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In the last four years, since Volume I of this Bibliography first appeared, there has been an explosion of literature in all the main functional areas of business. This wealth of material poses problems for the researcher in management studies — and, of course, for the librarian: uncovering what has been written in any one area is not an easy task. This volume aims to help the librarian and the researcher overcome some of the immediate problems of identification of material. It is an annotated bibliography of management, drawing on the wide variety of literature produced by MCB University Press. Over the last four years, MCB University Press has produced an extensive range of books and serial publications covering most of the established and many of the developing areas of management. This volume, in conjunction with Volume I, provides a guide to all the material published so far.

In this paper, one combines information theory, and more especially the concept of entropy, with the statistical theory of decision to derive new criteria for pattern recognition. A generalized definition of entropy is considered as a risk function, and the generalized decision rules so obtained contain the family of the Bayesian decisions as special cases. These criteria may help to check the results obtained by usual techniques; they can be used in adaptive and learning systems, and more generally they can be useful in cybernetic systems.

After briefly reviewing the past history of Bayesian econometrics and Alan Greenspan's (2004) recent description of his use of Bayesian methods in managing policy-making risk, some of the issues and needs that he mentions are discussed and linked to past and present Bayesian econometric research. Then a review of some recent Bayesian econometric research and needs is presented. Finally, some thoughts are presented that relate to the future of Bayesian econometrics.

This paper aims to give a brief review on behavioral economics and behavioral finance and discusses some of the previous research on agents' utility functions, applicable risk measures, diversification strategies and portfolio optimization.

The authors also cover related disciplines such as trading rules, contagion and various econometric aspects.

While scholars could first develop theoretical models in behavioral economics and behavioral finance, they subsequently may develop corresponding statistical and econometric models, this finally includes simulation studies to examine whether the estimators or statistics have good power and size. This all helps us to better understand financial and economic decision-making from a descriptive standpoint.

The research paper is original.

This chapter is designed to outline how current methods in formal policy analysis have evolved to better respect limits to an analyst's knowledge. These limits are referred to as model uncertainty both in order to capture the idea that formal policy analysis is predicated on mathematically precise formulations that embody assumptions on the part of an analyst and because model uncertainty, which represents a recognition of the potential for these assumptions to produce unsound analyses, has been an active area of research in economics and statistics for the last 15 or so years. The argumentation in this chapter is not original and is admittedly selective. For Austrian economists, the paper will hopefully be of interest in indicating how empirical work is evolving in a way that better respects limits to a social scientist's knowledge. I certainly do not mean to suggest that these arguments should eliminate the objections that have been raised by some Austrian economists to formal empirical work. Rather, the intent of this chapter is to indicate the possibility of dialog and debate between Austrian and non-Austrian economists on the role of formal empirical work. In several contexts, I have introduced arguments concerning the limits of formal econometric analysis by Hayek and von Mises to both illustrate how the perspectives in this chapter relate to their views in order to suggest why, in my judgment, some of their skepticism is unwarranted.

“Statistical decision theory is a theory of decision‐making, i.e., of selecting among alternatives. It is not a theory of problem solving, i.e., of finding the cause of a particular set of symptoms. Thus, if the problem or opportunity is defined poorly even the best analysis thereafter will be of limited value. In fact, it could be detrimental”.

Introduction Operations research, i.e. the application of scientific methodology to operational problems in the search for improved understanding and control, can be said to have started with the application of mathematical tools to military problems of supply bombing and strategy, during the Second World War. Post‐war these tools were applied to business problems, particularly production scheduling, inventory control and physical distribution because of the acute shortages of goods and the numerical aspects of these problems.

The traditional literature dealing with statistical decision problems usually assumes that previous information about an associated experiment may be expressed by means of conditional probabilistic information, and the actual experimental outcomes can be perceived with exactness by the statistician. We now consider statistical decision problems satisfying the first assumption above, so that the actual available information cannot be exactly perceived, but rather it may be assimilated with fuzzy information (as defined by Zadeh et al.).

Describes the thinking that went into a piece of experimental research in social psychology that is still in the planning stage. Discusses the potential market for a newspaper: how many read the paper?; how many read it now?; and of those who could but aren't reading it – why? Posits that existing kinds of research did not seem to have helped very much to solve the problem stated. States the use of statistical decision theory is the way to fix this problem. Proposes that if values and probabilities are put together something powerful and effective should arise. Concludes that the model herein takes care of values and probabilities but not of instigations or constraints.