Dealing with bad guys: actor- and process-level determinants of the “devil shift” in policy making

Policy actors tend to misinterpret and distrust opponents in policy processes. This phenomenon, known as the “devil shift”, consists of the following two dimensions: actors perceive opponents as more powerful and as more evil than they really are. Analysing nine policy processes in Switzerland, this article highlights the drivers of the devil shift at two levels. On the actor level, interest groups, political parties and powerful actors suffer more from the devil shift than state actors and powerless actors. On the process level, the devil shift is stronger in policy processes dealing with socio-economic issues as compared with other issues. Finally, and in line with previous studies, there is less empirical evidence of the power dimension of the devil shift phenomenon than of its evilness dimension.

Total Variation Blind Deconvolution: The Devil Is in the Details

In this paper we study the problem of blind deconvolution. Our analysis is based on the algorithm of Chan and Wong [2] which popularized the use of sparse gradient priors via total variation. We use this algorithm because many methods in the literature are essentially adaptations of this framework. Such algorithm is an iterative alternating energy minimization where at each step either the sharp image or the blur function are reconstructed. Recent work of Levin et al. [14] showed that any algorithm that tries to minimize that same energy would fail, as the desired solution has a higher energy than the no-blur solution, where the sharp image is the blurry input and the blur is a Dirac delta. However, experimentally one can observe that Chan and Wong's algorithm converges to the desired solution even when initialized with the no-blur one. We provide both analysis and experiments to resolve this paradoxical conundrum. We find that both claims are right. The key to understanding how this is possible lies in the details of Chan and Wong's implementation and in how seemingly harmless choices result in dramatic effects. Our analysis reveals that the delayed scaling (normalization) in the iterative step of the blur kernel is fundamental to the convergence of the algorithm. This then results in a procedure that eludes the no-blur solution, despite it being a global minimum of the original energy. We introduce an adaptation of this algorithm and show that, in spite of its extreme simplicity, it is very robust and achieves a performance comparable to the state of the art.

Presencia y latencia del diablo en tres comedias de Agustín Moreto

In this paper, we analyze the dramatic configuration of the devil in three plays by Agustín Moreto, Los siete durmientes (1651) El más ilustre francés, San Bernardo (1657) y El azote de su patria (1669). The main prospect is not only the study of the stage power of a character with such a wide range of theatrical and artistic possibilities, but also his structural role in the conflict and his decisive influence on the action even when he is not actually present.