Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization. It responds to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems. For this new edition the book has been thoroughly updated throughout. There are new chapters on nonlinear interior methods and derivative-free methods for optimization, both of which are used widely in practice and the focus of much current research. Because of the emphasis on practical methods, as well as the extensive illustrations and exercises, the book is accessible to a wide audience. It can be used as a graduate text in engineering, operations research, mathematics, computer science, and business. It also serves as a handbook for researchers and practitioners in the field. The authors have strived to produce a text that is pleasant to read, informative, and rigorous - one that reveals both the beautiful nature of the discipline and its practical side.

Numerical Optimization

List of Figures. List of Tables. Preface. Foreword. 1. Basic Concepts. 2. Evolutionary Algorithm MOP Approaches. 3. MOEA Test Suites. 4. MOEA Testing and Analysis. 5. MOEA Theory and Issues. 3. MOEA Theoretical Issues. 6. Applications. 7. MOEA Parallelization. 8. Multi-Criteria Decision Making. 9. Special Topics. 10. Epilog. Appendix A: MOEA Classification and Technique Analysis. Appendix B: MOPs in the Literature. Appendix C: Ptrue & PFtrue for Selected Numeric MOPs. Appendix D: Ptrue & PFtrue for Side-Constrained MOPs. Appendix E: MOEA Software Availability. Appendix F: MOEA-Related Information. Index. References.

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Evolutionary algorithms for solving multi-objective problems

Confirmation bias, as the term is typically used in the psychological literature, connotes the seeking or interpreting of evidence in ways that are partial to existing beliefs, expectations, or a h...

/pdf/confirmation-bias-a-ubiquitous-phenomenon-in-many-guises-18m0fi8wck.pdf

Confirmation Bias: A Ubiquitous Phenomenon in Many Guises:

phenix.refine is a program within the PHENIX package that supports crystallographic structure refinement against experimental data with a wide range of upper resolution limits using a large repertoire of model parameterizations. It has several automation features and is also highly flexible. Several hundred parameters enable extensive customizations for complex use cases. Multiple user-defined refinement strategies can be applied to specific parts of the model in a single refinement run. An intuitive graphical user interface is available to guide novice users and to assist advanced users in managing refinement projects. X-ray or neutron diffraction data can be used separately or jointly in refinement. phenix.refine is tightly integrated into the PHENIX suite, where it serves as a critical component in automated model building, final structure refinement, structure validation and deposition to the wwPDB. This paper presents an overview of the major phenix.refine features, with extensive literature references for readers interested in more detailed discussions of the methods.

/pdf/towards-automated-crystallographic-structure-refinement-with-43hriikvv9.pdf

Towards automated crystallographic structure refinement with phenix.refine

In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

https://link.springer.com/content/pdf/10.1057%2Fjors.1987.128.pdf

Applied Statistics. A Handbook of BMDP Analyses

With the advent of the knowledge economy and the growing importance of knowledge societies, organizations are constantly seeking new ways of leveraging knowledge assets to support Decision Making (DM) processes. This chapter presents an initial insight to the little-researched phenomenon of how Knowledge Management Systems (KMSs) can support DM processes in organizations. A synthesis of ideas from a literature review suggests a new conceptual framework with several critical factors that organizations should take into account to assess the usage of KMSs tools in supporting DM processes in organizations. The proposed framework, “USUQ,” will benefit managers in both public and private sectors in knowing how the Usage, Satisfaction, Usefulness, and the Quality of using KMSs can support DM processes.

Assessing Knowledge Management Systems Usage in Supporting Decision Making Processes in Organizations

Our contention in developing the special issue has been that risk is both a pervasive phenomenon that requires systematic assessment and management efforts and, at the same time, a slippery concept that evades attempts to achieve precise quantiﬁcation. The pervasiveness is demonstrated by the range of application areas which are represented in this special issue.

/pdf/special-issue-on-risk-management-4urecc7txw.pdf

Special issue on risk management

Stern (2016) documents the evolution of the Duckworth–Lewis (DL) method from its inception (Duckworth & Lewis, 1998) through the Professional edition (Duckworth & Lewis, 2004) up to the contemporar...

A comment on the Duckworth–Lewis–Stern method

The context of decision making in the public sector is somewhat different from that in business and industry. Decision making may not be confined to a small management team or board of directors; but rather involves many politicians, officials, and different political institutions. There is a need to address many stakeholder concerns and be seen to do so, particularly when the decision involves major technological, health, environmental, or other risks. The issues addressed tend to be more complex and involve many factors and uncertainties. Here, we discuss how decision and risk analyses fit into such contexts and the differences between their approach and that of cost–benefit analysis.


Keywords:

cost–benefit analysis;
decision analysis;
regulators and planning;
societal decision making

Simon French

Papers

Applied Statistics. A Handbook of BMDP Analyses

Assessing Knowledge Management Systems Usage in Supporting Decision Making Processes in Organizations

Special issue on risk management

A comment on the Duckworth–Lewis–Stern method

Societal Decision Making