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.

Decision making not decision theory

Nordic Decision Conference: An Exercise on Clean-up Actions in an Urban Environment after a Nuclear Accident Arlandia Hotel, Sweden 30th-31th August, 1995

Knowledge Management Systems (KMS) help decision makers and users leverage and share their knowledge. In the current fluid environment, the challenge for Multinational Corporations (MNC) is to accumulate knowledge that stems from various sources to support Decision Making (DM) processes. Based on semi-structured interviews with 42 subjects, this paper identifies factors that affect knowledge sharing through the use of KMS to support DM processes in MNC. The study extends the existing literature by proposing a new theoretical framework.

https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1472&context=amcis2011_submissions

Knowledge Management System’s Characteristics that facilitate Knowledge Sharing to Support Decision Making Processes in Multinational Corporations

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Outranking Probabilities and the Synthesis of Forecasts

Assume that an expert makes probability forecasts for a number of random variables. The expert's calibration is roughly a measure of how accurate his forecasts are. For discrete probability forecasts, we can say that an expert is well calibrated if, over the set of occasions when he states a probability p, a proportion of events p actually occurs. A calibration score was developed by Murphy (1973) who partitioned the well-known Brier score into calibration and information parts. De Groot and Fienberg (1982) showed that any strictly proper scoring rule for probabilities can be similarly partitioned. If an expert states his distributions for continuous variables, Kadane and Lichtenstein (1982) suggested that he is well calibrated if, in the long run, the proportion of outcomes falling at or below the p% quantile of his assessed distributions is equal to p%. Generalizations of the scores for discrete forecasts to the assessment of density functions for continuous variables are difficult. Though some scores might be devised by partitioning the proper scoring rules of Buehler (1971), these would be impractical as they rely on the expert to state a complete density function for each variable. Here, we develop scores appropriate to the case in which the expert states quantiles of his distribution rather than the full density (or distribution) function. The hypothesis that the expert is calibrated is suitably formulated so that his calibration can be assessed via a test statistic.

Simon French

Papers

Decision making not decision theory

Nordic Decision Conference: An Exercise on Clean-up Actions in an Urban Environment after a Nuclear Accident Arlandia Hotel, Sweden 30th-31th August, 1995

Knowledge Management System’s Characteristics that facilitate Knowledge Sharing to Support Decision Making Processes in Multinational Corporations

Outranking Probabilities and the Synthesis of Forecasts

Hypothesis-based calibration scores