International Relations

Let's think again about the semantics of causal ascriptions. Suppose that we want to know what  caused a building crane to collapse during a windstorm. We might arrive at an account something like this: An unusually heavy gust of wind at 3:20 pm, in the presence of this crane's specific material and structural properties, with the occurrence of the operator's effort to adjust the crane's extension at 3:21 pm, brought about cascading failures of structural elements of the crane, leading to collapse at 3:25 pm. The process described here proceeds from the "gust of wind striking the crane" through an account of the material and structural properties of the device, incorporating the untimely effort by the operator to readjust the device's extension, leading to a cascade from small failures to a large failure. And we can identify the features of causal necessity that were operative at the several links of the chain. Notice that there are few causal regularities

The Center for the Study of Complex Systems at the University of Michigan hosted an intensive day-long training on some of the basics of machine learning for graduate students and interested faculty and staff. Jake Hofman, a Microsoft researcher who also teaches this subject at Columbia University, was the instructor, and the session was both rigorous and accessible ( link ). Participants were asked to load a copy of R , a software package designed for the computations involved in machine learning and applied statistics, and numerous data sets were used as examples throughout the day. (Here is a brief description of R; link .) Thanks, Jake, for an exceptionally stimulating workshop. So what is machine learning? Most crudely, it is a handful of methods through which researchers can sift through a large collection of events or objects, each of which has a very large number of properties, in order to arrive at a predictive sorting of the events or objects into a set of categories. The obj

image: diagram of molten salt reactor Organizational and regulatory features are sometimes part of the causal background of important technology failures. This is particularly true in the history of nuclear power generation. The promise of peaceful uses of atomic energy was enormously attractive at the end of World War II. In abstract terms the possibility of generating useable power from atomic reactions was quite simple. What was needed was a controllable fission reaction in which the heat produced by fission could be captured to run a steam-powered electrical generator. The technical challenges presented by harnessing nuclear fission in a power plant were large. Fissionable material needed to be produced as useable fuel sources. A control system needed to be designed to maintain the level of fission at a desired level. And, most critically, a system for removing heat from the fissioning fuel needed to be designed so that the reactor core would not overheat and melt down, releasing e

Groups make decisions through processes of discussion aimed at framing a given problem, outlining the group's objectives, and arriving at a plan for how to achieve the objectives in an intelligent way. This is true at multiple levels, from neighborhood block associations to corporate executive teams to the President's cabinet meetings. However, collective decision-making through extended discussion faces more challenges than is generally recognized. Processes of collective deliberation are often haphazard, incomplete, and indeterminate. What is collective deliberation about? It is often the case that a collaborative group or team has a generally agreed-upon set of goals -- let's say reducing the high school dropout rate in a city or improving morale on the plant floor or deterring North Korean nuclear expansion. The group comes together to develop a strategy and a plan for achieving the goal. Comments are offered about how to think about the problem, what factors may be rel