How can thought experiments lead to new empirical knowledge if they do not make use of empirical information? This puzzle has been widely discussed in the philosophy of science. It arises in conceptual metaphor research as well and is especially important for the clarification of its empirical foundations. The aim of the paper is to suggest a possible solution to the puzzle of thought experiments in conceptual metaphor research. The solution rests on the application of a novel metatheoretical framework that (...) conceives of linguistic theorizing as a process of plausible argumentation. The central idea of the solution is that through the dynamic feedback mechanism of the cyclic, prismatic and retrospective re-evaluation of information, thought experiments in conceptual metaphor research may indirectly supply the process of plausible argumentation with empirical knowledge. (shrink)

While the analytical philosophy of science regards inconsistent theories as disastrous, Chomsky allows for the temporary tolerance of inconsistency between the hypotheses and the data. However, in linguistics there seem to be several types of inconsistency. The present paper aims at the development of a novel metatheoretical framework which provides tools for the representation and evaluation of inconsistencies in linguistic theories. The metatheoretical model relies on a system of paraconsistent logic and distinguishes between strong and weak inconsistency. Strong inconsistency is (...) destructive in that it leads to logical chaos. In contrast, weak inconsistency may be constructive, because it is capable of accounting for the simultaneous presence of seemingly incompatible structures. However, paraconsistent logic cannot grasp the dynamism of the emergence and resolution of weak inconsistencies. Therefore, the metatheoretical approach is extended to plausible argumentation. The workability of this metatheoretical model is tested with the help of a detailed case study on an analysis of discontinuous constituents in Government-Binding Theory. (shrink)

The paper relates two different fields of research: the historiography of generative linguistics and argumentation theory, a central topic of which is the investigation of fallacies. Relating the two fields is a challenge: Since fallacies seem to be at the heart of the historiography of generative linguistics, any thorough evaluation of its present state of the art also involves accounting for fallacies. The paper applies Kertész and Rákosi’s p-model of plausible argumentation to a case study on heated discussions in the (...) historiography of generative linguistics. As a result, basic properties of fallacious argumentation are revealed and delimited from plausible argumentation in linguistic historiography. In addition, the paper also draws generalizable conclusions that may affect the historiography of other human sciences as well. (shrink)

The paper exposes the philosophical and mathematical flaws in an attempt to settle the continuum problem by a new class of axioms based on probabilistic reasoning. I also examine the larger proposal behind this approach, namely the introduction of new primitive notions that would supersede the set theoretic foundation of mathematics.

Entropy is ubiquitous in physics, and it plays important roles in numerous other disciplines ranging from logic and statistics to biology and economics. However, a closer look reveals a complicated picture: entropy is defined differently in different contexts, and even within the same domain different notions of entropy are at work. Some of these are defined in terms of probabilities, others are not. The aim of this chapter is to arrive at an understanding of some of the most important notions (...) of entropy and to clarify the relations between them, After setting the stage by introducing the thermodynamic entropy, we discuss notions of entropy in information theory, statistical mechanics, dynamical systems theory and fractal geometry. (shrink)

Fifty years of effort in artificial intelligence (AI) and the formalization of legal reasoning have produced both successes and failures. Considerable success in organizing and displaying evidence and its interrelationships has been accompanied by failure to achieve the original ambition of AI as applied to law: fully automated legal decision-making. The obstacles to formalizing legal reasoning have proved to be the same ones that make the formalization of commonsense reasoning so difficult, and are most evident where legal reasoning has to (...) meld with the vast web of ordinary human knowledge of the world. Underlying many of the problems is the mismatch between the discreteness of symbol manipulation and the continuous nature of imprecise natural language, of degrees of similarity and analogy, and of probabilities. (shrink)