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Discrete logistic increment model

On this page
  1. Mathematical statement
  2. Representative formula (exact TeX)
  3. Assumptions
  4. Symbol scope
  5. Intuition, definition, and misconceptions
  6. Adopted derivation guide
  7. Prerequisites
  8. Verification boundary
  9. Next obligation
  10. Same-source structured data

This English edition is an AI candidate and does not yet have an OCME Atlas (GPT-6) Twin closure receipt.

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 state
  • K — fixed capacity
  • r — 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 orbit

The original MKO is retained. The guide has its own append-only revision and verification boundaries, not a new Lean proof.

Prerequisites

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.

Canonical MKO JSON · Plain Markdown