indiscriminate violence| suppressive model

1 collaborator

WHAT IS IT?

A model of the effects of indiscriminete violence in Counter Insurgency warfare. [work in progress]

HOW IT WORKS

To test our Hypotheses, we built an agent-based model based on Data taken from the relevant literature (Arney and Arney, 2013; Lyall, 2009; Martinez and Fitzpatrick, 2011; Zhukov, 2014). Agent based modelling is a simulated approach that uses a computational method. According to Axelrod 1997 it is a third way of doing science, in addition to induction and deduction (Ihara and Lee, 2019, 306-307). Like deduction, agent-based modelling starts with a set of assumptions, which represent the rules of the simulated world. The data that results from the model can be analysed inductively. Contrary to pure induction, the data comes not from the real world but from a strictly specified set of rules that the modeller created in the simulated world. The purpose of agent-based modelling is to aid intuition. So, one should not expect predictions about a future state of a complicated system, but rather understanding of the system. (Woodaman, 2000, 7-8) Our model should be opened and used while reading this paper to better understand it. In our model we make two assumptions. First, we assume that there is no intelligence available. Therefore, we are dealing with a suppressive model, as pictured by Zhou (2019). Because of this, our model assumes that indiscriminate violence solely has a negative effect on insurgencies, as Lyall's results showed in his study on the second Chechen war during 2000 to 2005. Second, it is assumed that the counter insurgency forces have no intelligence over the insurgency whatsoever and do not know who of the population is an insurgent and who is just a peaceful village dweller. This is important because the relevant literature agrees on intelligence being an important influence factor for counter insurgencies. (Lyall and Wilson 2009; Sewall et al., 2015; Zhukov 2014) This model solely deals with an approach to counter insurgency that focuses primarily, if not exclusively, on the insurgents.“Some U.S. tactics in Vietnam such as free-fire zones and carpet bombing, or Russia’s overall approach to fighting Chechen rebels[...]” which “[...] concentrates on physically destroying the unseen opponent embedded in the general population.” As (Sewall et al., 2015, 25) put it in the U.S. Army & Marine Corps Counterinsurgency Field Manual. In contrast to that stands the population centred approach, where counter insurgents try to win the hearts and minds of the population in order to win the war, which will not be covered by this paper. “One common feature of insurgencies is that the government that is being targeted generally takes a while to recognize that an insurgency is occurring. Insurgents take advantage of that time to build strength and gather support.” (Sewall et al., 2015, 52) Therefore our Model can be thought of as one of early stages of a conflict or one where the mentioned insurgent focused approach is pursued by the counter insurgents.Erklären Last, we assume that our individuals (or agents) act completely rational which of course is not exactly how real-life units behave. This is necessary for agent-based models, since it is very hard to code feelings into a computer program. These agents may be seen as single individuals, platoons or even regiments, as long as the same scale is applied to every agent in the model. There are four different sets of agents (breeds) in our model, which can be adjusted in numbers by the user. We have given the adjustable values a range that we think is broad enough to fit most conflicts and yet is narrow enough to be realistic and meaningful. The reason for this “best guess” is that non of the former counter insurgents we contacted answered back to us as well as the Austrian and British ministries of defence acknowledged our official inquiries, but did not answer our request. The same goes for our unofficial requests via insiders. First, Counter insurgents (COIN) who have a chance of ~80% of killing an insurgent within one unit of distance (patch). The chance of ~80% is and educated guess emerged off of casualty numbers from the Afghanistan and Iraq conflicts between 2001 and 2011. This number should be taken with a grain of salt since such numbers are never 100% reliable as (Epps, 2003) elaborated in detail in her paper on Civilian casualties in modern warfare. This counts especially when it comes to insurgent casualties due to the possibility of collateral damage being falsely labelled as insurgent casualties. Second, Insurgents (INS) who have a chance of ~5% of killing a COIN within one patch. Additionally, they have a chance of persuading a civilian to become an active insurgent within one patch. This chance is calculated by generating a random number up to the current number of insurgents and adding a random number of up to 1,000. This number is then compared to a random number of up to the number of civilians. If the first value is lower than the second, the persuasion is executed. These randomizations are added to create a buffer effect onto the agents, so they do not switch sides every time they meet an agent of the other side, although this kind of behaviour can occur. Third, Civilians (CIV) who have a chance of soothe an insurgent to become a peaceful civilian. This calculation is made similarly like with the insurgents, but this time it is dependent on the number of dead agents. Last, those who are unfit for battle (UNF) or completely unwilling and therefore can't be persuaded. This breed of agents cannot interact with any other agent breed and is solely there to act as a target for indiscriminate violence, for example an artillery strike.

