NetLogo User Community Models
## WHAT IS IT?
This model explores the rock paper scissors strategy of multi organism competition. Numbers are monitored to see if the model enters equilibrium or non-equilibrium state.
## HOW IT WORKS
When any two competitor species organisms meet one kills the other on the basis:
## HOW TO USE IT
View settings allow you to use the pen to draw movements, useful for tracking the changes in movement when behaviour strategies are applied, see the energy level of each agent and set a duration for the model to run, up to 20000 ticks, that can be changed in the settings for the duration slider to a new maximum at any time. Switch off Timed will leave the model to run indefinitely.
World settings is divided into initial settings and variables. Initial settings let you control the starting numbers. First section on variables allows for the setting of the energy input into the system which is used by the organisms to breed.
The next section Turtle Settings has controls for speed which lets you set the base turtle speed then modify it for each species. Reproduction energy again you create a base setting, which can then be modified for each species. Note reproduction sliders have been set up to prevent cost of zero which causes flooding and will hang the model. However this is not code protected, so if the sliders are changed outside of current settings please beware of the danger.
Respawning represents in nature the chance of an organism finding a hiding hole to prevent total extinction. It is currently designed to be equal, the frequency can be set to a high every tick to once every 100 ticks. Respawning can be turned off by setting to zero.
On the right hand of the layout are the monitors and behaviour strategy switches. Strategies coded in include hunt victim species, evade enemy species, avoid enemy species and find food. The suffix letter on the switch corresponds to the species r = rocks, s = scissors and p = papers.
To change the model dynamics you can turn on predation and set an energy gain for the prey organism eaten. This changes the model substantially from the rock paper scissors effectively just being toxic to each other and in that way acting as population control, to feeding off each other and using that energy to increase their population. Use the r-gain, s-gain and p-gain to customise the predation modelling, in that way you can create hunter species that hunt for the energy gained from prey, whilst others just kill their target species without eating them for the energy.
## THINGS TO NOTICE
You will notice that without Respawning it is not possible to find a dynamic equilibrium of species, the model leads to domination and mass reproduction of one victor species, though you can't predict which will win out.
If you try selectively setting strategies against each other, like evaders vs hunters and adjust settings to create an evolutionary arms race, it is interesting to see which species actually ends up the winner. From the models I've tested predator strategies needs a substantial return on predation to sustain numbers, this reflects the circular nature of the rock paper scissors competition, since what they hunt is also what makes the environment safer for them.
## THINGS TO TRY
The Turtle Settings section lets you change the speed and reproduction abilities of each species. Try to figure out what is advantageous or disadvantageous differences. Try to adjust cost / benefits to reflect investment cost by the species.
Behaviour Strategies section lets you turn on and off strategies of hunting, evading, avoiding and food finding. These strategies do have an order of preference if more than one is turned on, evading as a keep alive strategy has greatest priority and food finding has greater priority than hunting prey species. In this rock paper scissors world, there is actually little advantage to killing prey since they are not food, but it does model a competitive environment in a fashion that causes interesting dynamics.
## EXTENDING THE MODEL
A simple development would be to add different respawning rates, however that doesn't seem to offer a great amount of interesting investigation. New behaviours would of course be interesting as would testing different movement patterns, in this model the base movement is in a straight line.
## NETLOGO FEATURES
The behaviours are dependent on the NetLogo 'neighbours' primitive which allows the organism agent to investigate the world immediately around it and make a movement decision on it. The order of decision making when switched on in order of priority is: Evade an enemy species, find food and finally hunt a target species.
## RELATED MODELS
None searched for.
## CREDITS AND REFERENCES
Rashid Mhar email statishun(at)outlook.com
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