Author
Maggie Marcum
Bio: Maggie Marcum is an academic researcher. The author has contributed to research in topics: Timeline. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.
Topics: Timeline
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TL;DR: In this paper, a comparative study of global fighter development timelines is presented, showing that while the United States remains the leader in fighter designs and advanced technology development, countries such as China are able to rapidly bridge the gap by copying foreign designs and building on the experience of collaborative partners.
Abstract: STUDY OF INNOVATION AND TECHNOLOGY IN CHINA POLICY BRIEF 2014-3 January 2014 A Comparative Study of Global Fighter Development Timelines Maggie MARCUM T his policy brief provides a summary of trends in the research, development, and acquisition (RDA) practices of fighter aircraft programs from the 1970s to modern times. This paper expands the evolving practice of RDA analysis by incorporating timeline analysis to compare the length of time the United States, Russia, China, and India take to design, produce, test, and field military fighters. The research suggests that while the United States remains the leader in fighter designs and advanced technology development, countries such as China are able to rapidly bridge the gap by copying foreign designs and building on the experience of collaborative partners. The brief lays the foundation for additional comparative studies that will focus on technology development and the ability of technology followers to emulate sophisticated capabilities for the next generation of fighter aircraft. The Study of Innovation and Technology in China (SITC) is a project of the University of California Institute on Global Conflict and Cooperation. SITC Research Briefs provide analysis and recommendations based on the work of project participants. This material is based upon work supported by, or in part by, the U.S. Army Research Laboratory and the U.S. Army Research Office through the Minerva Initiative under grant #W911NF-09-1-0081. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the U.S. Army Research Laboratory and the U.S. Army Research Office.
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TL;DR: This study presents a science education framework that helps to obtain relevant decision knowledge and close the opinion gaps, and meets the recent trend of data-driven decision-making.
Abstract: For an R&D institution to design a specific high investment cost product, the budget is usually ‘large but limited’. To allocate such budget on the directions with key potential benefits (e.g., core technologies) requires, at first and at least, a priority over the involved design criteria, as to discover the relevant decision knowledge for a suitable budgeting plan. Such a problem becomes crucial when the designed product is relevant to the security and military sustainability of a nation, e.g., a next generation fighter. This study presents a science education framework that helps to obtain such knowledge and close the opinion gaps. It involves several main tutorial phases to construct and confirm the set of design criteria, to establish a decision hierarchy, to assess the preferential structures of the decision makers (DMs) (individually or on a group basis), and to perform some decision analyses that are designed to identify the homogeneity and heterogeneity of the opinions in the decision group. The entire framework has been applied in a training course hold in a large R&D institution, while after learning the staff successfully applied these knowledge discovery processes (for planning the budget for the fighter design works and for closing the opinion gaps present). With the staffs’ practical exercises, several empirical findings except for the budgeting priority (e.g., the discrimination between ‘more important criteria’ against the less important ones) are also interesting. For some examples (but not limited to these), it is found that the results from using two measures (statistical correlation vs. geometrical cosine similarity) to identify the opinion gaps are almost identical. It is found that DMs’ considerations under various constructs are sometimes consistent, but often hard to be consistent. It is also found that the two methods (degree of divergence (DoD) vs. number of observed subgroups (NSgs)) that are used to understand the opinions’ diversity under the constructs are different. The proposed education framework meets the recent trend of data-driven decision-making, and the teaching materials are also some updates to science education.
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