Special issue introduction: Approaching spatial uncertainty visualization to support reasoning and decision making

While research on uncertainty and decision-making has a long history across several disciplines, recent technological developments compel researchers to rethink how to best address and advance the understanding of how humans reason and make decisions under spatial uncertainty. This introduction presents a visual summary graphic to provide an overview of each article in this special issue. Upon viewing these visual summaries, the reader will find that each of these articles covers different topics in the uncertainty visualization domain, offering complementary research in this field. Extending this body of research and finding new ways to explore how these visualizations may help or hinder the analytical and reasoning process of humans continues to be a necessary step towards designing more effective uncertainty visualizations to support reasoning and decision-making

Model-Based Tools for Pharmaceutical Manufacturing Processes

The Special Issue on “Model-Based Tools for Pharmaceutical Manufacturing Processes” will curate novel advances in the development and application of model-based tools to address ever-present challenges of the traditional pharmaceutical manufacturing practice as well as new trends. This book provides a collection of nine papers on original advances in the model-based process unit, system-level, quality-by-design under uncertainty, and decision-making applications of pharmaceutical manufacturing processes

Human preferences and risky choices

There are different views on what preferences for risks are and whether they are indicators of stable, underlying generic cognitive systems. Preferences could be conceived as an attitude towards a set of properties of context, memory and affect - a gauge of how much uncertainty one is willing to tolerate. This special issue aims to initiate a discussion on the stability of preferences for risks - as research has shown that different decision domains, response modes, and framing facilitate preference reversals. A consistent claim from behavioural decision researchers is that, contrary to the assumptions of classical economics, preferences are not stable and inherent constructs in individuals but are modified by levels of accessibility in memory, context, decision complexity, and type of psychological processing (e.g., sampling or computational “tradeoffs” in processing). For example, in a sampling-based decision-making paradigm it is argued that preferences are not essential for making risky decisions. The existing theoretical and empirical evidence reveals that human preferences are relative and unstable, undermining the predictions of normative theory. Recent theoretical accounts in psychology have expanded the debate further by offering evolutionary models of decision-making under risk. While most of the researcher has explored optimisation goals (traditionally assumed in economics), evolutionary psychology has promoted adaptation-driven processes for risky choices. Moreover, we have witnessed a renaissance of preferences as affect rather than as a construct with psycho-economical properties. Although behavioural decision research is still engaged in challenging the foundation of economic theory, at present, opinions seem less unified as to whether preferences reflect common psychological constructs

Data Mining in Smart Grids

Effective smart grid operation requires rapid decisions in a data-rich, but information-limited, environment. In this context, grid sensor data-streaming cannot provide the system operators with the necessary information to act on in the time frames necessary to minimize the impact of the disturbances. Even if there are fast models that can convert the data into information, the smart grid operator must deal with the challenge of not having a full understanding of the context of the information, and, therefore, the information content cannot be used with any high degree of confidence. To address this issue, data mining has been recognized as the most promising enabling technology for improving decision-making processes, providing the right information at the right moment to the right decision-maker. This Special Issue is focused on emerging methodologies for data mining in smart grids. In this area, it addresses many relevant topics, ranging from methods for uncertainty management, to advanced dispatching. This Special Issue not only focuses on methodological breakthroughs and roadmaps in implementing the methodology, but also presents the much-needed sharing of the best practices. Topics include, but are not limited to, the following: Fuzziness in smart grids computing Emerging techniques for renewable energy forecasting Robust and proactive solution of optimal smart grids operation Fuzzy-based smart grids monitoring and control frameworks Granular computing for uncertainty management in smart grids Self-organizing and decentralized paradigms for information processin

Evaluation of the U. S. Army Corps of Engineers\u27 Decision-Making Process for Selection of Dredged Material Disposal Sites

