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The point of this chapter is to provide an alternative to simplifying or changing the model, but the wisdom of Davidson and MacKinnon s advice is worth emphasizing in that our approach is appropriate only when the more complicated model is indeed of interest Respeci cation and reanalysis is important and appropriate advice in some applications of linear regression because a noninvertible Hessian has a clear substantive interpretation: It can only be caused by multicollinearity or including more explanatory variables than observations (although even this simple case can be quite complicated; see Searle 1971) As such, a noninvertible Hessian might indicate a substantive problem that a researcher would not be aware of otherwise It is also of interest in some nonlinear models, such as logistic regression, where the conditions of noninvertibility are also well known.

Tie together the operation of these systems; and Do so in a way that can be changed as needed, depending on management goals.

In most instances, in response to this question the interviewers want you to say that you would take a course of action to

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In nonlinear models, however, noninvertible Hessians are related to the shape of the posterior density, but how to connect the problem to the question being analyzed can often be extremely dif cult In addition, for some applications, the textbook advice is disconcerting, or even misleading, because the same model speci cation may have worked in other contexts and really is the one from which the researcher wants estimates Furthermore, one may nd it troubling that dropping variables from the speci cation substantially affects the estimates of the remaining variables and therefore the interpretation of the ndings (Leamer 1973) The point developed in this chapter is that although a noninvertible Hessian means the desired variance matrix does not exist, the likelihood function may still contain considerable information about the questions of interest.

An excessively sophisticated RMP will be a handicap, as will an excessively dif cult project. But learning requires a challenge, and only by using a bit more sophistication than we need can we recognize when and where it is safe to take short cuts. As experience is gained, the emphasis can move from RMPs as a general risk management learning experience in the context of effective use from the outset for all projects considered, to RMPs as an effective and ef cient way to deal with immediate concerns. Short cuts can be taken in the light of an understanding of how much effort will be saved by the short cuts and what the likely impact will be on the effectiveness of the project management process as a whole. Most organizations rst use a formal RMP because of an organizational imperative: sometimes imposed by nature , sometimes imposed by regulators, bankers, or other interested parties. However, once formal RMPs are in place, most organizations have expanded their motives as an appreciation of the bene ts has been acquired. In the past organizations have tended to learn the hard way , as have the authors. There is now no need for organizations to learn the hard way to such an extent. The pioneers took a decade to learn what rst-time users can now learn in a year. This doesn t mean there will be no learning curve. But to use the Southampton to Cherbourg sailing analogy yet again, other people have now made the crossing lots of times and written about their experiences, in some cases with guidance about speci c crossings. The International Journal of Project Management, particularly since 1990, is one good source of project risk management experience that may provide cases relevant to the reader s circumstances.

Learning in the face of mystery is an expression borrowed from Barry Turner with the intention of paying him homage In a paper presented at Palermo (Italy) in 1991, Barry Turner drew a distinction between what he called old organizational learning and new organizational learning , which can be considered two narratives on learning based on two different rhetoricsTurner viewed old organizational learning as a functionalist narrative based mainly on a cybernetic model of organization and a feedback model of learning..

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