At its core, economics is about making decisions. In the history of economic thought, great intellectual prowess has been exerted toward devising exquisite theories of optimal decision making in situations of constraint, risk, and scarcity. Yet not all of our choices are purely logical, and so there is a longstanding tension between those emphasizing the rational and irrational sides of human behavior. One strand develops formal models of rational utility maximizing while the other draws on what behavioral science has shown about our tendency to act irrationally. In Risk, Choice, and Uncertainty, George G. Szpiro offers a new narrative of the three-century history of the study of decision making, tracing how crucial ideas have evolved and telling the stories of the thinkers who shaped the field. Szpiro examines economics from the early days of theories spun from anecdotal evidence to the rise of a discipline built around elegant mathematics through the past half century's interest in describing how people actually behave. Considering the work of Locke, Bentham, Jevons, Walras, Friedman, Tversky and Kahneman, Thaler, and a range of other thinkers, he sheds light on the vast scope of discovery since Bernoulli first proposed a solution to the St. Petersburg Paradox. Presenting fundamental mathematical theories in easy-to-understand language, Risk, Choice, and Uncertainty is a revelatory history for readers seeking to grasp the grand sweep of economic thought.

Today there are more technology, technologists, knowledge and experts than at any time in human history; but from a global perspective, it is difficult to argue that this accumulation of knowledge and technology has put the world in an unambiguously better position than it was in the past. Business is not getting any easier to do and major corporate collapses based on poor decisions, poor conduct, and poor judgement continue to occur. In public administration too, basic institutions and services (education, health, transport) seem to be continually undergoing "crises" of inadequate delivery and excessive pressure. Wisdom and Management in the Knowledge Economy explains why unwise managerial practice can happen in a world characterized by an excess of information and knowledge. Drawing on Aristotle's idea of practical wisdom, the book develops a theory of social practice wisdom that addresses important social psychological and sociological dynamics that underpin wise management and organizations. As well as providing a detailed theory of social practice wisdom, this book considers practical issues in organizational communication, behavior, culture, change and knowledge as well as in HRM, leadership, ethics, strategy, international business, business education, and wisdom research. By introducing the notion of social practice wisdom, aspects of social structure, organizational culture, and organizational communication needed for wisdom to flourish are for the first time rendered visible in a way that opens new possibilities for wiser management, wiser organizations, and wisdom research.

Trust is an important factor in risk management, affecting judgements of risk and benefit, technology acceptance and other forms of cooperation. In this book the world's leading risk researchers explore all aspects of trust as it relates to risk management and communication. Drawing on a wide variety of disciplinary approaches and empirical case studies (on topics such as mobile phone technology, well-known food accidents and crises, wetland management, smallpox vaccination, cooperative risk management of US forests and the disposal of the Brent Spar oil drilling platform), this is the most thorough and up-to-date examination of trust in all its forms and complexities. The book integrates diverse research traditions and provides new insights into the phenomenon of trust. Factors that lead to the establishment and erosion of trust are identified. Insightful analyses are provided for researchers and students of environmental and social science and professionals engaged in risk management and communication in both public and private sectors. Related titles The Tolerability of Risk (2007) 978-1-84407-398-6

Procrastination is a business's worst enemy, but it is most damaging when if affects the ability to lead...
Too many decisions are taken too slowly or not at all, because of the dithering behavior of our leaders, often leading to failure of the project, or worse, the organization. See how procrastination has led to major contemporary leadership failures and learn how to recognize and resolve the problem in yourself and others.

In recent years, there has been increasing implementation of group and team decision-making within organizations, much of it managed electronically, between members of what are "virtual" groups or teams. Recent research into effective team implementation emphasizes "trust" as an intermediary process, and trust must be a part of any account of team decision-making. This book provides an integrated framework that represents process in decision-making by interactive groups and teams. This framework furthers both our understanding of process and our capabilities in implementation, based on an account of group decision-making that differentiates the information types contributing to decision quality and relates them to process in interactive groups and teams. Author Steve Silver emphasizes the social structure that is inherent in the interaction of decision-makers as group or team members and effects on the information they exchange.

The epistemological and methodological issues within management sciences constitute a difficult area for theoretical reflection. The main goal of this book is to deepen epistemological and methodological reflection concerned with the foundations of organization and management in order to broaden the methodological awareness of researchers from the field of management sciences. This book does not describe paradigm change; it points out possible evolutional directions for management reflection. The key matter is to convince of the uncertainty and of the contextual nature of the knowledge obtained through management.

It's not what we know, but how we learn. This is the key that Learning to Read the Signs uses in order to evaluate and apply ideas and facts to one's organization life. The book asks the reader to go back to and reclaim pragmatism: an activity of thought involving four parts: Investigation, Hypothesis, Action, and Testing. Pragmatism is a method of interpretation or inquiry which offers to the thoughtful business practitioner a way to better understand the reality in which we operate, to think critically and creatively, and for business people to think together to make the best use of all our perspectives and talents. Questions raised in this book include: What are the signs telling us? Where are we headed and why? Why are things going the way they are? What is our purpose?

