Discrete logistic increment model
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For fixed capacity K and 0≤r≤1, this entry uses x_(n+1)=x_n+r*x_n*(1−x_n/K) to describe each capacity-limited discrete increment.
mko-discrete-logistic-growth · definition · v0.10.0
Mathematical statement
This discrete logistic increment model fixes K>0 and 0≤r≤1 and updates x_(n+1)=x_n+r*x_n*(1−x_n/K). If 0≤x_n≤K, the next state remains in [0,K]; 0 and K are fixed points. This is the specified discrete model, not an analytic solution of the continuous logistic equation.
Representative formula (exact TeX)
x(n+1)=G(x(n))
Assumptions
- Time step, K and r remain fixed; the initial state lies in [0,K] and every computation uses exact rationals.
- Exercises expand only short finite orbits and infer no stability, convergence or real-population fit from them.
Symbol scope
x— model stateK— fixed capacityr— per-step increment parameter
Intuition, definition, and misconceptions
Near x=0 the capacity factor is near 1; at x=K the increment is zero.
The bounds 0≤r≤1 and 0≤x≤K make one update preserve the interval. This does not automatically cover other parameter domains or definitions of a discrete logistic model.
Real populations face randomness, time-varying capacity, migration and measurement effects. A fixed model’s finite orbit is not empirical validation.
Do not conflate different logistic-map forms or treat a short orbit’s trend as long-term stability or a measured forecast.
Adopted derivation guide
Discrete logistic increment model: invariance of [0,K], fixed points and a short orbitThe original MKO is retained. The guide has its own append-only revision and verification boundaries, not a new Lean proof.
Prerequisites
- Discrete exponential growth model — The logistic increment adds a capacity-suppression factor to fixed-factor growth.
- Sequence — Discrete time indexes the model states.
- Arithmetic operations — The update uses exact addition, subtraction, multiplication and division.
Verification boundary
This slice fixes the increment model and parameter domain; it asserts neither stability, convergence nor biological fit.
not_formalized
This does not mean the content is false; it means no replayable computational or formal evidence is registered yet.
Next obligation
Compare the standard logistic map, Euler discretization, equilibrium stability and data calibration.
Same-source structured data
This page and the interactive view use the same object and existing language edition; this adds no proof or human review.
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