A
AgnosticBoy
Guest
STT,
Here’s further insight on the limitations of linear equations (your equation in post #1) to describe complex and/or unpredictable behaviors, such as emergent behaviors. It provides a good context of why physicists and other scientists rely on nonlinear equations to describe such behaviors.
Good article that expands on nonlinear vs. linear equations by MacGregor Campbell…learner.org/courses/mathilluminated/units/13/textbook/02.php:
Excerpts:
Here’s further insight on the limitations of linear equations (your equation in post #1) to describe complex and/or unpredictable behaviors, such as emergent behaviors. It provides a good context of why physicists and other scientists rely on nonlinear equations to describe such behaviors.
Good article that expands on nonlinear vs. linear equations by MacGregor Campbell…learner.org/courses/mathilluminated/units/13/textbook/02.php:
Excerpts:
In high school, we learned that a linear equation is any expression of the form y = mx + b, with m and b representing constants (such as 3 and -7) and x and y representing variables, generally called the independent and dependent variables, respectively.** The equation is “linear” because its graph (all the “x,y” points on the coordinate plane that satisfy the equation) is a straight line**, and also because a small change in the value of x effects a proportional, constant change in y. A **nonlinear equation **is something that doesn’t have just a first power of the independent variable and consequently can’t be graphed as a simple straight line. One such example is a quadratic equation, ax2 + bx + c = 0.
The distinction between linear and nonlinear systems in mathematics defines the boundary between the relatively knowable, and the frustratingly elusive. Both types of systems can describe the dynamics of many different processes, such as planets orbiting each other, fluctuations in animal populations, the behavior of electrical circuits, and so on. The difference between linear and nonlinear lies in the details of the equations that govern how these systems interact. For systems that behave linearly, it is relatively easy to find exact solutions that we can use to predict future behavior within the system. For nonlinear systems, we are lucky to find any such solution. Indeed, in nonlinear dynamics , we often have to redefine what we consider to be a solution. Before we get to this new view of solutions, however, let’s take a closer look at the older, linear view.
A mass on a spring is an example of a simple harmonic oscillator, a well-understood linear system.
•Linear systems can be solved relatively simply because they can be broken down into parts that can be solved separately.
• A pendulum swinging outside of the small-angle approximation, where sin θ ~ θ, is an example of a nonlinear system.
•For small swings, a pendulum behaves predictably, but for large swings, it can behave strangely.
…These so-called nonlinear systems can exhibit some wild behaviors, behaviors that might be considered surprising, behaviors that don’t fit so nicely into equations. For example, our simple pendulum behaves very smoothly and predictably as long as it doesn’t swing too high.
https://www.learner.org/courses/mathilluminated/images/units/13/1761.png
For larger and larger angles, the range of possible behaviors is more varied than the simple cycling back and forth. For example, if the pendulum has sufficient momentum, it will swing past the horizontal line of the pivot and go all the way around, over the top. If it has a little less momentum than this, it might stall near the vertical position above the pivot, lose the tension of the string, and drop almost straight down under the influence of gravity. Both of these behaviors are examples of nonlinearities. It’s worth noting that for a pendulum to swing higher than its pivot, the mass must have some initial velocity. Velocity due to gravity alone will not suffice. Since we are only concerned with general methods and qualitative behavior, we can ignore this.
Some nonlinear systems do behave nicely and predictably, while others do not. The range of nonlinear behaviors is vast, with chaos being just one type. It’s the type that we understand the best.
From the scientific journal Nature:
or counterintuitive, and yet their behaviour is not random.Nonlinear dynamics is the branch of physics that studies systems governed by equations more complex than the linear, aX+b form. Nonlinear systems, such as the weather or neurons, often appear chaotic, unpredictable