Uumarrty: Agent Based Simulation Model of Predator Prey Interactions with a Game Theoretical Framework

Abstract

ABSTRACTEvolutionary game theory boasts a rich history of application in the study of phenotypic evolution and behavioral strategies. Complementing this, agent-based modeling offers a powerful simulation framework that allows for explicit modeling and parameterization of individual agents within a virtual environment. The agent-based modelling approach empowers ecologists and evolutionary biologists to accurately model highly complex and dynamic systems. Leveraging these methodologies, we developed the agent-based model Uumarrty, which integrates concepts from game theory to investigate the evolution of behavioral phenotypes. Our model embraces essential game theoretical principles, including pay-offs, mixed versus pure strategies, and optimal behavioral strategies, seamlessly within the agent-based framework. Furthermore, we introduced a novel metric called the Nash score, which serves as an analog of the evolutionarily stable strategy within our agent-based environment. With this innovative approach, researchers gain a versatile tool to comprehensively explore optimal behavioral strategies and assess their evolutionary stability. To demonstrate the efficacy of our model, we applied it to a predator-prey system involving kangaroo rats and rattlesnakes. By examining various factors such as differing foraging gains by kangaroo rats, varying strike success of rattlesnakes by microhabitat, and the influence of introducing a second predator, we gained valuable insights into the evolution of microhabitat preferences within this predator-prey system.