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The logistic map is a simple example of how chaos can form from very simple non-linear equations. While it is famous in physics for providing a qualitative map of the transition from order to chaos, its origins are rooted in biology. In the mid-1970s, biologist Robert May popularized this equation as a discrete-time model for animal populations. The variable x represents the population at a given time, scaled between 0 (extinction) and 1 (maximum carrying capacity). The equation x_n+1 = r * x_n * (1 - x_n) works through a simple feedback loop:

• Growth (r * x_n): This represents the birth rate. When the population is small, it grows proportionally to how many individuals are already there.
• Starvation (1 - x_n): This represents the "limiting factor," such as food scarcity or disease. As the population x approaches 1 (the environment's limit), this term approaches zero, slowing down or crashing the population.

May’s breakthrough was showing that a single, deterministic biological rule could lead to stable equilibriums, predictable cycles, or total, unpredictable chaos just by changing the growth rate.

Its relation to fluids is that it is a simple handy comparison with laminar and turbulent flows. The growth rate parameter r acts as a mathematical proxy for the Reynolds number. Just as increasing the Reynolds number pushes a fluid from a steady, predictable state toward total chaos, increasing r drives the map through distinct phases:

• Steady State (r < 3): This represents laminar flow. No matter where the population starts, the system eventually settles into a single, stable value.
• Periodic Oscillation (3 < r < 3.57): As r increases, the system undergoes "period-doubling." The value begins to bounce between two points, then four, then eight. In fluid terms, this is analogous to the onset of instabilities, like the rhythmic shedding of vortices in a von Karman vortex street.
• Deterministic Chaos (r > 3.57): The system enters a regime of turbulence. The values appear random and never repeat, yet they are governed by a strictly deterministic equation, like the Navier-Stokes equations.

In the turbulent phase, two starting values, which we can consider some arbitrary initial fluid conditions that differ by even a microscopic amount, will eventually diverge into completely different paths. This sensitivity is exactly why predicting high-Reynolds number turbulent flows is so difficult. Small errors in the initial state can lead to vastly different results over time.

This simulation models a Plinko board, which nicely demonstrates the Central Limit Theorem. At every peg, a falling ball effectively flips a coin to decide whether to bounce left or right.

The bell curve emerges because of path combinatorics. There are far more ways for a ball to reach the center bins than the edges, ending up on the far side requires a rare "perfect streak" of turns in a single direction. In contrast, landing in the center can happen through many different left-right combinations.

This random process naturally results in a Gaussian distribution. You can use the Skew slider to rig the coin flip, shifting the probability to see how bias affects the final expected outcome of the distribution.