Risks can be judged unacceptable for instrumental reasons, because they appear too large, relative to the associated benefits. They can also be judged unacceptable for fundamental reasons, because it is deemed morally...
Risks can be judged unacceptable for instrumental reasons, because they appear too large, relative to the associated benefits. They can also be judged unacceptable for fundamental reasons, because it is deemed morally or ethically wrong to incur them. With novel risks, these two sets of judgments are interrelated, as individuals look at the risk and at one another's behavior for guidance. After sketching these processes in general terms, we consider their expression in the context of a technology whose acceptability is currently being decided: genetically modified organisms in agriculture. We suggest some simple indicators of how well the technology is faring, as ways to gain insight into its future.
Recent studies by consumer and environmental groups have raised questions about Perchloroethylene (PCE) used by over 85% of the commercial dry cleaners in the United States, but classified as a possible or probable ca...
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When the public decides that a product or production process is socially unacceptable, the share price of the firms involved may suffer. The danger is that, out of distaste, people will refrain from buying the product...
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When the public decides that a product or production process is socially unacceptable, the share price of the firms involved may suffer. The danger is that, out of distaste, people will refrain from buying the product or the shares. But being able to assess the degree of unacceptability can mean being better able to assess how it will affect a firm's profitability, and being better able to assess the value of a firm. Over the past twenty-five years, many psychological studies have considered predictors of unacceptability for one class of industrial activities: those perceived as producing risks to health, safety, and the environment. We compare results from several studies of risk perception conducted from 1975-1994 with current consumer boycotts and the screening criteria of socially responsible investment firms-two forms of organized distaste. From both perspectives, high historic ratings on undesirable risk characteristics have predicted current organized aversion. These relationships are discussed in terms of how to make more precise estimates of the direct and indirect effects of social unacceptability on share price. One way is to pay critical attention to the financial disclosures of firms that may have such problems in light of the concurrent state of scientific knowledge. We illustrate these issues with the case of genetically modified organisms. [ABSTRACT FROM AUTHOR]
It can be difficult for new faculty to get the information they need on issues such as teaching, advising, and setting up a research program. While some have excellent mentors, others have come to rely on trial and er...
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It can be difficult for new faculty to get the information they need on issues such as teaching, advising, and setting up a research program. While some have excellent mentors, others have come to rely on trial and error or word of mouth. In 1996, the NSF engineering Education Scholars Workshop began at carnegie Mellon University to address the needs of new and future engineering faculty by: providing professional teaching development;offering guidance in supervising graduate students and conducting research;discussing likely engineering education and research challenges in the 21st century;and providing intellectual and social support with colleagues. After three years, a significant amount of knowledge and experience has been gathered by the workshop co-chairs. This paper details the structure of the workshop and discusses the underlying principles and implementation to provide guidance for those planning similar workshops.
Model checking can tell us whether a system is correct;probabilistic model checking can also tell us whether a system is timely and reliable. Moreover, probabilistic model checking allows one to verify properties that...
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In order to support mobile computing research, including the development of software which will allow seamless access to multiple wireless data networks, a wireless data network infrastructure is being built at Carneg...
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In order to support mobile computing research, including the development of software which will allow seamless access to multiple wireless data networks, a wireless data network infrastructure is being built at carnegie Mellon University. This infrastructure will allow researchers and other members of the campus community to use mobile computers to gain access to data networks while they are on-campus, or off-campus in the greater Pittsburgh area. The infrastructure will initially include two different types of wireless networks, a low-bandwidth wide area system and a high-bandwidth local area system, each of which will provide access to the campus computer network. Since the campus network is called "Andrew" (after Andrew carnegie and Andrew Mellon), the new wireless infrastructure has been dubbed "Wireless Andrew". The article describes the Wireless Andrew infrastructure being built. An overview of the infrastructure and the characteristics of the two types of wireless networks used are presented. Each of these networks is then described in more detail. The technology used, its implementation in the wireless network infrastructure, and deployment experience are discussed.
In integrated-services networks, both the arrival processes and the performance objectives can vary greatly from one packet stream to another. Either the network must be designed to meet the strictest performance requ...
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In integrated-services networks, both the arrival processes and the performance objectives can vary greatly from one packet stream to another. Either the network must be designed to meet the strictest performance requirements by every possible measure of performance, or traffic control algorithms are needed that differentiate traffic based on performance objectives, and adjust performance accordingly. There is therefore a need to evaluate and compare the performance of such traffic control algorithms including algorithms for admission control, transmission scheduling, and packet dropping. However, ATM integrated-services networks place great demands on simulation systems. This paper describes an efficient approach to the design of discrete-event simulations for these networks. It is used to evaluate several different traffic control approaches. Some sample results from the simulation are presented which compare these algorithms. It is shown that cost-based scheduling is the most effective of the scheduling algorithms simulated, but the priority token bank, which is simpler to implement, is close behind.
This paper illustrates the use of symbolic model checking in the design of deadlock-free flexible manufacturing systems. Our verification methodology consists of the following stages. First, we extract a state machine...
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This paper illustrates the use of symbolic model checking in the design of deadlock-free flexible manufacturing systems. Our verification methodology consists of the following stages. First, we extract a state machine model of the system. Second, we write the system specifications using a propositional temporal logic. Finally we use the model checker to check the state machine of the system against its requirements. When a deadlock is identified, a counterexample is automatically generated with a scenario that leads to the deadlock. The counterexample is used to design the proper operational policy that will prevent the corresponding deadlock. This verification approach allows an exhaustive search of all possible behaviors and scenarios. We designed a flexible manufacturing system capable of producing 3 types of parts with 4 machines and 3 robots. It took 8 seconds to find possible deadlocks assuming machine processing capacity of one part at a time and about 36 seconds when we increased the machine processing capacity to two parts at a time. The size of the state space was in the order of 10/sup 18/ states.
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