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## WHAT IS IT?

The purpose of the Socio-Natural model is to test which social behaviors
contribute to the resiliency of both culture and environment utilizing comparison between two differing social systems.

## HOW IT WORKS

The Socio-Natural model is made up of two breeds of agents who interact
with the environment and other agents. The environment is a wrapping world made up of patches with a generic resource that have a determined carrying capacity and growth rate assigned at the start of the simulation. For this model one tick or iteration represents one year. Every model should run for at least one-thousand iterations or until population collapse. Population collapse is defined as no more individuals.

The individuals of different breeds possess different land-use, common benefit, and
movement rules. For the A-person breed these rules include: (1) equitable distribution of wealth and resources to provide for common benefit (2) demographic regulation with possible seasonal migration to prevent resource depression (3) utilization of diverse resources to prevent resource depression.

The differing elements of the B-person breed include: (1) unequitable distribution of wealth and resources altering levels of benefit (2) sedentariness and sharp shifts in population (3) skewed resource dependence that effects biodiversity.

At every iteration all individuals (1) move, (2) harvest, (3) share, (4) randomly reproduce based on number of possible offspring, (5) age, and (6) randomly die. The world (7) regrows grass at a fixed amount every iteration.

## HOW TO USE IT

On the left, adjust the sliders to change initial population levels of each breed, to determne how much resource each inidividual of a breed can share, and to decide reproduction rates.

On the right, monitor the population levels of each breed, resource levels in the world, and the distribution of resources.

## THINGS TO NOTICE

The socio-natural model is interested in certain response variables. These include: (1) How long a population sustains before collapse, if collapse occurs, (2) how stable
population levels are, (3) how much resource is maintained in the world, and (4) how equitably resources are shared.

## THINGS TO TRY

Move sliders to alter how much breeds share, reproduce or to see how initial population effects outcomes.

How long do populations last when they are not competeing against one another?
What does the resource level look like?
How equitable is their society?

## EXTENDING THE MODEL

Advance any of these these settings by altering the code with simple or complex changes.
For example, resource regrowth could be altered to seasonal cycles or change the code to reflect agricultural and technological control over resource cycles.

This socio-natural model is currently a closed system which if opened to simulate immigration, has potential to reveal more interesting resilient behavior relational patterns. Additionally, more diverse resources along with diverse use of those resources would enhance the program. Moreover, introducing a level of diversity and modifying to an open system would produce dynamic resource growth rates, advanced migratory and movement patterns and allow for more socio-natural perturbations to be tested.

Another avenue to achieve higher variance in social complexity use of NetLogo’s Hubnet. Hubnet is participatory simulation offering that allows models to run by its programmed rules as well as by human participation.

The socio-natural model can also be advanced with innovation coding. This can be achieved by either equipping agents with coping mechanisms in the programming stage or including a genetic algorithm in which agents learn.

Future simulations with this modification have the potential to illuminate much about resilient behavior adoption and sustainable development education.

## RELATED MODELS

This model incorporates features from other netlogo models: diffusion on a directed network, cooperation, feeding, and wolf/sheep predation.

## CREDITS AND REFERENCES

George Lescia

Axtell, Robert L., Joshua M. Epstein, Jeffrey S. Dean, George J. Gumerman, Alan C. Swedlund, Jason Harburger, Shubha Chakravarty, Ross Hammond, Jon Parker, and Miles Parker
2002 Population growth and collapse in a multiagent model of the Kayenta Anasazi in Long House Valley. Proceedings of the National Academy of Sciences 99(suppl 3): 7275–7279.

Dean, Jeffrey S., George J. Gumerman, Joshua M. Epstein, Robert L. Axtell, Alan C. Swedlund, Miles T. Parker, and Stephen McCarroll
2000 Understanding Anasazi culture change through agent-based modeling. Dynamics in
human and primate societies: Agent-based modeling of social and spatial processes: 179–205.

Epstein, Joshua M.
1996 Growing artificial societies: social science from the bottom up. Brookings Institution
Press.

1997 Artificial societies and generative social science. Artificial Life and Robotics 1(1): 33–34.

1999 Agent-based computational models and generative social science. Generative Social
Science: Studies in Agent-Based Computational Modeling 4(5): 4–46.

2006 Generative social science: Studies in agent-based computational modeling. Princeton
University Press.

2008 Why model? Journal of Artificial Societies and Social Simulation 11(4): 12.
Epstein, Joshua M., and Robert Axtell

Gilbert, Nigel, and Klaus G. Troitzsch
2005 Simulation for the Social Scientist (2nd Edition). McGraw-Hill Professional Publishing, Berkshire, GBR.

Kohler, Timothy & Sander Van der Leeuw. (Eds.)
2007 The Model Based Archaeology of Socio-Natural Systems. School for Advanced Research,
Santa Fe, NM.

Wilensky, Uri, and William Rand
2015 An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered
Complex Systems with NetLogo. MIT Press, April 10.

Wilensky, U. (1997). NetLogo Cooperation model. http://ccl.northwestern.edu/netlogo/models/Cooperation. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL

Stonedahl, F. and Wilensky, U. (2008). NetLogo Diffusion on a Directed Network model. http://ccl.northwestern.edu/netlogo/models/DiffusiononaDirectedNetwork. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

Wilensky, U. (1998). NetLogo Wealth Distribution model. http://ccl.northwestern.edu/netlogo/models/WealthDistribution. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

Li, J. and Wilensky, U. (2009). NetLogo Sugarscape 3 Wealth Distribution model. http://ccl.northwestern.edu/netlogo/models/Sugarscape3WealthDistribution. 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.

Wilensky, U. (2005). NetLogo Wolf Sheep Predation (System Dynamics) model. http://ccl.northwestern.edu/netlogo/models/WolfSheepPredation(SystemDynamics). Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

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