turtles-own [
  sugar           ;; the amount of sugar this turtle has
  metabolism      ;; the amount of sugar that each turtles loses each tick
  vision          ;; the distance that this turtle can see in the horizontal and vertical directions
  vision-points   ;; the points that this turtle can see in relative to it's current position (based on vision)
]

patches-own [
  psugar           ;; the amount of sugar on this patch
  max-psugar       ;; the maximum amount of sugar that can be on this patch
]

;;
;; Setup Procedures
;;

to setup
  clear-all
  create-turtles initial-population [ turtle-setup ]
  setup-patches
  reset-ticks
end

to turtle-setup ;; turtle procedure
  set color red
  set shape "circle"
  move-to one-of patches with [not any? other turtles-here]
  set sugar random-in-range 5 25
  set metabolism random-in-range 1 4
  set vision random-in-range 1 6
  ;; turtles can look horizontally and vertically up to vision patches
  ;; but cannot look diagonally at all
  set vision-points []
  foreach n-values vision [? + 1]
  [
    set vision-points sentence vision-points (list (list 0 ?) (list ? 0) (list 0 (- ?)) (list (- ?) 0))
  ]
  run visualization
end

to setup-patches
  file-open "sugar-map.txt"
  foreach sort patches
  [
    ask ?
    [
      set max-psugar file-read
      set psugar max-psugar
      patch-recolor
    ]
  ]
  file-close
end

;;
;; Runtime Procedures
;;

to go
  if not any? turtles [
    stop
  ]
  ask patches [
    patch-growback
    patch-recolor
  ]
  ask turtles [
    turtle-move
    turtle-eat
    if sugar <= 0
      [ die ]
    run visualization
  ]
  tick
end

to turtle-move ;; turtle procedure
  ;; consider moving to unoccupied patches in our vision, as well as staying at the current patch
  let move-candidates (patch-set patch-here (patches at-points vision-points) with [not any? turtles-here])
  let possible-winners move-candidates with-max [psugar]
  if any? possible-winners [
    ;; if there are any such patches move to one of the patches that is closest
    move-to min-one-of possible-winners [distance myself]
  ]
end

to turtle-eat ;; turtle procedure
  ;; metabolize some sugar, and eat all the sugar on the current patch
  set sugar (sugar - metabolism + psugar)
  set psugar 0
end

to patch-recolor ;; patch procedure
  ;; color patches based on the amount of sugar they have
  set pcolor (yellow + 4.9 - psugar)
end

to patch-growback ;; patch procedure
  ;; gradually grow back all of the sugar for the patch
  set psugar min (list max-psugar (psugar + 1))
end

;;
;; Utilities
;;

to-report random-in-range [low high]
  report low + random (high - low + 1)
end

;;
;; Visualization Procedures
;;

to no-visualization ;; turtle procedure
  set color red
end

to color-agents-by-vision ;; turtle procedure
  set color red - (vision - 3.5)
end

to color-agents-by-metabolism ;; turtle procedure
  set color red + (metabolism - 2.5)
end


; Copyright 2009 Uri Wilensky.
; See Info tab for full copyright and license.
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@#$#@#$#@
## WHAT IS IT?

This second model in the NetLogo Sugarscape suite implements Epstein & Axtell's Sugarscape Constant Growback model, as described in chapter 2 of their book Growing Artificial Societies: Social Science from the Bottom Up. It simulates a population with limited, spatially-distributed resources available. It differs from Sugarscape 1 Immediate Growback in that the growback of sugar is gradual rather than instantaneous.

## HOW IT WORKS

Each patch contains some sugar, the maximum amount of which is predetermined. At each tick, each patch regains one unit of sugar, until it reaches the maximum amount. The amount of sugar a patch currently contains is indicated by its color; the darker the yellow, the more sugar.

At setup, agents are placed at random within the world. Each agent can only see a certain distance horizontally and vertically. At each tick, each agent will move to the nearest unoccupied location within their vision range with the most sugar, and collect all the sugar there.  If its current location has as much or more sugar than any unoccupied location it can see, it will stay put.

Agents also use (and thus lose) a certain amount of sugar each tick, based on their metabolism rates. If an agent runs out of sugar, it dies.

## HOW TO USE IT

Set the INITIAL-POPULATION slider before pressing SETUP. This determines the number of agents in the world.

Press SETUP to populate the world with agents and import the sugar map data. GO will run the simulation continuously, while GO ONCE will run one tick.

The VISUALIZATION chooser gives different visualization options and may be changed while the GO button is pressed. When NO-VISUALIZATION is selected all the agents will be red. When COLOR-AGENTS-BY-VISION is selected the agents with the longest vision will be darkest and, similarly, when COLOR-AGENTS-BY-METABOLISM is selected the agents with the lowest metabolism will be darkest.

The four plots show the world population over time, the distribution of sugar among the agents, the mean vision of all surviving agents over time, and the mean metabolism of all surviving agents over time.

## THINGS TO NOTICE

The world has a carrying capacity, which is lower than the initial population of the world. Agents who are born in sugarless places or who consume more sugar than the land cannot be supported by the world, and die. Other agents die from competition - although some places in the world have enough sugar to support them, the sugar supply is limited and other agents may reach and consume it first.

As the population stabilizes, the average vision increases while the average metabolism decreases. Agents with lower vision cannot find the better sugar patches, while agents with high metabolism cannot support themselves. The death of these agents causes the attribute averages to change.

## THINGS TO TRY

How dependent is the carrying capacity on the initial population size?  Is there a direct relationship?

## EXTENDING THE MODEL

How does changing the amount or rate of sugar growback affect the behavior of the model?

## NETLOGO FEATURES

All of the Sugarscape models create the world by using `file-read` to import data from an external file, `sugar-map.txt`. This file defines both the initial and the maximum sugar value for each patch in the world.

Since agents cannot see diagonally we cannot use `in-radius` to find the patches in the agents' vision.  Instead, we use `at-points`.

## RELATED MODELS

Other models in the NetLogo Sugarscape suite include:

* Sugarscape 1 Immediate Growback
* Sugarscape 3 Wealth Distribution

## CREDITS AND REFERENCES

Epstein, J. and Axtell, R. (1996). Growing Artificial Societies: Social Science from the Bottom Up.  Washington, D.C.: Brookings Institution Press.


## HOW TO CITE

If you mention this model in a publication, we ask that you include these citations for the model itself and for the NetLogo software:

* Li, J. and Wilensky, U. (2009).  NetLogo Sugarscape 2 Constant Growback model.  http://ccl.northwestern.edu/netlogo/models/Sugarscape2ConstantGrowback.  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.

## COPYRIGHT AND LICENSE

Copyright 2009 Uri Wilensky.

![CC BY-NC-SA 3.0](http://i.creativecommons.org/l/by-nc-sa/3.0/88x31.png)

This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License.  To view a copy of this license, visit http://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.

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