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For the systems engineering application addressed in this book, the objectives are the performance requirements that have been de ned as described in 6, as well as derived performance requirements that have been de ned as part of the development of the allocated architecture Following the de nition of the objectives, a value scale must be de ned for each objective at the bottom of the objectives hierarchy This value scale de nition begins by de ning the minimum acceptable value of performance for a given objective (constraining requirement) and the most desired value of performance for the objective (the design goal) Then the relative value of improving from the minimum acceptable threshold to the design goal is quanti ed in the form of a value curve.

Objectives that are a combination of bottom-level objectives are in the hierarchy for ease of aggregation and communication; as a result these intermediate and the top-level (or fundamental) objectives are computed from lower level objectives After value scales are de ned for each bottom-level objective, value weights that address the relative value associated with improving from the bottom (minimum acceptable threshold) of the value scale to the top (design goal) must be assessed from the stakeholder for all bottom-level objectives as well as the intermediate objectives The discussion in this chapter is going to address the.

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Another key part of the theory is that motivation is viewed as a process and as such each stage of the process must function well for motivation, the outcome of the process, to be high. For example, if outcomes with high need satisfaction potential are tied to evaluations which are clearly tied to the level of the results, but the person does not believe that he/she can produce the results, motivation will not be high. This is an extremely important aspect of the theory that will be very useful in understanding the effects of the different performance management techniques. For motivation, and ultimately performance, to be high, all components of the model must be high.

All users are known by three variables: userid, nodename, and network. A userid can be the person s initials, or the person s last name, or anything else the person decided upon when he opened his computer account. A nodename is also known as a hostname. It designates the computer the user is using. The network indicates which of the two dozen or so networks the computer is connected to. If you look at my name at the top of this article, you will see that my userid is Hank, my nodename is Taunivm (that is in Israel, in case you were wondering), and my network is called Bitnet. The nodename and network section of a user s handle has been undergoing a transformation in the past few years and this will be explained later. The one common protocol that all networks talk is something called RFC822 standard mail. Within individual networks there are other protocols which will be covered where necessary.

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The Harmony Project (found at www.metadata.net/harmony/) was an international collaboration funded by the Distributed Systems Technology Centre (DSTC, www.dstc.edu.au), Joint Information Systems Committee (JISC, www.jisc.ac.uk), and National Science Foundation (NSF, www.nsf.gov), which ran for three years (from July 1999 until June 2002). The goal of the Harmony Project was to investigate key issues encountered when describing complex multimedia resources in digital libraries, the results (published on the site) applied to later projects elsewhere. The project s approach covered four areas: Standards. A collaboration was started with metadata communities to develop and re ne developing metadata standards that describe multimedia components. Conceptual Model. The project devised a conceptual model for interoperability among community-speci c metadata vocabularies, able to represent the complex structural and semantic relationships that might be encountered in multimedia resources.

Good risk management ultimately depends on good decision making. Good decision making, in turn, is based on the rapid acquisition of the proper amount and kind of data, building a framework for deciding, moving forward, and incorporating feedback from past decisions as the feedback becomes available. The field of decision science has been well researched, particularly in the past two decades, and a considerable amount of information on how to improve organizational decisions is available in the related academic and popular literature. The University of Chicago and the Wharton School at the University of Pennsylvania in particular have strong decision science specialties. Some of our recommended books and papers on the subject are referred to throughout the chapter, as well as in the resources section at the end of the chapter. One in particular is Winning Decisions, by J. Edward Russo and Paul J. H. Schoemaker.

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