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Machine learning (ML) is a data-driven modeling approach that has become popular in recent years, thanks to major advances in software and hardware. Given enough data about a complex system, ML allows a computer model to imitate that system and predict its behavior. Unlike a deductive modeling approach, which requires some understanding of a system to be able to predict its behavior, the inductive approach of ML can predict the behavior of a system without ever understanding it in a traditional sense. Climate is a complex system, but there is not enough observed data describing an unprecedented event like global warming on which a computer model can be trained. Instead, it may be more fruitful to use ML to imitate a climate model, or a component of it, to greatly speed up computations. This will allow the parameter space of climate models to be explored more efficiently.

One of the main problems that Paul Horwich’s Minimalist theory of truth must face is the generalization problem, which shows that Minimalism is too weak to have the fundamental explanatory role Horwich claims it has. In this paper, I defend Horwich’s response to the generalization problem from an objection raised by Bradley Armour-Garb. I also argue that, given my response to Armour-Garb, Horwich’s proposal to cope with the generalization problem can be simplified.

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17 - Machine Learning

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Conceivability, Minimalism and the Generalization Problem

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2 results

17 - Machine Learning

Summary

Conceivability, Minimalism and the Generalization Problem