Online research in philosophy

Justificatory liberalism1 rests on a conception of members of the public as free and equal. To say that each is free implies that each has a fundamental claim to act as she sees fit on the basis of her own reasoning. To say that each is equal is to insist that members of the public are symmetrically placed insofar as no one has a natural right to command others, nor does anyone have a natural duty to defer to the reasoning of others. Given this conception of persons as free and equal, the legal authority of the state, because it is based on the use, and the threat of, force against its citizens, is deeply problematic: state functionaries employ power to force citizens, or issue threats to use force against them, to induce conformity to the law. On what grounds could anyone exercise such power and yet claim that she is respecting the person (as free and equal) that is imposed upon? In Immanuel Kant’s eyes, a crucial and necessary condition is that the person imposed upon by the law verifies that following the law is the thing to do — it is what his own reason instructs him to do. If the imposed law reflects the reason of those who are subject to it, Kant and his followers have insisted, in a fundamental sense the law treats them as free and equal (qua legislators) even though (qua subjects) they are bound. “A rational being belongs to the realm of ends as a member when he gives universal laws in it while also himself subject to these laws. He belongs to it as sovereign when he, as legislator, is subject to the will of no other.”2 Justificatory liberalism thus starts out with the idea of “free persons who have no authority over one another”3 and seeks to show how their reason can lead each to freely accept common laws to which they are subject. Only coercive laws that are publicly justified in this way — they are endorsed by the reason of all members of the public — can respect each as free and equal. “Respect for others requires public justification of coercion: that is the clarion call of justificatory liberalism.”4 My concern in this essay is not to motivate justificatory liberalism, but to investigate its relation to we might call “substantive” liberalisms..

Reasoning about mental states and processes is important in varioussubareas of the legal domain. A trial lawyer might need to reason andthe beliefs, reasoning and other mental states and processes of membersof a jury; a police officer might need to reason about the conjecturedbeliefs and reasoning of perpetrators; a judge may need to consider adefendant's mental states and processes for the purposes of sentencing;and so on. Further, the mental states in question may themselves beabout the mental states and processes of other people. Therefore, if AIsystems are to assist with reasoning tasks in law, they may need to beable to reason about mental states and processes. Such reasoning isriddled with uncertainty, and this is true in particular in the legaldomain. The article discusses how various different types ofuncertainty arise, and shows how they greatly complicate the task ofreasoning about mental states and processes. The article concentrates onthe special case of states of belief and processes of reasoning, andsketches an implemented, prototype computer program (ATT-Meta) thatcopes with the various types of uncertainty in reasoning about beliefsand reasoning. In particular, the article outlines the system'sfacilities for handling conflict between different lines of argument,especially when these lie within the reasoning of different people. Thesystem's approach is illustrated by application to a real-life muggingexample.

This article deals with the relation between a theory of law and a theory of legal reasoning. Starting from a close reading of Chapter VII of H. L. A. Hart's The Concept of Law, it claims that a theory of law like Hart's requires a particular theory of legal reasoning, or at least a theory of legal reasoning with some particular characteristics. It then goes on to say that any theory of legal reasoning that satisfies those requirements is highly implausible, and tries to show that this is the reason why not only Hart, but also writers like Neil MacCormick and Joseph Raz have failed to offer a theory of legal reasoning that is compatible with legal positivism as a theory of law. They have faced a choice between an explanation of legal reasoning that is incompatible with the core of legal positivism or else strangely sceptical, insofar as it severs the link between general rules and particular decisions that purport to apply them.

This article examines the relationship in Kant between transcendental laws and empirical laws (focusing on causal laws), and then brings a particular interpretation of that issue to bear on familiar puzzles concerning the status of the regulative maxims of reason and reflective judgment. It is argued that the 'indeterminate objective validity' possessed by the regulative maxims derives ultimately from strictly constitutive demands of understanding.

One aim of the Critique of Practical Reason is to establish that reason alone can determine the will. To show that it can, it suffices to show that there are practical principles given by reason alone – what Kant terms ‘practical laws’, or (roughly) requirements of reason on action. Chapter I of the Analytic accomplishes this aim by arguing that the moral law is an authoritative practical principle given as a ‘fact of reason’. The chapter begins in section 1 with a ‘Definition’ (Erklärung) of a practical law as a practical principle that..

Normic Laws and the Significance of Nonmonotonic Reasoning for Philosophy of Science. Normic laws have the form ‘if A then normally B’. They have been discovered in the explanation debate, but were considered as empirically vacuous (§1). I argue that the prototypical (or ideal) normality of normic laws implies statistical normality (§2), whence normic laws have empirical content. In §3–4 I explain why reasoning from normic laws is nonmonotonic, and why the understanding of the individual case is so important here. After sketching some foundations of nonmonotonic reasoning as developed by AI-researchers (§5), Iargue that normic laws are also the best way to understand ceteris paribus laws (§6). §7 deals with the difference between physical and non-physical disciplines and §9 with the difference between normicity and approximation. In §8 it is shown how nonmonotonic reasoning provides a new understanding of the protection of theories against falsification by auxiliary hypotheses. §10, finally, gives a system- and evolution-theoretical explanation of the deeper reason for the omnipresence of normic laws in practice and science, and forthe connection between ideal and statistical normality.

Where to begin? I’ll take three books from my shelves. First, now nearly forty years old, a little book of television lectures by the great physicist Richard Feynman, The Character of Physical Law. He talks about the laws of motion, the inverse square law of gravitation, conservation laws, symmetry principles and the various ways these all hang together. Feynman obviously takes it that it is a prime aim of science to discover such laws. But what are laws? He writes – and this is about his one and only shot at a characterization at the level of abstraction that we might think of as philosophical –.

BOOK I. OF LAWS IN GENERAL. Positive laws oughtto be consequenft of the laws of nature: this is the spirit of laws. MONTESQ_UIEU'S SPIRIT OF LAWS. ...

This paper presents a new algorithm to find an appropriate similarityunder which we apply legal rules analogically. Since there may exist a lotof similarities between the premises of rule and a case in inquiry, we haveto select an appropriate similarity that is relevant to both thelegal rule and a top goal of our legal reasoning. For this purpose, a newcriterion to distinguish the appropriate similarities from the others isproposed and tested. The criterion is based on Goal-DependentAbstraction (GDA) to select a similarity such that an abstraction basedon the similarity never loses the necessary information to prove the ground (purpose of legislation) of the legal rule. In order to cope withour huge space of similarities, our GDA algorithm uses some constraintsto prune useless similarities.