The aim of this dissertation research was to begin approaching a larger goal of studying social discrimination showing the experimental potential of artificial life simulation in exploring problems involving both genetics and social behavior. Artificial life simulations can be used to conduct experiments that cannot be carried out on real humans, are easily manipulated and repeated, can collect detailed genetic data, and have an accelerated time-scale that can be used to study effects over many generations.
The first stage of this research was the creation and initial testing of an artificial life package capable of multiple kinds of environments, Garden, and a custom genetics package, DGEANN, which supports multiple genetic models. Experimentation showed that diploid genetics worked well in Garden and were used for the following experiments. In the second stage, pilot studies began introducing social behavior involving family relationships and inbreeding, focusing on how mating behavior and inbreeding avoidance influenced the population's genetic diversity, behavior, and long-term survival.
In the last stage of the research presented here, mating choice and preference for similar or dissimilar mates was examined as a simple form of discrimination. Mate similarity preferences evolved in environments that included genetic disorders and migration to another population, which influenced the direction in which those preferences evolved. The higher migration level led to agents preferring more similar mates, as did the higher initial percentage of genetic disorder carriers. The artificial life simulation shown in this work can be applied to other real-world problems involving both social and genetic components.
|Commitee:||Si, Mei, Sun, Ron, Spector, Lee|
|School:||Rensselaer Polytechnic Institute|
|School Location:||United States -- New York|
|Source:||DAI-B 82/8(E), Dissertation Abstracts International|
|Subjects:||Computer science, Biology, Artificial intelligence|
|Keywords:||Agent-based simulation, Artificial life, Evolving neural networks, Social simulation|
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