Conceptual Analysis in Metaethics

A critical survey of various positions on the nature, use, possession, and analysis of normative concepts. We frame our treatment around G.E. Moore’s Open Question Argument, and the ways metaethicists have responded by departing from a Classical Theory of concepts. In addition to the Classical Theory, we discuss synthetic naturalism, noncognitivism (expressivist and inferentialist), prototype theory, network theory, and empirical linguistic approaches. Although written for a general philosophical audience, we attempt to provide a new perspective and highlight some underappreciated problems about normative concepts

Physicalism, conceptual analysis, and acts of faith

Frank Jackson and the author each take the other to hold a position in philosophy of mind that it is extremely difficult to sustain. This chapter tries to say something about how that can be. It seeks to demonstrate the sanity of Jackson's opponents and the fragility of his own position than to hold out for the truth of any particular doctrine. It wants to bring to the surface an assumption in ontology, which is seen as a crucial part of the grounding of Jackson's particular version of physicalism. Once it is appreciated that this assumption is contestable, Jackson's opponents may be seen in a different light from the one in which they appear in his writings. More generally, a connection will appear between the vast literature on physicalism as a topic in philosophy of mind and the equally vast literature on material constitution as a topic in metaphysics

Detecting Large Concept Extensions for Conceptual Analysis

When performing a conceptual analysis of a concept, philosophers are interested in all forms of expression of a concept in a text---be it direct or indirect, explicit or implicit. In this paper, we experiment with topic-based methods of automating the detection of concept expressions in order to facilitate philosophical conceptual analysis. We propose six methods based on LDA, and evaluate them on a new corpus of court decision that we had annotated by experts and non-experts. Our results indicate that these methods can yield important improvements over the keyword heuristic, which is often used as a concept detection heuristic in many contexts. While more work remains to be done, this indicates that detecting concepts through topics can serve as a general-purpose method for at least some forms of concept expression that are not captured using naive keyword approaches

Positioning for conceptual development using latent semantic analysis

With increasing opportunities to learn online, the problem of positioning learners in an educational network of content offers new possibilities for the utilisation of geometry-based natural language processing techniques. In this article, the adoption of latent semantic analysis (LSA) for guiding learners in their conceptual development is investigated. We propose five new algorithmic derivations of LSA and test their validity for positioning in an experiment in order to draw back conclusions on the suitability of machine learning from previously accredited evidence. Special attention is thereby directed towards the role of distractors and the calculation of thresholds when using similarities as a proxy for assessing conceptual closeness. Results indicate that learning improves positioning. Distractors are of low value and seem to be replaceable by generic noise to improve threshold calculation. Furthermore, new ways to flexibly calculate thresholds could be identified

SME Development Banks: Conceptual Framework and Empirical Analysis

In this paper we develop a conceptual framework to define small and medium-sized enterprise development banks (SMEDB). This conceptual effort is motivated by the lack of a clear definition of SMEDB. Once a consistent definition of SMEDB is provided, we compare a sample of banks that are SMEDB according to such definition with a sample of commercial banks. We conclude that it is possible to separate SMEDB from commercial banks in a statistically significant manner by taking into consideration a set of relevant financial indicators and we confirm the widespread idea that SMEDB play a crucial public/social role

Machine-assisted Cyber Threat Analysis using Conceptual Knowledge Discovery

Over the last years, computer networks have evolved into highly dynamic and interconnected environments, involving multiple heterogeneous devices and providing a myriad of services on top of them. This complex landscape has made it extremely difficult for security administrators to keep accurate and be effective in protecting their systems against cyber threats. In this paper, we describe our vision and scientific posture on how artificial intelligence techniques and a smart use of security knowledge may assist system administrators in better defending their networks. To that end, we put forward a research roadmap involving three complimentary axes, namely, (I) the use of FCA-based mechanisms for managing configuration vulnerabilities, (II) the exploitation of knowledge representation techniques for automated security reasoning, and (III) the design of a cyber threat intelligence mechanism as a CKDD process. Then, we describe a machine-assisted process for cyber threat analysis which provides a holistic perspective of how these three research axes are integrated together