International Relations

When we think of the issues of health and safety that exist in a modern complex economy, it is impossible to imagine that these social goods will be produced in sufficient quantity and quality by market forces alone. Safety and health hazards are typically regarded as "externalities" by private companies -- if they can be "dumped" on the public without cost, this is good for the profitability of the company. And state regulation is the appropriate remedy for this tendency of a market-based economy to chronically produce hazards and harms, whether in the form of environmental pollution, unsafe foods and drugs, or unsafe industrial processes. David Moss and John Cisternino's New Perspectives on Regulation provides some genuinely important perspectives on the role and effectiveness of government regulation in an epoch which has been shaped by virulent efforts to reduce or eliminate regulations on private activity. This volume is a report from the Tobin Project. It...

How can industries involving processes that create large risks of harm for individuals or populations be modified so they are more capable of detecting and eliminating the precursors of harmful accidents? How can nuclear accidents, aviation crashes, chemical plant explosions, and medical errors be reduced, given that each of these activities involves large bureaucratic organizations conducting complex operations and with substantial inter-system linkages? How can organizations be reformed to enhance safety and to minimize the likelihood of harmful accidents? One of the lessons learned from the Challenger space shuttle disaster is the importance of a strongly empowered safety officer in organizations that deal in high-risk activities. This means the creation of a position dedicated to ensuring safe operations that falls outside the normal chain of command. The idea is that the normal decision-making hierarchy of a large organization has a built-in tendency to maintain production schedul...

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...

Is it possible to elucidate complex social outcomes using computational tools? Can we overcome some of the issues for social explanation posed by the fact of heterogeneous actors and changing social environments by making use of increasingly powerful computational tools for modeling the social world? Ken Kollman, John Miller, and Scott Page make the affirmative case to this question in their 2003 volume, Computational Models in Political Economy . The book focuses on computational approaches to political economy and social choice. Their introduction provides an excellent overview of the methodological and philosophical issues that arise in computational social science. The subject of this book, political economy, naturally lends itself to a computational methodology. Much of political economy concerns institutions that aggregate the behavior of multiple actors, such as voters, politicians, organizations, consumers, and firms. Even when the interactions within and rules of a political o...