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Simulating Evolutionary Social Behavior

by Stephen Hilber. Simulating Evolutionary Social Behavior. Abstract.

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Simulating Evolutionary Social Behavior

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  1. by Stephen Hilber Simulating Evolutionary Social Behavior

  2. Abstract • With the creation of Epstein and Axtell's Sugarscape environment, increasing emphasis has been placed on the creation of "root" agents - agents that can each independently act and interact to establish patterns identifiable in our everyday world. Models created for traffic patterns and flocking patterns confirm that these conditions are caused by each participating agent trying to achieve the best possible outcome for itself.

  3. Purpose • The purpose of this project is to attempt to model evolutionary behavior in agents in an environment by introducing traits and characteristics that change with the different generations of agents. Using the modeling package MASON programmed in Java, I will be able to create an environment where agents will pass down their genetic traits through different generations. This project will show that agents which possess the capability to change will change to better fit their environment.

  4. Behavioral Traits • By adding certain behavioral traits and a common resource to the agents, I hope to create an environment where certain agents will prosper and reproduce while others will have traits that negatively affect their performance. In the end, a single basic agent will evolve into numerous subspecies of the original agent and demonstrate evolutionary behavior.

  5. EXAMPLE: Start Simulation The agents start off in random locations, and have just begun to move in this run. BLUE dots represent the agents GRAY dots are the trails they leave behind In this run, a cluster of agents was created along the eastern environment. This will affect the rest of the run.

  6. Later,the eastern agents have attracted extroverted agents to their vicinity, as they already have large groups to satisfy the needs of the extroverts. This begins to thin out the rest of the environment as more agents are clustered around the eastern side of the environment.

  7. The extroverted agents are being gradually drawn east through a chain reaction of movement, while introverted agents are slowly being trapped against the wave of extroverts. The introverts simply have no place to run.

  8. Some of the agents have started to die out due to isolation; even introverts need to be around others at some point in their life. Most of the extroverts and the trapped introverts have moved to the east. The remaining agents are either moving east or dying off.

  9. As the run reaches stability, the vast majority of agents have survived by grouping together. Introverts may not be happy, but they need society to survive. A few introverts survive by staying on the fringes of society, keeping contact with people only when needed.

  10. In the final version of my project, the RED agents are extroverts and the BLUE agents are introverts. Here's the starting screen again. Much prettier this time. The Current Model

  11. The extroverted agents are now starting to find other extroverted agents, while the introverted agents are searching out other introverts.

  12. Now you can see them grouping together with others of like kind; some extroverts are trapping the introverts.

  13. Much the same as before.

  14. Introverts group together with other introverts, and extroverts group with other extraverts. This occurs regardless of the environment, and this seperation of introverts and extraverts is surprising. It seems that although the introverts are normally adverse to being too close to other agents, they prefer to interact with like kinds instead of being trapped in a sphere of different kinds of agents. Conclusion

  15. Credit goes to Conway for The Game of Life, Epstein and Axtell for Sugarscape, the MASON team for developing MASON, the Myers-Briggs Type Indicator, NetLogo, Swarm, and Dr. John A. Johnson's IPIP-NEO. Credits

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