask enemy [
       set population ((population - (population * superior-loss))
         + (own-population - (own-population * inferior-loss))) ]
;     set population 0    ;; leftover code, does not seem necessary
                          ;;   because it does not matter how big the
                          ;;   polulation of a group is before it dies
     die
   ]

;; omitted mechanism, see comments above
;   if any? inferior-enemy [    ;; leftover code, does not seem necessary
                                ;;   because the section directly above already
                                ;;   accounts for the same process
;     let enemy one-of inferior-enemy
;     let enemy-population [ population ] of enemy
;     set population ((population - (population * superior-loss))
;       + (enemy-population - (enemy-population * inferior-loss)))
;;     ask enemy [ set population 0    ;; leftover code, does not seem necessary
;;       die ]
;     ask enemy [ die ]
;   ]
end

;; how turtles merge

;; turtles compare their ethnicity to that of other turtles
;;   on the same patch
;; the turtle adds its population to one of the same ethnicity
;;   and then it simply dies, because it loses its function when its
;;   population is incorporated into another one and does not
;;   represent a group anymore

to merge
  let own-ethnicity ethnicity
  let own-population population
  let friends other turtles-here with [ ethnicity = own-ethnicity ]

  if any? friends [
    ask one-of friends [ set population (population + own-population) ]
    die
  ]
end

;; how stress affects turtles

;; this checks if stress occurs with a chance of 0.5 percent
;; if it does, the set number of turtles die, chosen randomly

to stress
  let stress-chance 0.5
  let stress-occurence random 100  ;; changed from random-float to random (v02)
  set total-groups (count turtles)
  if stress-occurence <= stress-chance [
    ask n-of round(total-groups * (loss-severity / 100)) turtles [ die ]
  ]
end

;; how to collect statistics

;; only interesting statistics at this point are total number of ethnicities
;;   present in the simulation, partially in order to know when to end it
;;   or how long it may still run for and the total-population, which I found
;;   helpful to compare population growth to the original model
;;   (total-population was omitted in v02)
;; at first it checks if there are turtles with a set ethnicity
;; if there are, the total number of ethnicities raises for 1
;; next iteration of the loop it checks for a different ethnicity
;; after the check a counter is set to +1 and compared to the maximum
;;   possible number of ethnicites in the simulation, so the loop ends once
;;   it checked for the maximum number of ethnicities present

to statistics
  set total-ethnicities 0
;  set total-population 0    ;; omitted in v02

  let n 1
  while [ n <= initial-groups ] [
    if any? turtles with [ ethnicity = n ] [
      set total-ethnicities (total-ethnicities + 1)
    ]
    set n (n + 1)
  ]
;  ask turtles [ set total-population (total-population + population) ]
;  set total-population round(total-population)    ;; omitted in v02

;; the following was removed in version 3 because it does not add
;;   any significant information to the simulation and it did not
;;   work without adjustments in the newer NetLogo version
;  set-current-plot "total-population"    ;; omitted in v02
;  set-current-plot-pen "total-population"
;  plot total-population
end


;;;;;;;;;;;
;; going ;;
;;;;;;;;;;;


;; what happens when the simulation is started

;; the simulation stops once the total number of ethnicities present is 1

to go
  ask turtles [
    grow
    split
    migrate
    warfare
    merge
  ]
  stress
  statistics
  if total-ethnicities = 1 [ stop ]
  tick
end
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@#$#@#$#@
## WHAT IS IT?

This model is a replication of the model described in [1], a social simulation that tests the _Circumscription Theory_ using agent-based modelling. According to this theory, the emergence of states (autonomous political units of higher social complexity) is a predictable response to environmental circumscription, which is supposed to entail warfare of social groups over available land resources [2].

For the purpose of this model, social complexity is defined as political hierarchy, implicitly emerging when social groups subjugate each other through warfare. The highest level of social complexity is achieved when one of the initial social groups subjugates all the others in the simulation.

Using this model, environmental circumscription can be simulated by decreasing the amount of land resources (habitable land), while leaving the number of competing social groups constant.

The model was used to test the following hypothesis: an increase of habitable land in the modelled artificial landscape also increases the time (measured in time-steps) required for one social group to subjugate all other groups in the simulation, and for social complexity to emerge.

A short summary of the research project, that this model is the result of, can be found in [3].


## HOW IT WORKS

The model has the following key parameters:

 1. Habitable land patches in the artificial landscape in percent
 2. Number of different social groups present in the simulation
 3. Carrying capacity of land patches
 4. Population growth of social groups

Social groups are differentiated by their _ethnicity_, a unique variable assigned to each of the initial groups. When the population variable of a social group reaches the carrying capacity of the land patch it is located on (by the means of a logistic population growth model), it splits up into two smaller groups of the same ethnicity. One of them moves to a new land patch. If that patch is already inhabited by another group of the same ethnicity, both groups merge. If it is inhabited by a different group, warfare occurs.

Warfare is a comparison of population sizes. The social group with the larger population subjugates the other group by changing its ethnicity to its own. Then both groups merge after suffering population losses. Social groups of one ethnicity having subjugated all the other groups in the simulation represents the emergence of social complexity, for example the formation of political hierarchies, such as chiefdoms.

Environmental stress represents natural disasters (such as droughts) that may affect social groups. It occurs randomly at a fixed chance at any given time-step (corresponding to ticks) and eliminates a set percentage of groups from the simulation.


## HOW TO USE IT

**_setup_** creates a number of habitable (green) and inhabitable (blue) patches according to the _habitability_ percentage, and randomly locates a number of turtles (representing social groups with a fixed population range) corresponding to _initial-groups_ on habitable patches.

**_go_** starts the simulation (going through the processes described in HOW IT WORKS at each tick) and keeps it going until the number of **total-ethnicities** is 1.

_habitability_ sets the percentage of habitable land patches in the artificial landscape (the lower the percentage, the more circumscribed the environment, other things being equal).

_initial-groups_ sets the number of initial social groups in the simulation (each being assigned their own ethnicity, represented by the colour of the turtle).

_carrying-capacity_ sets the population growth limit at which social groups split up.

_reproduction_ sets the rate of population growth.

_loss-severity_ sets the percentage of social groups that are affected by environmental stress (with randomly chosen groups being eliminated from the simulation).

**total-ethnicities** tracks the number of different social groups (contrasted by their ethnicity) that remain in the simulation at a given tick (corresponds to _initial-groups_ and **total-groups** after setup).

**total-groups** tracks the total number of social groups that remain in the simulation at a given tick (independently from their ethnicity).

## THINGS TO TRY

The suggested and intended use of this model is to test, whether an increase of _habitability_ also increases the number of time-steps (measured in ticks) the simulation goes through before the number of **total-ethnicities** is 1.

## RELATED MODELS

The original model described in [1] was not made public.

## CREDITS AND REFERENCES

[1] S. Scott, “Environmental Circumscription and the Emergence of Social Complexity,” in _Computational Social Science Society of America (CSSSA) Annual Conference 2011_, Washington, DC: Computational Social Science Society of America, 2011.
[2] R. L. Carneiro, “A Theory of the Origin of the State,” _Science, New Series_, vol. 169, pp. 733-738, 1970.
[3] R. Andre, "Simulating the Emergence of Social Complexity Using Agent-Based Modelling," in _Proceedings of the MEi:CogSci Conference 2018_, Bratislava: Comenius University, p. 64, 2018
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@#$#@#$#@
NetLogo 6.0.4
@#$#@#$#@
@#$#@#$#@
@#$#@#$#@
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