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Nonlinear 

Not simple, not easy to resolve. Comes from math: whenever you see the word "linear", you know it's easy.

This word is not restricted to use in math.
y'' = -sin(y) <- The nonlinear pendulum.
y'' = -y <- Linearized (nonrealistic) pendulum.

"meselfs, I have girl troubles. I have no friends so I'm asking you, a known dateless loser."
"Sounds pretty nonlinear. Unfortunately, I can only suggest you go tensile test yourself."
Nonlinear by meselfs May 22, 2005

nonlinear catastrophic structural exasperation 

The sudden, unexplainable, transition of wood, metal, plastic, concrete into an explosive state.

Example: OH NOES!! MY DESK IS SUFFERING FROM NONLINEAR CATASTROPHIC STRUCTURAL EXASPERATION!!!
Example: OH NO!! My desk is suffering from nonlinear catastrophic structural exasperation!!!

nonlinear response 

A reaction that is out of measure with its impetus.
Kyle: "Hey Linda, have you got the time?"
Linda: "Eat a dick Kyle"
Brian: "nonlinear response there Linda. Its 9:30 Kyle"
nonlinear response by jake jeckle December 19, 2008

Nonlinear Systems

Systems where the output is not proportional to the input—where small changes can produce huge effects, and large changes can produce tiny effects. Nonlinear Systems are the norm in reality: ecosystems, economies, bodies, societies. They're characterized by thresholds, feedback loops, and emergence. Unlike linear systems, which are predictable and controllable, nonlinear systems are wild, surprising, and often uncontrollable. Nonlinear Systems theory is the foundation of complexity thinking, the recognition that we live in a world where cause and effect are not simple, where interventions backfire, where prediction is hard. It's the mathematics of humility, the proof that the world is not a machine.
Example: "He thought management was linear: more pressure, more output. But the team was a nonlinear system: at some threshold, pressure caused collapse, not productivity. Nonlinear Systems theory explained why his simple model failed: the world doesn't do proportional. He had to learn to think differently—or keep breaking things."

Nonlinear Epistemology

The theory that knowledge itself operates nonlinearly—that small insights can produce huge shifts in understanding, that large amounts of information can produce no learning, that what we know depends sensitively on where we start. Nonlinear Epistemology argues that learning is not cumulative but transformative, that paradigms shift suddenly, that understanding leaps rather than grows. It's the epistemology of Black Swans, of scientific revolutions, of personal transformations. The theory explains why education often fails (it assumes linear accumulation), why debates are so hard (positions are nonlinear, not easily shifted by evidence), why some insights change everything and others change nothing. Nonlinear Epistemology is the study of how we know in a nonlinear world.
Example: "He'd been adding facts for years, thinking knowledge was cumulative. Nonlinear Epistemology showed him otherwise: real understanding came in leaps, not increments. A single insight could reorganize everything; years of study could produce nothing. He stopped hoarding facts and started seeking transformations."

Nonlinear Science

The branch of science that studies nonlinear phenomena—systems where output is not proportional to input, where small causes have large effects, where prediction is hard. Nonlinear Science includes chaos theory, complexity theory, and the study of emergent phenomena. It's the science of the real world, as opposed to the simplified linear models that dominated 20th-century science. Nonlinear Science explains why weather is unpredictable, why ecosystems are fragile, why economies crash. It's the scientific foundation of humility, the proof that the world is more complicated than our models.
Example: "He'd been trained in linear science—simple causes, simple effects, simple predictions. Nonlinear Science showed him a different world: chaos, emergence, thresholds. Weather wasn't predictable; ecosystems weren't controllable; economies weren't stable. His old tools failed because the world wasn't linear. He had to learn new science—or stay wrong."