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PT 5: To what extent do you agree with the claim "all models are…
PT 5: To what extent do you agree with the claim "all models are wrong, but some are useful" (attributed to George Box)? Discuss with reference to Mathematics and one other AOK
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Arguments
For
models like statistical distributions are based on assumptions, they can predict real-world phenomena with reasonable accuracy
simplified models are necessary for solving practical problems, despite limitations in representing reality
Models allow for hypotheses to be tested in real-world scenarios, enabling progression for us even if the model is not fully accurate
models are often improving as new data and technology emerge. This process helps them become “less wrong” over time
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Againts
models can oversimplify systems, potentially misleads/ incorrect conclusions
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the use of "wrong" models in critical areas (e.g., medicine) can have harmful/ethical consequences
Over-reliance on models can lead to false confidence in their results, potentially causing misinformed decisions
Some models lack predictive power because they cannot account for all possible variables or uncertainties in the real-world
the predictive abilities of models can be overstated, leading to overconfidence. When models fail, the fallout can be devastating
Raising questions
Can models ever truly represent the real world, or are they always bound by the limitations
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How do experts decide which aspects of a model’s limitations are acceptable when making real-world predictions?
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understanding the title
But some are useful”
Despite their imperfections, models can still be valuable if they provide insights, predict outcomes etc
“All models are wrong”
Models are simplifications of reality, which means they can never perfectly represent the real world thus why no model can be entirely correct
AOK
Mathematics
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Mathematical models, such as algorithms and equations, can predict outcomes with a high degree of certainty (e.g., in physics, finance)
mathematical models still provide powerful tools for engineering, physics, and economics.
Natural science
Models are used to test hypotheses and predict future results (e.g., Newtonian physics, climate change models)
Models in natural sciences simplify complex systems (e.g., climate, ecosystems) to make them understandable and predictable.
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Assumptions
Models assume that natural laws are universally applicable , though this may not always hold
Scientific models assume controlled environments, which may not represent the chaotic and unpredictable nature of the real world
Models
applications/utility
widely used for forecasting future events, such as weather predictions, stock market trends, and public health outcomes (e.g., disease spread)
Engineers use models to simulate and analyze structures and systems, ensuring safety and efficiency before physical implementation
models serve as educational tools, helping students and professionals understand complex concepts through visualization and simulation
limitations
Many models cannot capture all aspects of the phenomenon they aim to represent, leading to gaps in understanding
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may simplify complex phenomena to the point that they miss critical factors or interactions, leading to misleading results
Many models fail to account for how systems change over time,
The application of models in fields like healthcare or criminal justice can lead to ethical dilemmas