The Influence of Demographic Stochasticity on Population Dynamics

A Mathematical Study of Noise-Induced Bistable States and Stochastic Patterns

Nonfiction, Science & Nature, Science, Other Sciences, System Theory, Mathematics, Statistics, Social & Cultural Studies, Social Science
Cover of the book The Influence of Demographic Stochasticity on Population Dynamics by Tommaso Biancalani, Springer International Publishing
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Tommaso Biancalani ISBN: 9783319077284
Publisher: Springer International Publishing Publication: June 4, 2014
Imprint: Springer Language: English
Author: Tommaso Biancalani
ISBN: 9783319077284
Publisher: Springer International Publishing
Publication: June 4, 2014
Imprint: Springer
Language: English

The dynamics of population systems cannot be understood within the framework of ordinary differential equations, which assume that the number of interacting agents is infinite. With recent advances in ecology, biochemistry and genetics it is becoming increasingly clear that real systems are in fact subject to a great deal of noise. Relevant examples include social insects competing for resources, molecules undergoing chemical reactions in a cell and a pool of genomes subject to evolution. When the population size is small, novel macroscopic phenomena can arise, which can be analyzed using the theory of stochastic processes. This thesis is centered on two unsolved problems in population dynamics: the symmetry breaking observed in foraging populations and the robustness of spatial patterns. We argue that these problems can be resolved with the help of two novel concepts: noise-induced bistable states and stochastic patterns.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

The dynamics of population systems cannot be understood within the framework of ordinary differential equations, which assume that the number of interacting agents is infinite. With recent advances in ecology, biochemistry and genetics it is becoming increasingly clear that real systems are in fact subject to a great deal of noise. Relevant examples include social insects competing for resources, molecules undergoing chemical reactions in a cell and a pool of genomes subject to evolution. When the population size is small, novel macroscopic phenomena can arise, which can be analyzed using the theory of stochastic processes. This thesis is centered on two unsolved problems in population dynamics: the symmetry breaking observed in foraging populations and the robustness of spatial patterns. We argue that these problems can be resolved with the help of two novel concepts: noise-induced bistable states and stochastic patterns.

More books from Springer International Publishing

Cover of the book Technology Trends by Tommaso Biancalani
Cover of the book Computer Science -- Theory and Applications by Tommaso Biancalani
Cover of the book Topographies of Memories by Tommaso Biancalani
Cover of the book Advances in Computational Intelligence Systems by Tommaso Biancalani
Cover of the book Semantics of Complex Words by Tommaso Biancalani
Cover of the book Viscous Flows by Tommaso Biancalani
Cover of the book Inflammation and Oxidative Stress in Neurological Disorders by Tommaso Biancalani
Cover of the book Human Agency and Behavioral Economics by Tommaso Biancalani
Cover of the book Key Factors of Combustion by Tommaso Biancalani
Cover of the book Emerging Zoonoses by Tommaso Biancalani
Cover of the book Integration as Solution for Advanced Smart Urban Transport Systems by Tommaso Biancalani
Cover of the book Combinatorial Optimization and Applications by Tommaso Biancalani
Cover of the book Peptides and Peptide-based Biomaterials and their Biomedical Applications by Tommaso Biancalani
Cover of the book Stratification, Rollover and Handling of LNG, LPG and Other Cryogenic Liquid Mixtures by Tommaso Biancalani
Cover of the book Label-free and Multi-parametric Monitoring of Cell-based Assays with Substrate-embedded Sensors by Tommaso Biancalani
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy