Zed Langston
April 5, 2015
Assignment 1: Exploring NetLogo

Description:

This is a 2-dimensional ecomodel that was built with code using NetLogo.  This model uses commands, variables and procedures to simulate turtles (interacting agents) and grass (patches) interactions.  Turtles move around to find food (patches), can eat, give birth to new turtles or die based on their energy.  Each turtle loses one energy with every step taken across the patches.  If a turtle reproduces, the new turtle gets 25 energy that is deducted from the parents energy.  If a turtle runs out of energy, the turtle dies.  In this model the grass (patches) are set to regrow.  The model setup parameters are the number of turtles (50), energy from grass (25) and birth energy (25).  The procedures include moving, eating, reproduction and death along with the setup and go commands.  The NetLogo model also includes monitors and an automatic rescaling plot to visually depict the interactions.

Below is a short video of the simulation over 750 time-steps (ticks).

Questions:

1.) The “energy-from-grass” parameter (adjustable) influences the emergent patterns in the model simulation because the energy a turtle receives from eating is one of the main factors in the rate of reproduction and if turtles continues to eat or dies over time.  The higher this parameter is set the faster the population grows over time meaning the turtles reproduce more often and faster visible in the interface images.  Several model iterations using the baseline conditions and changing the “energy-from-grass” parameter reveal that with low “energy-from-grass” (25) the stabilized system operation population is ~1,300 (left image).  Using the same baseline settings and a medium “energy-from-grass” setting (50) at a similar time step, the population becomes stable around ~2,500 (middle image).  At a high “energy-from-grass” setting (100) at a similar time step, the population becomes stable around 5,100 (right image).  Each run starts with a high number of patches (that are “eaten” rapidly) and 50 turtles whose population grows exponentially (to approximately twice the stable value) and sharply decreases until the system stabilizes.  The interaction intersections as well as the slope of the population decline change depending on the parameter settings.  The time step of the images below is ~92.5 ticks.

2) The “birth-energy” parameter (adjustable) influences the emergent patterns in the model simulation because that value determines the initial energy of a new turtle and how much the parents energy is reduced by reproduction.  In addition, a higher “birth-energy” means that a turtle can reproduce faster in time and is less likely to die if the turtle has to move farther to find grass.  Several model iterations using the baseline conditions and changing the “birth-energy” parameter reveal that with low “birth-energy” (25) the stabilized system operation population is ~1,175 (left image).  Using the same baseline settings and a medium “birth-energy” setting (50) at a similar time step, the population becomes stable around ~1,250 (middle image).  At a high “birth-energy” setting (100) at a similar time step, the population becomes stable around 1,200 (right image) but at a later time step (tick).  The interaction intersections as well as the slope of the population decline change depending on the parameter settings. The time step of the images below is ~92.5 ticks.

3) The emergent pattern is not sensitive to the initial population size.  Over time the emergent patterns are a reflection of the parameter settings based on the local influences (interactions) of the individual agents.  The individual decisions will result in a collective behavior independent of the number of agents at the beginning.  The turtles can not see the whole system but instead make their decisions based on their best interest regardless of how many agents there are.