Version 1
: Received: 6 July 2024 / Approved: 8 July 2024 / Online: 8 July 2024 (12:36:47 CEST)
How to cite:
Montgomery, R. Simulation and Statistical Analysis of Population Dynamics in Evolutionary Systems: A Simulated Study. Preprints2024, 2024070621. https://doi.org/10.20944/preprints202407.0621.v1
Montgomery, R. Simulation and Statistical Analysis of Population Dynamics in Evolutionary Systems: A Simulated Study. Preprints 2024, 2024070621. https://doi.org/10.20944/preprints202407.0621.v1
Montgomery, R. Simulation and Statistical Analysis of Population Dynamics in Evolutionary Systems: A Simulated Study. Preprints2024, 2024070621. https://doi.org/10.20944/preprints202407.0621.v1
APA Style
Montgomery, R. (2024). Simulation and Statistical Analysis of Population Dynamics in Evolutionary Systems: A Simulated Study. Preprints. https://doi.org/10.20944/preprints202407.0621.v1
Chicago/Turabian Style
Montgomery, R. 2024 "Simulation and Statistical Analysis of Population Dynamics in Evolutionary Systems: A Simulated Study" Preprints. https://doi.org/10.20944/preprints202407.0621.v1
Abstract
This project aims to simulate and analyze population dynamics in evolutionary systems, using stochastic differential equations and probabilistic rules for crossbreeding and benefits. The study employs advanced computational techniques to model the evolution of populations over an extended period. Specifically, we explore a scenario with ten distinct groups, each starting with an equal population and evolving under defined genetic and cooperative behaviors. The simulation spans 1000 years, incorporating individual lifespans, stochastic updates, and probabilistic interactions. To ensure robustness and reliability, the study includes detailed statistical analysis, encompassing descriptive statistics, confidence intervals, and ANOVA tests. We further visualize the temporal behavior of populations and subpopulations, providing insights into the evolutionary dynamics and stability of the system. The results demonstrate the effectiveness of balanced cooperation and genetic diversity in maintaining stable population distributions. The project showcases the integration of mathematical modeling, statistical analysis, and computational simulations, contributing valuable insights into evolutionary biology and complex systems. It highlights the importance of interdisciplinary approaches in understanding and predicting population behaviors, with implications for both theoretical research and practical applications in fields such as ecology, genetics, probability and social sciences.
Keywords
Keywords: Population Dynamics, Evolutionary Systems, Partial Differential Stochastic Equations, Simulation
Subject
Computer Science and Mathematics, Applied Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.