Rational monism and rational pluralism

Consequentialists often assume rational monism: the thesis that options are always made rationally permissible by the maximization of the selfsame quantity. This essay argues that consequentialists should reject rational monism and instead accept rational pluralism: the thesis that, on different occasions, options are made rationally permissible by the maximization of different quantities. The essay then develops a systematic form of rational pluralism which, unlike its rivals, is capable of handling both the Newcomb problems that challenge evidential decision theory and the unstable problems that challenge causal decision theor

An argument against causal decision theory

This paper develops an argument against causal decision theory. I formulate a principle of preference, which I call the Guaranteed Principle. I argue that the preferences of rational agents satisfy the Guaranteed Principle, that the preferences of agents who embody causal decision theory do not, and hence that causal decision theory is false

Idealised Historical Myths and Meta-Temporal Space in Nazi and Fascist Political Religion

Political religion is the ‘sacralisation’, or making sacred, of formerly political entities –in this case the nation-state –as an item of worship.1Such a process is integral to totalitarian ideology, as it enables the manufacture of societal values.2In Nazism and Fascism, a specific ‘Idealised Historical Myth’ was created in order to present a nation worth worshiping –this manifested itself in the Italian Fascist ‘Romanita’, concerned with Imperial Roman values and architecture, and the Nazi ‘Germanentum’, likewise fixated with ancient Germanic and Norse culture.3In turn, these myths offered a ‘Meta-Temporal Space’ in which Fascist and Nazi values became eternal, and enabled individuals to transcend their decadent present selves. Thus, Nazi and Fascist political religion had a genuine transcendent, spiritual value comparable to the experience of conventional religions

A CONVEX AND SELECTIVE VARIATIONAL MODEL FOR IMAGE SEGMENTATION

Selective image segmentation is the task of extracting one object of interest from an image, based on minimal user input. Recent level set based variational models have shown to be effective and reliable, although they can be sensitive to initialization due to the minimization problems being nonconvex. This sometimes means that successful segmentation relies too heavily on user input or a solution found is only a local minimizer, i.e. not the correct solution. The same principle applies to variational models that extract all objects in an image (global segmentation); however, in recent years, some have been successfully reformulated as convex optimization problems, allowing global minimizers to be found. There are, however, problems associated with extending the convex formulation to the current selective models, which provides the motivation for the proposal of a new selective model. In this paper we propose a new selective segmentation model, combining ideas from global segmentation, that can be reformulated in a convex way such that a global minimizer can be found independently of initialization. Numerical results are given that demonstrate its reliability in terms of removing the sensitivity to initialization present in previous models, and its robustness to user input

Stabilised bias field: segmentation with intensity inhomogeneity

Automatic segmentation in the variational framework is a challenging task within the field of imaging sciences. Achieving robustness is a major problem, particularly for images with high levels of intensity inhomogeneity. The two-phase piecewise-constant case of the Mumford-Shah formulation is most suitable for images with simple and homogeneous features where the intensity variation is limited. However, it has been applied to many different types of synthetic and real images after some adjustments to the formulation. Recent work has incorporated bias field estimation to allow for intensity inhomogeneity, with great success in terms of segmentation quality. However, the framework and assumptions involved lead to inconsistencies in the method that can adversely affect results. In this paper we address the task of generalising the piecewise-constant formulation, to approximate minimisers of the original Mumford-Shah formulation. We first review existing methods for treating inhomogeneity, and demonstrate the inconsistencies with the bias field estimation framework. We propose a modified variational model to account for these problems by introducing an additional constraint, and detail how the exact minimiser can be approximated in the context of this new formulation. We extend this concept to selective segmentation with the introduction of a distance selection term. These models are minimised with convex relaxation methods, where the global minimiser can be found for a fixed fitting term. Finally, we present numerical results that demonstrate an improvement to existing methods in terms of reliability and parameter dependence, and results for selective segmentation in the case of intensity inhomogeneity. </jats:p

Chan-Vese Reformulation for Selective Image Segmentation.

Selective segmentation involves incorporating user input to partition an image into foreground and background, by discriminating between objects of a similar type. Typically, such methods involve introducing additional constraints to generic segmentation approaches. However, we show that this is often inconsistent with respect to common assumptions about the image. The proposed method introduces a new fitting term that is more useful in practice than the Chan-Vese framework. In particular, the idea is to define a term that allows for the background to consist of multiple regions of inhomogeneity. We provide comparative experimental results to alternative approaches to demonstrate the advantages of the proposed method, broadening the possible application of these methods

Objective Value is Always Newcombizable

This paper argues that evidential decision theory (EDT) is incompatible with options having objective values. After some scene-setting (§§1-3), we consider three arguments for our thesis: the argument from Newcomb’s Problem (§§4-5), the argument from Expectationism (§§6-7), and the argument from Newcombizability (§8). The first two arguments fail for instructive reasons. But the third succeeds. EDT is incompatible with options having objective values because objective value is always Newcombizable. What to make of this incompatibility is a matter on which the authors disagree. One is inclined to take it to be a reason for rejecting the claim that options have objective values; the other is inclined to take it to be a reason for rejecting EDT. The paper, itself, takes no stand on this downstream disagreement; it merely argues for the incompatibility

Why Take Both Boxes?

The crucial premise of the standard argument for two‐boxing in Newcomb's problem, a causal dominance principle, is false. We present some counterexamples. We then offer a metaethical explanation for why the counterexamples arise. Our explanation reveals a new and superior argument for two‐boxing, one that eschews the causal dominance principle in favor of a principle linking rational choice to guidance and actual value maximization

Everybody Loves My Baby : But My Baby Don\u27t Love Nobody But Me

Contains advertisements and/or short musical examples of pieces being sold by publisher.https://digitalcommons.library.umaine.edu/mmb-vp/6868/thumbnail.jp