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.

Food risks: integrating effective risk communication into the risk management process

Abstract: Much has been written in the past twenty years on the issues relating to the communication of food risks and safety issues to the public. Most of this research has been based upon post-hoc studies of what went wrong – or, occasionally, right. Here we survey those findings briefly and draw these into a general framework for risk management and communication. By integrating risk communication into a coherent, common framework, we believe that public authorities, food producers and industry may develop more effective communication strategies which, in turn will enable the public to make more informed decisions on their diet.

Are current processes for nuclear emergency management in Europe adequate

Abstract: We describe the results of process mapping of nuclear emergency management procedures in four European countries. We find clear differences and explore these in relation to their suitability for building a shared understanding across the emergency management team of the evolving situation and a balanced appreciation of the uncertainties. Our findings indicate that there are some issues that cause concern in that the procedures may run smoothly and efficiently but they may also risk underestimating uncertainty or ignore key issues that have only been identified by a minority of experts or models. We are concerned that they do not facilitate the building of shared mental models that the literature such as that on highly reliable organisations has shown is important.

Towards a constructive approach to act-conditional subjective expected utility models

Abstract: In structuring a decision problem under uncertainty, the uncertain environment may be affected by the choice of an act. In decision analysis, the decision maker provides subjective probabilities and utilities through separate elicitation processes, and then both components are combined together to give an index of his preference over decision alternatives. Based upon this conceptualisation of decision analysis, a constructive approach to act-conditional subjective expected utility theory is proposed. Two utility models have been addressed: the linear utility model and the weighted utility model.

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.