arXiv subjects

Ricardo P. Cavassane

Publications and source records attributed to Ricardo P. Cavassane.

2 recordsLinked to original sources

arXiv2024

Pragmatic Nonsense

Inspired by the early Wittgenstein's concept of nonsense (meaning that which lies beyond the limits of language), we investigate two different types of nonsense: formal nonsense and pragmatic nonsense. The simpler notion of formal nonsense is defined in accordance with Tarski's semantic theory of truth; the notion of pragmatic nonsense is in turn formulated within the context of the theory of pragmatic truth, also known as quasi-truth, as formalized by da Costa and his collaborators. Pragmatic nonsense extends formal nonsense, the same way da Costa's pragmatic truth is an extension of Tarski's definition of truth. An expression is thus considered formally nonsensical in case the formal criteria required for the assignment of any truth-value (whether true, false, pragmatically true, or pragmatically false) are not met; and an expression, or even a well-formed formula, is considered pragmatically nonsensical if either the formal or the pragmatic criteria of relevance (inscribed within the context of scientific practice) required for the assignment of any pragmatic truth-value (pragmatically true or pragmatically false) are not met. We also introduce the concept of strictly pragmatic truth, which excludes pragmatic nonsense and necessarily depends on certain criteria of relevance, unlike the original definition of pragmatic truth/quasi-truth.

math.LO↗

arXiv2024

A simplicity bubble problem and zemblanity in digitally intermediated societies

In this article, we discuss the ubiquity of Big Data and machine learning in society and propose that it evinces the need of further investigation of their fundamental limitations. We extend the ``too much information tends to behave like very little information'' phenomenon to formal knowledge about lawlike universes and arbitrary collections of computably generated datasets. This gives rise to the simplicity bubble problem, which refers to a learning algorithm equipped with a formal theory that can be deceived by a dataset to find a locally optimal model which it deems to be the global one. In the context of lawlike (computable) universes and formal learning systems, we show that there is a ceiling above which formal knowledge cannot further decrease the probability of zemblanitous findings, should the randomly generated data made available to the formal learning system be sufficiently large in comparison to their joint complexity. Zemblanity, the opposite of serendipity, is defined by an undesirable but expected finding that reveals an underlying problem or negative consequence in a given model or theory, which is in principle predictable in case the formal theory contains sufficient information. We also argue that this is an epistemological limitation that may generate unpredictable problems in digitally intermediated societies.

cs.IT↗