This study evaluates the decision-making process of the U.S. Army Corps of Engineers (COE) as it applies to the selection of land or open-water disposal sites for sediment from dredging projects planned by the COE. The study seeks to illustrate: 1) the structure of the COE decision-making process as provided for in laws, regulations, and COE policies; 2) the operational, economic, and environmental variables that might influence COE decision-making; 3) how the New England Division of the COE makes decisions on disposal sites for dredged material through consideration of operational, economic and environmental objectives. The study finds that the COE decision-making process, with respect to dredged material disposal, is structurally complex and bureaucratic in nature. It is characterized by the interaction of numerous laws, regulations, federal agencies, and public and private interests. The process is designed to accommodate potentially conflicting interests and objectives. There are many potentially important variables involved in the decision-making process. The possibility of conflicts between operational, economic and environmental objectives requires flexibility in decision-making. Because the potential environmental effects of dredged material disposal are still poorly understood, the environmental objective is the one most easily compromised. Based upon the quantitative evaluation performed in this study, the operational economic and environmental variable groups that were examined appear to have little or no influence on disposal site decision-making in the New England Division (NED) of the COE. This suggests that, in practice, the NED decision-making process is loosely structured, project specific, and highly subjective in nature. That finding is probably a function of the complex nature of both the dredged material disposal problem and the decision-making process. It is difficult for the NED decision-makers to objectively assess the same set of variables in each disposal decision. The diversity of potentially important variables, and the variability of project conditions make the application of broad-scale decision rules impractical. The decision-making process must be flexible enough to accommodate the special considerations of each project. As a result, disposal site selections must be made on a project-by-project basis, and are ultimately subjective in nature. The findings of this study raise an interesting issue in terms of the COE\u27environmental regulatory requirements. The New England Division does not appear to be making an attempt to exclude or restrict all potentially contaminated dredged material from open-water disposal. It seems that dredged material is excluded from open-water disposal only if it obviously violates current regulatory standards. Little effort is made to restrict the discharge of material that may be marginally contaminated. so long as it complies with the regulations. IF these finds reflect the policy of the New England Division, that policy may or may not be justified in light of the uncertainty surrounding the potential advese environmental impacts of dredged material disposal. The issue of contention is that, even though such a policy fulfills the regulatory requirements, it does not fully conform to the intent of Congress as expressed in the legislation

Conflict: Sacred Values, Decision Inertia and the Psychology of Choice in Military Decision-Making.

This thesis focusses on how individuals make hard choices. Specifically, it focusses on the cognitive conflict that emerges when members of the Armed Forces are presented with two options that are equally adverse during combat operations. Such decisions are often high-risk and any resulting decision inertia (a form of “indecision” that I pay special attention to throughout this thesis) can be costly. The issue, however, is that, to date, psychology has done little to explore least-worst decision-making and decision inertia in military populations. To understand the psychological processes behind these types of decisions, this thesis presents qualitative data collected from Soldiers who have combat experience (e.g., in Afghanistan and Iraq). Through my analysis of this data, I then focus on values, and specifically the importance of “sacred” values, as predictors of decision-making in conditions of high-consequence, uncertainty, and least-worst options. I then take a step back, and examine what separates military and non-military decision-making and decision-makers, by studying the decision-making of Soldiers, police officers and students within a series of simulated military and non-military scenarios. Through these experimental studies, I am then able to empirically test the role of value systems in decision-making within and between these groups. Overall, as well as identifying several domain-specific and domain-general correlates of least-worst decision-making, I identify two “clusters” of values (egocentric and empathetic) which, I feel, hold special importance when considering how least-worst decisions are, and are not, made

Preface

John Dennis Hey was born on 26 September 1944 and is a Professor of Economics and Statistics and Director of the Centre for Experimental Economics (EXEC) at the University of York . Between 1997 and 2011, he held a dual appointment as Professore Ordinario in Italy, first at the University of Bari and later at LUISS in Rome . He was Managing Editor of the Economic Journal from 1986 to 1996, and co-founder of several centres and laboratories in experimental economics, including EXEC at the University of York, Centro di Economia Sperimentale A Roma Est (CESARE) at LUISS, and Economia Sperimentale al Sud d’Europa (ESSE) at the University of Bari. He is the author or co-author of more than 100 research articles, and author, editor or co-editor of more than 20 books. To celebrate John Hey’s 70th birthday, this special issue has been prepared to acknowledge his important contributions in the field of decision making under risk and uncertainty