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1189 results in Optimization, OR and risk

Page 35 of 60

Approaches to the Theory of Optimization
  • J. P. Ponstein
  • Published online:
    06 October 2009
    Print publication:
    20 November 1980
  • Optimization is concerned with finding the best (optimal) solution to mathematical problems that may arise in economics, engineering, the social sciences and the mathematical sciences. As is suggested by its title, this book surveys various ways of penetrating the subject. The author begins with a selection of the type of problem to which optimization can be applied and the remainder of the book develops the theory, mainly from the viewpoint of mathematical programming. To prevent the treatment becoming too abstract, subjects which may be considered 'unpractical' are not touched upon. The author gives plausible reasons, without forsaking rigor, to show how the subject develops 'naturally'. Professor Ponstein has provided a concise account of optimization which should be readily accessible to anyone with a basic understanding of topology and functional analysis. Advanced students and professionals concerned with operations research, optimal control and mathematical programming will welcome this useful and interesting book.

Stochastic Control of Partially Observable Systems
  • Alain Bensoussan
  • Published online:
    16 September 2009
    Print publication:
    13 August 1992
  • The problem of stochastic control of partially observable systems plays an important role in many applications. All real problems are in fact of this type, and deterministic control as well as stochastic control with full observation can only be approximations to the real world. This justifies the importance of having a theory as complete as possible, which can be used for numerical implementation. This book first presents those problems under the linear theory that may be dealt with algebraically. Later chapters discuss the nonlinear filtering theory, in which the statistics are infinite dimensional and thus, approximations and perturbation methods are developed.

  • Preface

  • Simon French, University of Manchester, John Maule, University of Leeds, Nadia Papamichail, University of Manchester
  • Book:
    Decision Behaviour, Analysis and Support
    Published online:
    20 February 2010
    Print publication:
    30 July 2009, pp xxiii-xxvi

  • Summary

    An article by Ian Ayres in the Financial Times Magazine of 1 September 2007 begins:

    How can a mathematical formula outperform a wine connoisseur? Or predict how the US Supreme Court will vote more accurately than a panel of legal experts? The answer lies partly in the overconfidence of humans and partly in the fast improving powers of database analysis.

    In many ways these sentences chart the course we shall be steering in exploring decision making: how we do it and how we could do it better. Many behavioural studies have shown that, while we humans may be the best decision makers on the planet, we are not as good as we think we are. We are subject to biases, inconsistencies and – dare we say it? – irrationalities in our decision making. We could do better. Therefore, it is not surprising, perhaps, that computers bringing advanced forecasting algorithms to bear on vast modern databases that bulge with fact upon fact are able to outperform even the best experts in highly structured forecasting tasks.

    Of course, this is not to suggest that computers are more intelligent than humans (we designed and programmed them, after all!), just that they are more consistent, able to keep more facts ‘in mind’ and less likely to be distracted by some outlying fact that runs against the broad thrust of evidence or, worse, some personal pet theory. They are not prone to overconfidence. Experts tend not to notice their failures. They fail to moderate their future predictions with the humility of their past inaccuracies.

Page 35 of 60