Soo Kim

According to T.M. Scanlon, the core idea of contractualism consists in the claim that what we are morally required to do is conceptually grounded in the value of living in “relations of mutual recognition” with others. Specifically, Scanlon's contractualist idea of “living in relations of mutual recognition with others” requires that one act only in ways that cannot be reasonably rejected by all of those affected, according to the results of a hypothetical reflection conducted within one's own mind. I claim, however, that Scanlon is mistaken to rely on a single individual's hypothetical reflection (monological reflection) to determine what no one affected could reasonably reject. Instead, following Jürgen Habermas, I claim that we must engage in actual dialogue with all who stand to be affected, in order to live in relations of mutual recognition with others.

The problem of the “shift” in Lotmanian thought in his last period still remains a subject of controversy for those who are interested in the scholarly heritage of Lotman. Besides explanations pointing out the “circumstantial” factors which brought about changes in Lotman’s views, it is important to identify a dimension of substantial “internal” changes which took place in the theoretical system itself. Regarding the essential changes relating to the concept of explosion as a movement from a ‘spatial’, more specifically ‘centre-periphery’ model to a ‘temporal’, namely ‘dislocated time’ model, the present article will focus on revealing the theoretical implication of this movement. The concept of explosion as an exceptional moment of ‘time out of joint’ was an active alternative to a theoretical breakthrough which was introduced in the process of overcoming the limitations of a previous ‘spatial’ model

Discovering the underlying mathematical expressions describing a dataset is a core challenge for artificial intelligence. This is the problem of symbolic regression. Despite recent advances in training neural networks to solve complex tasks, deep learning approaches to symbolic regression are underexplored. We propose a framework that leverages deep learning for symbolic regression via a simple idea: use a large model to search the space of small models. Specifically, we use a recurrent neural network to emit a distribution over tractable mathematical expressions and employ a novel risk-seeking policy gradient to train the network to generate better-fitting expressions. Our algorithm outperforms several baseline methods (including Eureqa, the gold standard for symbolic regression) in its ability to exactly recover symbolic expressions on a series of benchmark problems, both with and without added noise. More broadly, our contributions include a framework that can be applied to optimize hierarchical, variable-length objects under a blackbox performance metric, with the ability to incorporate constraints in situ, and a risk-seeking policy gradient formulation that optimizes for best-case performance instead of expected performance.

Supporters of paternalistic policies argue that interference with risky or dangerous choices for citizens’ own good is permissible, as long as those choices are caused by cognitive irrationality or ignorance. Yet, some liberal thinkers argue that despite human irrationality, paternalistic policies are still wrong because they fail to respect citizens as moral equals. I argue that actually both views are mistaken about what respect for citizens requires, because they conceptualize the citizens’ interests from the wrong standpoint. In order for citizens to be respected as equals, I argue that citizens’ interests must be defined from a joint (second-person) standpoint which is constructed through a dialogical process between policymakers and citizens oriented towards mutual understanding. Furthermore, I argue that engaging citizens in such a dialogue is a distinctive paternalistic intervention in its own right, which unlike other kinds of paternalistic intervention, is compatible with respect for citizens as equals.