It is a fact that the larger the amount of defective (vague, partial, conflicting, inconsistent) information is, the more challenges scientists face when working with it. Here, I address the question of whether there is anything special about the ignorance involved in big data practices. I submit that the ignorance that emerges when using big data in the empirical sciences is ignorance of theoretical structure with reliable consequences and I explain how this ignorance relates to different epistemic achievements such as knowledge and understanding. I illustrate this with a case study from observational cosmology.
Inconsistency toleration is the phenomenon of working with inconsistent information without threatening one's rationality. Here I address the role that ignorance plays for the tolerance of contradictions in the empirical sciences. In particular, I contend that there are two types of ignorance that, when present, can make epistemic agents to be rationally inclined to tolerate a contradiction. The first is factual ignorance, understood as temporary undecidability of the truth values of the conflicting propositions. The second is what I call "ignorance of theoretical structure", which is lack of knowledge of relevant inference patterns within a specific theory. I argue that these two types of ignorance can be explanatory of the scientists' rational disposition to be tolerant towards contradictions, and I illustrate this with a case study from neutrino physics.
Two of the most important outcomes of The Contradictory Christ include: (i) identifying Christ as an unproblematically contradictory being as well as (ii) laying the foundations of an This work is the result of the book symposium on Jc Beall's The Contradictory Christ.
Here I argue that scientists can achieve some understanding of both the products of big data implementation as well as of the target phenomenon to which they are expected to refer—even when these products were obtained through essentially epistemically opaque processes. The general aim of the paper is to provide a road map for how this is done; going from the use of big data to epistemic opacity (Sec. 2), from epistemic opacity to ignorance (Sec. 3), from ignorance to insights (Sec. 4), and finally, from insights to understanding (Sec. 5, 6)
Like theories, reconstructions of episodes in the history of science can possess, or lack, certain virtues such that, when we face two or more different reconstructions of the same episode, we assume that we should choose the most “virtuous one”. However, we will argue that, with dissimilar reconstructions of the same episode, it is not always necessary to separate the “good ones” from the “wrong ones”, and that, as a matter of fact, each reconstruction could provide different but perhaps equally relevant data about the episode, about science in general, and about particular philosophical theses. In order to help us to identify these benefits, we will present a criterion that guides the search for historiographical reinforcement of philosophical theses and we will use it to evaluate three different reconstructions of the same scientific episode.
Defendemos aqui que o estudo da tolerância à inconsistência nas ciências so-ciais - especificamente, a tolerância às contradições entre teoria e observação - pode ser revelador no que diz respeito aos mecanismos que fundamentam o uso racional de informações inconsistentes. Para fazer isso, oferecemos uma tipologia de contradições desse tipo. Enfatizamos alguns dos aspectos salien-tes dessas contradições, e defendemos que esses aspectos desempenham um papel importante na seleção de mecanismos inferenciais que permitem a to-lerância às contradições. Ilustramos isso com casos econômicos.
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