Tootfinder

The Nature of Reasoning in Theology, Philosophy, and Mathematics
Chad R. Mangum
https://arxiv.org/abs/2508.20297 https://arxiv.org/pdf/2508.20297

The Nature of Reasoning in Theology, Philosophy, and Mathematics
This article supports the epistemological claim that sound human reasoning about ultimate knowledge is either foundational or circularly justified. In particular, questions which naturally arise in theology, philosophy, and related disciplines, to the extent that they rationally treat ultimate knowledge, are necessarily supported in one of these ways. Comparisons with, contrasts to, and analogies from mathematics are given to illustrate and enhance this central claim.

What is digital about abstraction?
Bernhard Rieder
https://arxiv.org/abs/2508.18181 https://arxiv.org/pdf/2508.18181

What is digital about abstraction?
This chapter examines abstraction as a central principle of computing, not merely as a cognitive skill or epistemological category, but as a material and organizational practice that structures how software is built, used, and embedded in society. By tracing abstraction through historical developments in programming, operating systems, and networking, the text highlights its dual role in enabling modularity and layering while simultaneously shaping cultural, economic, and organizational forms. …

Onto-Epistemological Analysis of AI Explanations
Martina Mattioli, Eike Petersen, Aasa Feragen, Marcello Pelillo, Siavash A. Bigdeli
https://arxiv.org/abs/2510.02996 https://

Onto-Epistemological Analysis of AI Explanations
Artificial intelligence (AI) is being applied in almost every field. At the same time, the currently dominant deep learning methods are fundamentally black-box systems that lack explanations for their inferences, significantly limiting their trustworthiness and adoption. Explainable AI (XAI) methods aim to overcome this challenge by providing explanations of the models' decision process. Such methods are often proposed and developed by engineers and scientists with a predominantly technical bac…

We're Still Doing It (All) Wrong: Recommender Systems, Fifteen Years Later
Alan Said, Maria Soledad Pera, Michael D. Ekstrand
https://arxiv.org/abs/2509.09414 https://

We're Still Doing It (All) Wrong: Recommender Systems, Fifteen Years Later
In 2011, Xavier Amatriain sounded the alarm: recommender systems research was "doing it all wrong" [1]. His critique, rooted in statistical misinterpretation and methodological shortcuts, remains as relevant today as it was then. But rather than correcting course, we added new layers of sophistication on top of the same broken foundations. This paper revisits Amatriain's diagnosis and argues that many of the conceptual, epistemological, and infrastructural failures he identified still persist, …

The Geometry of Motion, Vol. I: Mechanics as Geometries
Patrick Iglesias-Zemmour
https://arxiv.org/abs/2508.12810 https://arxiv.org/pdf/2508.12810

The Geometry of Motion, Vol. I: Mechanics as Geometries
This work presents a group-theoretic interpretation of the historical evolution of mechanics, proposing that each fundamental theory of motion corresponds to a distinct geometry in the sense of Felix Klein. The character of each geometry is uniquely determined by its Inertia Group-the group of spacetime transformations that preserves its privileged class of inertial motions. We trace the three major epistemological ruptures in the history of mechanics by translating foundational physical princi…

Replaced article(s) found for cs.SI. https://arxiv.org/list/cs.SI/new
[1/1]:
- Community as a Vague Operator: Epistemological Questions for a Critical Heuristics of Community D...
Juni Schindler, Matthew Fuller

Predictive economics: Rethinking economic methodology with machine learning
Miguel Alves Pereira
https://arxiv.org/abs/2510.04726 https://arxiv.org/pdf/251…

Predictive economics: Rethinking economic methodology with machine learning
This article proposes predictive economics as a distinct analytical perspective within economics, grounded in machine learning and centred on predictive accuracy rather than causal identification. Drawing on the instrumentalist tradition (Friedman), the explanation-prediction divide (Shmueli), and the contrast between modelling cultures (Breiman), we formalise prediction as a valid epistemological and methodological objective. Reviewing recent applications across economic subfields, we show how…