Welcome to the new world disorder: conflict and transformation in Ian McEwan’s saturday

Ian McEwan‘s novel Saturday deals with the complex issues of conflict and transformation in the age of terrorism. The plot presents one internal dilemma and several interpersonal altercations that occur within a mere twenty-four hours: a) Perowne (the protagonist) vs. himself, in face of his ambivalent thoughts regarding British military participation in the war in the Middle East; b) The protagonist vs. Baxter, a ruffian from East End, in the context of a car accident; c) Perowne vs. a fellow anaesthetist, Jay Strauss, during a squash game; d) Perowne‘s daughter, Daisy vs. her grandfather, John Grammaticus, both poets and rivals; e) Perowne‘s family vs. Baxter, who intrudes the protagonist‘s house. In this paper, I exemplify, analyse and discuss how: a) Understanding the causes of what we call evil constitutes an important step towards mutual understanding; b) Both science and arts (which Perowne considers, at first, irrelevant) are important elements in the process of transformation; c) Both personal and interpersonal conflicts are intrinsic to human nature — but they also propitiate healthy changes in behaviour and opinion, through reflection. In order to do so, I resort to Saturday, and to the work of several specialists in the field of conflict management.

A few what-ifs on using statistical analysis of stochastic simulation runs to extract timeliness properties

Modern real-time systems, with a more flexible and adaptive nature, demand approaches for timeliness evaluation based on probabilistic measures of meeting deadlines. In this context, simulation can emerge as an adequate solution to understand and analyze the timing behaviour of actual systems. However, care must be taken with the obtained outputs under the penalty of obtaining results with lack of credibility. Particularly important is to consider that we are more interested in values from the tail of a probability distribution (near worst-case probabilities), instead of deriving confidence on mean values. We approach this subject by considering the random nature of simulation output data. We will start by discussing well known approaches for estimating distributions out of simulation output, and the confidence which can be applied to its mean values. This is the basis for a discussion on the applicability of such approaches to derive confidence on the tail of distributions, where the worst-case is expected to be.