In this essay, I propose an interpretation of John Rawls’s The Law of Peoples that puts the stability of liberal societies as the central organizing idea of its principles. I start by critically examining other interpretations currently found in the literature. I observe two characteristics of Rawls’s conception of stability from his political turn: stability for the right reasons and in the right way. In the main body of the essay, I argue that the absence of a global egalitarian principle is compatible with the stability of liberal peoples, that the toleration of decent peoples is conducive to the stability of liberal peoples, and that the universal enforcement of human rights and assistance to burdened societies are rationally required for the stability of liberal peoples. I clarify the meaning of the centrality of peace and stability, and explain why it is not conspicuous but still unsurprising given Rawls’s personal history and the development of his theory of international justice. I conclude by suggesting how the stability interpretation paves the way for further extensions of Rawls’s theory of justice and assessing the practical value of The Law of Peoples.

In this paper, I argue that a new principle of background justice should be added to Rawls’s Law of Peoples because climate change is an international and intergenerational problem that can destabilize the Society of Peoples and the well-ordered peoples therein. I start with explaining the nature of my project and Rawls’s conception of stability. I argue that climate change poses a realistic threat to the stability of climate-vulnerable liberal peoples and as a result undermines international peace and security. Despite the uncertainties due to the complexity of the climate system and about the resilience of liberal societies, liberal peoples’ fundamental interests in just basic institutions and national security call for the adoption of a precautionary principle. Rawls’s own principles are, I argue, inadequate to solve the stability problem from climate change. Still, his framework provides the theoretical resources to develop a new extension. I propose a new Rawlsian principle of international, intergenerational justice that guarantees the environmental background conditions under which well-ordered peoples can sustain their basic structure over generations and sketch the principle’s institutional implementation. I conclude with the theoretical and practical significance of this extension of Rawls’s theory.

This paper examines whether, and if so when, luxury tax is justifiable. After a characterization of luxury tax, I critically examine several arguments that have been or can be made in defence of luxury tax, including Ng’s diamond good argument and a variation of Frank’s positional good argument. I put forward an alternative, expressive argument, according to which luxury tax can help to create and sustain social norms that discourage conspicuous luxury consumption and display of wealth. I explain several ways in which luxury tax fails to achieve the expressive goal and brings about unintended consequences.

Light field (LF) images suffer from low spatial resolution due to the trade-off between angular and spatial resolutions. Thus, spatial super-resolution (SR) of LF images is an essential task to obtain high-quality LF images. However, the existing SR networks still have limitations, since they exploit only single-level features to use sub-pixel information in LF images. In this paper, we proposed a light field super-resolution (LFSR) network to effectively improve the spatial resolution of light field images. The proposed network takes one target image and its 8-neighboring images for references. We construct multilevel structures for the proposed network to effectively estimate and mix sub-pixel information in reference images. The proposed network is composed of a feature extractor, a feature warping module, a feature mixing module, and a upscaling module. The feature extractor provides multi-level features for SR and offsets to the feature warping module to obtain aligned features for multiple reference images. The feature mixing module mixes multiple aligned features based on the similarity between the target and reference images to obtain multi-level mixed features. Finally, the upscaling module generates a high-resolution residual image using the multi-level mixed features. Experimental results demonstrate the proposed network outperforms the state-of-the-art methods on various light field datasets. The pre-trained model and source codes are available at https://github.com/Hwa-Jong/LF_MLS.