This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.
We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale website.
A decent match to the data is made with Wolf Death Rate = 0.15 Wolf Birth Rate Factor = 0.0203 Moose Death Rate Factor = 1.08 Moose Birth Rate = 0.4 Carrying Capacity = 2000 Initial Moose: 563 Initial Wolves: 20
I used RK-4 with step-size 0.1, from 1959 for 60 years.
The moose birth flow is MBR*M*(1-M/K) Moose death flow is MDRF*Sqrt(M*W) Wolf birth flow is WBRF*Sqrt(M*W) Wolf death flow is WDR*W
A More Realistic Model of Isle Royale: Predator Prey Interactions
A combination of qualitative and quantitative methods for implementing a systems approach, including virtual intervention experiments using computer simulation models. See also Complex Decision Technologies IM
Interventions and leverage points added in IM-1400 (complex!)
Based on a book chapter by Rosemarie Sadsad based on her PhD Thesis. See also other Insights tagged Multiscale and Realist ( IM-3546 and IM-3834 are embedded here)
Causal Loop and Summary of Klein and Kahneman's American Psychologist 2009 article with 2024 Gigerenzer The Rationality Wars article update, assisted by my Gemini interaction April2026 using Gene Bellinger's AI prompts
Simple box model for atmospheric and ocean carbon cycle, with surface and deep water, including DIC system, carbonate alkalinity, weathering, O2, and PO4 feedbacks.
Without mortality, with time measured in days, with infection rate 1/2, recovery rate 1/3, and initial infectious population I_0=1.27x10-4, we reproduce their figure
With a death rate of .005 (one two-hundredth of the infected per day), an infectivity rate of 0.5, and a recovery rate of .145 or so (takes about a week to recover), we get some pretty significant losses -- about 3.2% of the total population.
