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Published on: March 31, 2023
Values and uncertainties in climate prediction, revisited
Philosophers debate the role of values in science. This study challenges the idea that climate change predictions are solely value-free, arguing social values influence probability assignments, though perhaps less than claimed.
Area of Science:
- Philosophy of Science
- Climate Science
- Environmental Ethics
Background:
- Ongoing debate on the role of social values in scientific research.
- Eric Winsberg's model-based challenge to the value-free ideal in science.
- Focus on the influence of values in climate change prediction models.
Purpose of the Study:
- To evaluate Winsberg's argument regarding social values in climate change probability assignments.
- To examine the extent to which social values influence uncertainty estimates in climate prediction.
- To explore alternative challenges to the value-free ideal in the context of climate science.
Main Methods:
- Critical analysis of Eric Winsberg's model-based argument.
- Formulation of objections to Winsberg's claims on value influence.
- Comparison of Winsberg's argument with traditional challenges to the value-free ideal.
Main Results:
- Two objections are raised against Winsberg's argument.
- The objections suggest Winsberg may exaggerate the influence of social values on climate uncertainty.
- A traditional challenge to the value-free ideal is presented as highly relevant to climate science.
Conclusions:
- Winsberg's argument highlights the potential influence of social values in climate modeling.
- The influence of social values on climate prediction uncertainty may be less pervasive than Winsberg suggests.
- Traditional philosophical challenges to the value-free ideal offer a robust framework for analyzing values in climate science.
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Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.