HunterGatherers

This model represents a number of hunter-gatherer bands in an environment that represents forest and plains areas.

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WHAT IS IT? ----------- This model represents a number of hunter-gatherer bands in an environment that represents forest and plains areas. HOW IT WORKS ------------ Turtles (hunter-gatherer bands) burn energy every turn and they try to eat every turn to gain energy. When they eat, they degrade the environment which then recovers at a slower rate. Turtles have a maximum energy limit, above which they stop growing. The amount of foliage in the environment is color-coded. The more green a patch is, the more foliage (energy) is has. Forest areas start very green and Plains start much less green, by default. Each type of environment has a maximum energy limit, above which it stops growing. Similarly, each type of environment has a recovery rate. If a forest is degraded below a certain threshold level, it will change to a plains environment type and therefore not recover as much. Each turtle tries to find a place to go on the next turn by choosing a semi-cardinal patch from their current patch. Turtles will randomly choose one of the patches with the most energy (food). Turtles leave tracks, which expire after a while. Turtles are xenophobic in that they will avoid other turtles' tracks, which can override the search for high-energy patches. If no food can be found elsewhere, hunger overrides xenophobia. HOW TO USE IT ------------- Click the "Setup" button to randomly place some turtles in the environment. Click the "Go" button to run the model continuously and the "Step" button to run it one turn at a time. The number of turtles in the model is set with the numberOfTurtles slider (4 by default). Four turtles is a stable population for the default environment size and variable settings. The remainder of the variables are closely interrelated! Please play with them, but do not be surprised if the model varies wildly once they are changed. It is the relative proportions of these variables more than their individual amplitudes that matters. The maximum amount of energy that a turtle may have is set with the maxTurtleEnergy slider. THINGS TO NOTICE ---------------- Turtles will degrade the environment and the environment will (more slowly) recover. The total energy of the environment will drop until it finds a new equilibrium energy level. Turtles that start on the plains will (by default) not be able to extract enough energy to live. Therefore, their search for food will either end in death by starvation or in a move to the forest. Note that turtles in the forest will tend to stay in the forest. They will appear to "bounce off" of the forest-plain boundary due to the search for patches with available food. If the amountTurtlesEat/Burn are the same, they can't make up for lost energy. Therefore, turtle populations are unstable and will always eventually die. THINGS TO TRY ------------- Increase the number of turtles and run the simulation. If too many turtles try to share the environment, some (or all) of them will die. If the simulation starts with more turtles than the equilibrium level, the environment will degrade rapidly, causing the death of many turtles. Once the excess turtle population has been removed, the environment will recover to a new equilibrium state, defined by a stable energy level over time. That energy level will be proportional to the number of remaining turtles. If the number of turtles is increased drastically (try 100), the environment will degrade so rapidly as to kill all turtles. The environment will eventually recover, although some parts of the forest may have been damaged and only recover to the energy level of plains. Try starting the simulation with turtles on the plains vs in the forest. What happens to the turtles on the plains? By default, turtles cannot live on the plains for long so their search for food either ends in starvation or in a move to the forest. EXTENDING THE MODEL ------------------- Turtles all represent hunter-gatherer bands of the same size population (and have the same maximum energy level). The model could be extended by allowing turtles (bands) to have different population levels (and hence energy requirements). Turtles do not currently have individual members that are "born" or "die". It has yet to be shown whether modeling "generations" will be necessary or useful. Turtles do not currently learn in any way. The model could be extended by allowing turtles (bands) to learn more about their environment over time, which could result in a greater capacity for exploitation. Better exploitation capability could allow, for example, a band to extract more energy from an environment without degrading it as fast. Bands may only be able to exploit a patch if other turtles are not also using it (e.g. "farming" by one turtle may allow other turtles to "steal" from the patch, but not to also "farm"). Turtles may be able to learn about adjacent environments (e.g. forest turtles living near plains) through exposure. This has precidence in the detailed environmental knowledge held by extant hunter-gatherer bands. Turtles may "forget" what they have learned about an environment if they are not exposed to it for a long time. This may be modeled by direct forgetfullness or by losing "institutional memory", as generations pass. Can it be shown that learning through environmental exposure is sufficient to model the dynamics between agricultural and hunter-gatherer societies. NETLOGO FEATURES ---------------- This model uses the construct "random-one-of neighbors with ..." to find semi-cardinal patches near the current turtle with various properties. This construct is used in a cascade of loosening restrictions on turtle movement to implement a turtle's preferences in the hunt for food. Each patch has a variable which determines its environment type. RELATED MODELS -------------- This model was partially inspired by the Schelling's famous Segegration model and should evolve to become a superset of the Cooperation and Wealth Distribution models in order to account for those behaviors. CREDITS AND REFERENCES ---------------------- To refer to this model in academic publications, please use: Hyland-Wood, D. (2004). NetLogo HunterGatherer model. http://www.itee.uq.edu.au/~dwood/NetLogoModels/HunterGatherers. School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia. In other publications, please use: Copyright 2004 by David P. Hyland-Wood. All rights reserved. See http://www.itee.uq.edu.au/~dwood/models/HunterGatherers.html for terms of use. This model is built in the NetLogo environment: Wilensky, U. 1999. NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University. Evanston, IL.