Document Type
Thesis
Degree
Master of Science
Major
Physics
Date of Defense
6-14-2012
Graduate Advisor
Sonya Bahar, PhD
Committee
Maric, Nevena
Flores, Ricardo
Abstract
Amongst the scientific community, there is consensus that evolution has occurred; however, there is much disagreement about how evolution happens. In particular, how do we explain biodiversity and the speciation process? Computational models aid in this study, for they allow us to observe a speciation process within time scales we would not otherwise be able to observe in our lifetime. Previous work has shown phase transition behavior in an assortative mating model as the control parameter of maximum mutation size (µ) is varied. This behavior has been shown to exist on landscapes with variable fitness (Dees and Bahar, 2010), and is recently presented in the work of Scott et al. (submitted) on a completely neutral landscape, for bacterial-like fission as well as for assortative mating. Here I investigate another dimension of the phase transition. In order to achieve an appropriate ‘null’ hypothesis and make the model mathematically tractable, the random death process was changed so each individual has the same probability of death in each generation. Thus both the birth and death processes in each simulation are now ‘neutral’: every organism has not only the same number of offspring, but also the same probability of being randomly killed. Results show a continuous nonequilibrium phase transition for the order parameters of the population size and the number of clusters (analogue of species) as the random death control parameter δ is varied for three different mutation sizes of the system. For small values of µ, the transition to the active state of survival happens at a small critical value of δ; in contrast, for larger µ, the transition happens later – suggesting a robustness of the system with increased mutation ability.
OCLC Number
809382225
Recommended Citation
King, Dawn MIchelle, "Classification of Phase Transition Behavior in a Model of Evolutionary Dynamics" (2012). Theses. 274.
https://irl.umsl.edu/thesis/274