Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/257554 
Year of Publication: 
2021
Citation: 
[Journal:] Games [ISSN:] 2073-4336 [Volume:] 12 [Issue:] 4 [Article No.:] 72 [Publisher:] MDPI [Place:] Basel [Year:] 2021 [Pages:] 1-19
Publisher: 
MDPI, Basel
Abstract: 
Classical evolutionary game theory allows one to analyze the population dynamics of interacting individuals playing different strategies (broadly defined) in a population. To expand the scope of this framework to allow us to examine the evolution of these individuals' strategies over time, we present the idea of a fitness-generating (G) function. Under this model, we can simultaneously consider population (ecological) and strategy (evolutionary) dynamics. In this paper, we briefly outline the differences between game theory and classical evolutionary game theory. We then introduce the G function framework, deriving the model from fundamental biological principles. We introduce the concept of a G-function species, explain the process of modeling with G functions, and define the conditions for evolutionary stable strategies (ESS). We conclude by presenting expository examples of G function model construction and simulations in the context of predator-prey dynamics and the evolution of drug resistance in cancer.
Subjects: 
G function
adaptive landscape
eco-evolutionary dynamics
ESS
evolutionary game theory
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
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