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dc.contributor.authorBayout Alvarenga, Marco Antonioen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Nuclear Engineeringen_US
dc.contributor.otherInternational Atomic Energy Agencyen_US
dc.date.accessioned2014-09-16T23:38:31Z
dc.date.available2014-09-16T23:38:31Z
dc.date.issued1993en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/89754
dc.description"September 1993."en_US
dc.description"Prepared for: International Atomic Energy Association [sic], Wagramerstrasse 5, P. 0. Box 100 A-1400 Vienna, Austria."en_US
dc.descriptionPart of appendix A and bibliography missingen_US
dc.descriptionIncludes bibliographical referencesen_US
dc.description.abstractThe field of human-machine systems and human-machine interfaces is very multidisciplinary. We have to navigate between the knowledge waves brought by several areas of the human learning: cognitive psychology, artificial intelligence, philosophy, linguistics, ergonomy, control systems engineering, neurophysiology, sociology, computer sciences, among others. At the present moment, all these disciplines seek to be close each other to generate synergy. It is necessary to homogenize the different nomenclatures and to make that each one can benefit from the results and advances found in the other. Accidents like TMI, Chernobyl, Challenger, Bhopal, and others demonstrated that the human beings shall deal with complex systems that are created by the technological evolution more carefully. The great American writer Allan Bloom died recently wrote in his book 'The Closing of the American Mind' (1987) about the universities curriculum that are commonly separated in tight departments. This was a necessity of the industrial revolution that put emphasis in practical courses in order to graduate specialists in many fields. However, due the great complexity of our technological world, we feel the necessity to integrate again those disciplines that one day were separated to make possible their fast development. This Report is a modest trial to do this integration in a holistic way, trying to capture the best tendencies in those areas of the human learning mentioned in the first lines above. I expect that it can be useful to those professionals who, like me, would desire to build better human-machine systems in order to avoid those accidents also mentioned above.en_US
dc.format.extent[120] pagesen_US
dc.publisherCambridge, Mass. : Massachusetts Institute of Technology, Dept. of Nuclear Engineering, [1993]en_US
dc.relation.ispartofseriesMITNE ; no. 304en_US
dc.subject.lccTK9008.M41 N96 no.304en_US
dc.subject.lcshHuman-machine systemsen_US
dc.titleModels and evaluation of human-machine systemsen_US
dc.typeTechnical Reporten_US
dc.identifier.oclc857902870en_US


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