Modeling A Simulation of Evolutionary Behavior
by Stephen Hilber
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. 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. 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. This project
will show that agents which possess the capability to change will change to better fit
their environment.
Abstract
Conway's Game of Life was the first prominent agent-based model. Each cell was an
"agent" that contained either a 0 or a 1 (alive or dead) depending on how many
neighbors it had, and acted independently of the environment. Conway's Game of
Life didn't lead to any profound insights, but it did pave the way for future agentbased
modeling. The advantage of agent-based modeling, as many found out, is that
it did not assume prior conditions. It was a method of building worlds "form the
bottom up", where independent agents were able to create complex worlds without
any overseers. One popular psychological game, Prisoner's Dilemma, spawned a
series of games where agents tried t maximize their outcome, often at the expense of
other agents. Eventually, these agent-based models were incorporated in studies of
flocking. The models created to show flocking behavior in birds did not incorporate
flock leaders, as many presumed. Instead, the birds all acted for their own best
interests, and directions and resting points were chosen as compromises of sorts.
Using the theory that independent agents can create organized structures such as
flocks, Epstein and Axtell created the Sugarscape world in an effort to discover if
social behaviors and human characteristics could emerge through independent
actions. The Sugarscape model had agents able to breed, fight, trade, and die, and the
core of the model was the resource sugar. Each agent had a metabolism rate which
burned off its sugar; if it ran out of sugar, the agent died. Instead of isolated
behavior, however, the agents soon used their resources to work together. Agents
shared sugar, sent "scouts" to gather sugar for the benefit of all, engaged in wars,
and in general performed a startlingly large amount of human behavioral
characteristics. When spice, a second resource with its own metabolism rate, was
introduced into the world of Sugarscape, trade emerges as agents tried to meet their
needs as best they could - and tried to get the best deal as a result. These behaviors
are surprising, but ultimately show the value of agent-based modeling and the useful
insight it can provide.
Research & Background
Theory & Expected Results
In order to simulate evolutionary behavior in an agent-based system, the agents
need to simulate the real world as much as possible. In actual evolution (as
described in computer science terms), two agents of opposing sex combine their
genetic information at random to generate offspring with half of each parent's
traits. Genetic mutations also happen at random, causing new traits that neither
parent had in their genetic code. This gradual evolution creates swarms of different
agents, and those agents that are best suited for their environments will be best able
to survive and reproduce. Of course, several different portions of the environment
could be home to different "breeds" of agents, and these different breeds could live
alongside each other in separate societies. It is this phenomenon that this project is
trying to recreate. By closely following the rules of genetics, the project should be
able to show several different breeds of agents thriving, having only been created
by a single agent type. Instead of passing on dominant and recessive genes,
however, this project opts for a higher-level approach by using characteristics such
as extroversion as the "genetic currency". While the human genome has tens of
thousands of genes to determine these characteristics, such attention to detail is
impossible and unnecessary for this project. By changing characteristics such as
cooperation and extroversion on a slider, agent's traits will gradually change as
they are passed down from generation to generation. This effectively simulates
actual genetic activity, and is thus effective for this project. Agents will breed, die,
and interact, eventually changing the genetic code of their societies to suit their
needs.
Credit goes to Conway for The Game of Life, Epstein and Axtell for Sugarscape,
the MASON team for developing MASON, and Dr. John A. Johnson for the IPIPNEO
Sources