Simulating Evolutionary Social Behavior

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Simulating Evolutionary Social Behavior

• by
• Stephen Hilber

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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.

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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.

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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.

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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.
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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.
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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.
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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.
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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.
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The Current Model

• 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.

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The extroverted agents are now starting to find
other extroverted agents, while the introverted
agents are searching out other introverts.
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Now you can see them grouping together with
others of like kind some extroverts are trapping
the introverts.
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Much the same as before.
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Conclusion

• 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.

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Credits

• 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.