Simon French

Bio: Simon French is an academic researcher from University of Warwick. The author has contributed to research in topics: Decision analysis & Decision support system. The author has an hindex of 45, co-authored 172 publications receiving 10626 citations. Previous affiliations of Simon French include University of Leeds & University of Manchester.

Players in a Decision

Abstract: Different people with different roles may be involved in a decision—decision makers, experts, stakeholders, and decision analysts. Each has different interests and responsibilities in the decision-making process and they interact in different ways. Understanding and respecting these differences is an important aspect in designing an appropriate decision or risk analysis. Keywords: decision analysis; decision maker; decision analyst; expert; stakeholder

The Classical Model: The Early Years

Abstract: Roger Cooke and his colleagues at the Delft University of Technology laid the foundations of the Classical Model for aggregating expert judgement in the 1980s. During 1985–1989, a research project funded by the Dutch Government saw the Classical Model developed and embedded in expert judgement procedures along with a Bayesian and a paired comparison method. That project and a subsequent working group report from the European Safety and Reliability Research and Development Association were instrumental in moving structured expert judgement procedures into the toolbox of risk analysts, particularly within Europe. As the number of applications grew, the Classical Model and its associated procedures came to dominate in applications. This chapter reflects on this early work and notes that almost all the principles and practices that underpin today’s applications were established in those early years.

Numerical Optimization

Abstract: 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.

Evolutionary algorithms for solving multi-objective problems

Abstract: 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.

Confirmation Bias: A Ubiquitous Phenomenon in Many Guises:

Abstract: 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...

Towards automated crystallographic structure refinement with phenix.refine

Abstract: 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.

Thinking fast and slow.

Abstract: 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.