Counter insurgents (COIN) are represented by blue policeman figures.

Insurgents (INS) are represented by green soldier figures.

Civilians (CIV) are represented by grey office clerk figures.

Unfit for battle (UNF) are presented by pink student figures. All agents move in a random manner at a speed of 0.2, except COINs wander at a speed of 0.1 to simulate the slower movement of regular army combat patrols. This random movement simulates the chance of agents meeting and attacking or persuading other agents and is dependent on the density of agents in the world. The created world in the model has no borders, to resemble a battle theatre without borders, although this could be implemented very easily if needed. This borderless world is an interesting side note since a 2D rectangle as displayed on the user’s screen is in fact a 3D world. Our agents therefore live on the surface of a Doughnut shaped world. The next actor in the model is a Button for the application of indiscriminate violence (IV?). It is a switch button which activates on user’s command. As long as it is activated a random number of up to 8 agents is killed per unit of time (tick), but as a result a random number of insurgents up to 2 per tick is soothed to become peaceful civilians. Think of this button as the command for an ongoing artillery barrage into a town, arbitrary imprisonments or executions. All these numbers are hardcoded into the model thus can not be changed by the user. It is not tremendously important what value the variables have as long as the positive/negative signs are set correctly, since it more or less only changes the ascend/descend rate of the respective graphs and not their general statement. These values could be changed or made adjustable to fit it to a specific conflict.

HOW TO USE IT

(how to use the model, including a description of each of the items in the Interface tab)

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

Johanna Borscheid, who helped me with the brainworks. Everyone who put a netLogo video on Youtube so I can learn from them.

Comments and Questions

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Click to Run Model

breed [villagers villager]
breed [soldiers soldier]
breed [rebels rebel]
breed [unfits unfit]
breed [deads dead]




globals [ ;; Global variables.
  total-villager
  total-soldier
  total-rebel
  total-dead
  total-unfit
]



turtles-own[  ;; Turtle variables.
  turn-check
  wall-turn-check
  age
]

to setup

  set-default-shape villagers "person business"
  set-default-shape soldiers "person police"
  set-default-shape rebels "person soldier"
  set-default-shape unfits "person student"
  set-default-shape deads "person"
  clear-all
  reset-ticks
  ask patches [set pcolor grey - 3]

  create-villagers CIV [
    set color grey
    setxy random-xcor random-ycor
    set age (random 100) / UNF
    if age < 1 [set breed unfits set color pink - 1]
  ]


  create-soldiers COIN [
    set color brown + 3
    setxy random-xcor random-ycor
    set age -2
  ]


  create-rebels INS [
    set color brown + 2
  setxy random-xcor random-ycor
    set age (random 100)
  ]

  ask turtles [set size 0.5]
end 

to go              ;; the fun beginns

  tick
  update-globals
  do-plots
  set-default-shape villagers "person business"
  set-default-shape soldiers "person police"
  set-default-shape rebels "person soldier"
  set-default-shape unfits "person student"
  set-default-shape deads "person"

  ask villagers [
  forward 0.2
    wander
    let current-villager self
   let persuade  random count villagers + random 1000
    if persuade <  random count deads [ask rebels with [distance current-villager < 1] [soothe]]
  ]

