Empirical data sets are algorithmically compressible: reply to McAllister?

Twardy, Charles; Gardner, Steven Lawrence; Dowe, David L.

doi:10.1016/j.shpsa.2005.04.004

Cited by 12 publications

(2 citation statements)

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“…(For some examples see Korb and nicholson 2004: ch. 8;dowe et al 2007;and twardy et al 2005. ) Peter Walley's (Ph.d. uWa) monograph, Statistical Reasoning with Imprecise Probabilities, is an influential extension of the bayesian theory of robustness, focussing on that much-discussed achilles heel of bayesianism, prior probability distributions (Walley 1991).…”

Section: Neil Thomasonmentioning

confidence: 99%

A Companion to Philosophy in Australia and New Zealand (Second Edition)

Oppy¹,

Trakakis²

2014

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Section: Neil Thomasonmentioning

confidence: 99%

A Companion to Philosophy in Australia and New Zealand (Second Edition)

Oppy¹,

Trakakis²

2014

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“…However, the best argument against McAllister's historical argument is to actually find examples of scientific practice where lossless compression takes place. Such examples have previously been given by Twardy, Gardner and Dowe (2005). They argue that minimum message length (MML) compression is used in a range of scientific disciplines to compress empirical data:…”

Section: The Historical Argumentmentioning

confidence: 99%

Compressibility and the Algorithmic Theory of Laws

Wheeler¹

2019

Principia

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The algorithmic theory of laws claims that the laws of nature are the algorithms in the best possible compression of all empirical data. This position assumes that the universe is compressible and that data received from observing it is easily reproducible using a simple set of rules. However, there are three sources of evidence that suggest that the universe as a whole is incompressible. The first comes from the practice of science. The other two come from the nature of the universe itself: the presence of chaotic behavior and the nature of quantum systems also suggests that the universe is incompressible. This paper evaluates these sources and argues that none provides a convincing case to reject the algorithmic theory of laws.

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The Algorithmic Theory of Laws

Wheeler

2018

Idealization and the Laws of Nature

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Empirical data sets are algorithmically compressible: reply to McAllister?

Cited by 12 publications

References 10 publications

A Companion to Philosophy in Australia and New Zealand (Second Edition)

A Companion to Philosophy in Australia and New Zealand (Second Edition)

Compressibility and the Algorithmic Theory of Laws

The Algorithmic Theory of Laws

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