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- DictionaryBut·ter·fly ef·fect/ˈbədərˌflaɪ əˈfɛkt/
noun
- 1. (in chaos theory) the phenomenon whereby a minute localized change in a complex system can have large effects elsewhere.
In chaos theory, the butterfly effect is the sensitive dependence on initial conditions in which a small change in one state of a deterministic nonlinear system can result in large differences in a later state.
Jun 9, 2023 · The butterfly effect is the idea that small, seemingly trivial events may ultimately result in something with much larger consequences – in other words, they have non-linear impacts on very complex systems.
The meaning of BUTTERFLY EFFECT is a property of chaotic systems (such as the atmosphere) by which small changes in initial conditions can lead to large-scale and unpredictable variation in the future state of the system.
The butterfly effect is the idea that small things can have non-linear impacts on a complex system. The concept is imagined with a butterfly flapping its wings and causing a typhoon.
Jun 30, 2023 · What is the butterfly effect in chaos theory? Can the flap of a butterfly wing alter the weather? You can find detailed info in this article.
It is a concept used in chaos theory, an area of mathematics, to illustrate how small changes can lead to enormous effects over time. The term itself comes from an example used to explain this concept: a butterfly flapping its wings in Brazil could cause a tornado in Texas.
May 5, 2015 · The answer may lie in the butterfly effect, the discovery that initial conditions can have disproportionately large effects over time—e.g. the beating of butterfly wings.
Nov 9, 2023 · The Butterfly Effect is a concept derived from chaos theory, which illustrates how small changes in a complex system can lead to significant and unpredictable consequences over time.
People who search for beauty in imperfection and dissonance -- precisely the kinds of patterns that chaotic systems leave behind, including the disturbed pendulum's lawless path. Eleonora...
The butterfly effect emphasizes the need for caution when assessing complex systems and predicting their behavior. It signifies the limitations and challenges associated with precise long-term forecasting due to the ever-present potential for small perturbations to cause dramatic shifts.