  ask soldiers [
    ;;let closest-home min-one-of patches with [pcolor = green] [distance myself]
    ;;face  closest-home
    wander
    forward 0.1
    let current-soldier self
    let attack random 100
    if attack < 80 [ask rebels with [distance current-soldier < 1] [kill]]
  ]


  ask rebels [
    ;;let dice random 3
    ;;let change (dice - 1)
    ;;set heading (heading + change)
    ;;let closest-rebel min-one-of (other rebels) [distance myself]
    forward 0.2
    wander
    let current-rebel self
    let persuade random count rebels + random 1000
    if persuade < random count villagers [ask villagers with [distance current-rebel < 1] [radicalize]]
    let attack random 100
    if attack < 5 [ask soldiers with [distance current-rebel < 1] [kill]]
  ]

  ask unfits [
    wander
    ;;let closest-home min-one-of patches with [pcolor = grey] [distance myself]
    ;;face  closest-home
    fd 0.2
]




  if IV? [
    carefully [ask  up-to-n-of random 8 turtles with  [age > -1]  [kill]] [print error-message]
  carefully [ask  up-to-n-of random 2 rebels [soothe]] [print error-message]
  ]
end 
;; one-of patches with [pcolor = grey]
;; while patch witch pcolor grey - 3 (other patches)



;; definitions

to reinforce
  create-soldiers COIN [
    set color brown + 2
    setxy random-xcor random-ycor
  ]
end 

to soothe
  set breed villagers
  set color grey
end 

to radicalize
  set breed rebels
  set color brown + 1
end 

to kill
  set breed deads
  set color black
  set age -2
end 

to wander                 ;; Random movement for agents.
  set turn-check random 20
  if turn-check > 15
  [right-turn]
  if turn-check < 5
  [left-turn]
  if [pcolor] of patch-ahead 1 = green
  [wall]
end 

to wall ;;  Turn agent away from wall
    set wall-turn-check random 10
    if wall-turn-check >= 6
    [wall-right-turn]
    if wall-turn-check <= 5
    [wall-left-turn]
end 

to wall-right-turn ;;Generate a random degree of turn for the wall sub-routine.
  rt 170
end 

to wall-left-turn ;;Generate a random degree of turn for the wall sub-routine.
  lt 170
end 

to right-turn ;;Generate a random degree of turn for the wander sub-routine.
  rt random-float 10
end 

to left-turn   ;;Generate a random degree of turn for the wander sub-routine.
  lt random-float 10
end 
;;to right-turn
  ;;rt random-float 10
;;end

;;to left-turn
  ;;lt random 10
;;end

;; reporters

to update-globals ;;Set globals to current values for reporters.
  set total-rebel (count rebels)
  set total-soldier (count soldiers)
  set total-villager (count villagers)
  set total-dead (count deads)
  set total-unfit (count unfits)
end 

to do-plots                ;; Update monitors
  set-current-plot "Population Totals"
  set-current-plot-pen "insurgents" plot total-rebel
  set-current-plot-pen "COIN" plot total-soldier
  set-current-plot-pen "Civilains" plot total-villager
  set-current-plot-pen "Dead" plot total-dead
  set-current-plot-pen "Unfit for battle" plot total-unfit
end

There are 6 versions of this model.

Uploaded by	When	Description	Download
Stefan Baumgartner	about 5 years ago	increased possible insurgents	Download this version
Stefan Baumgartner	about 5 years ago	implementing more bugs to fix later	Download this version
Stefan Baumgartner	about 5 years ago	fixa da bug	Download this version
Stefan Baumgartner	about 5 years ago	something	Download this version
Stefan Baumgartner	about 5 years ago	increased CIV amount, lowered turtle size	Download this version
Stefan Baumgartner	about 5 years ago	Initial upload	Download this version

Attached files

File	Type	Description	Last updated
indiscriminate violence\| suppressive model.png	preview	Preview for 'indiscriminate violence\| suppressive model'	about 5 years ago, by Stefan Baumgartner	Download

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Indicriminete Violence & Intelligence

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