Scholarly interest in the areas of sustainability, stakeholder relations and corporate social responsibility (CSR) has increased considerably in recent years. In this volume, we take a step back to consider the fundamental questions that underlie and tie research across these areas together. The chapters in this volume cover a wide range of theoretical perspectives grounded in strategy, economics and sociology, employ various methodological approaches, and offer new arguments on the connections that exist between firms' decisions relating to sustainability, CSR, and the governance of their stakeholder relations. The chapters in this volume highlight that business decisions relating to sustainability and CSR are ultimately decisions about the governance of stakeholder relations, and suggest that future work in these areas should consider more closely both the firms and their stakeholders as strategic actors driving firm decisions.

We need a new approach for solving tough problems in a complex world-we need to collaborate smarter. Market volatility. Sustainability demands. Hybrid working. Opportunities and hazards of fast-changing technology and regulations. Companies and nonprofits face more daunting challenges than ever. How can we collaborate in our organizations-and with outside partners-to solve problems, innovate, and succeed? Smarter Collaboration offers groundbreaking solutions. This indispensable new book lays out a pragmatic action plan blending rich stories, new empirical research, and loads of practical advice to help companies thrive by collaborating more effectively. As Harvard professor Heidi K. Gardner and senior executive Ivan A. Matviak show, firms that collaborate smarter consistently generate higher revenues and profits, boost innovation, strengthen client relationships, and attract and retain better talent. In this successor to Gardner's bestselling first book, Smart Collaboration, the authors expand their mandate, illustrating the fundamental dynamics of collaborating well across industries like financial services, health care, biotech/pharma, consumer products, automotive, and technology. Based on their research with thousands of executives from around the world, they share deep insights on how to implement smarter collaboration and avoid the potential pitfalls. They also help leaders troubleshoot thorny challenges like misaligned incentives, collaboration overload, and unintended consequences on diversity and inclusion. Complete with how-tos and cases, the book concludes with inspiring examples of groups harnessing smarter collaboration to tackle society's biggest challenges such as saving the oceans, eradicating diseases, and tackling global warming. Smarter Collaboration is the essential guide for forward-thinking leaders to transform their organizations, reshape the way they work, and increase impact and success.

This book deals with complex problems in the fields of logistics and supply chain management and discusses advanced methods, especially from the field of computational intelligence (CI), for solving them. The first two chapters provide general introductions to logistics and supply chain management on the one hand, and to computational intelligence on the other hand. The subsequent chapters cover specific fields in logistics and supply chain management, work out the most relevant problems found in those fields, and discuss approaches for solving them. Chapter 3 discusses problems in the field of production and inventory management. Chapter 4 considers planning activities on a finer level of granularity which is usually denoted as scheduling. In chapter 5 problems in transportation planning such as different types of vehicle routing problems are considered. While chapters 3 to 5 rather discuss planning problems which appear on an operative level, chapter 6 discusses the strategic problem of designing a supply chain or network. The final chapter provides an overview of academic and commercial software and information systems for the discussed applications. There appears to be a gap between general textbooks on logistics and supply chain management and more specialized literature dealing with methods for computational intelligence, operations research, etc., for solving the complex operational problems in these fields. For readers, it is often difficult to proceed from introductory texts on logistics and supply chain management to the sophisticated literature which deals with the usage of advanced methods. This book fills this gap by providing state-of-the-art descriptions of the corresponding problems and suitable methods for solving them.

This book takes a fresh look at safety decision-making by documenting and examining stories told by front-line managers in three different high-hazard industries: a chemical plant, a nuclear power station and an air-navigation service provider. From Piper Alpha to Deepwater Horizon, accident analysis has stressed the importance of excellent decision-making by those in charge out in the field. Organizations rely critically on the judgement and experience of such senior operations personnel and yet these qualities are undervalued in a business environment that emphasises documentation and measurement. Whilst operational managers are guided by rules, they also draw on their own long experience and can formulate a situation-specific 'line in the sand' to apply the experience of the operating team to complex, real-world situations that rule writers may not have foreseen. This volume refocuses our attention on the people who make these important decisions and the organizational processes that support the best choices. Jan Hayes uses her multi-disciplinary experience to draw together an account of safety decision-making that is both technically robust and yet accessible to academics, practitioners and regulators alike. Readers will see that the stories retold in this book provide a way for operational managers to share their knowledge, experience and expertise - with each other and with us.

This book considers the problem of determining how many barrels of crude oil an oil-producing and exporting country should produce annually for export along with several other important problems that decision-makers in the crude oil industry face and discusses procedures for finding optimum solutions for them. It considers the important Objective Functions they need in making these critical decisions, and discusses procedures to find the best solutions. Outputs from the treatment units, in an oil refinery are only semi-finished products; these are blended into finished products like gasoline, diesel oil, etc., meeting various specifications that the marketplace demands. The book discusses models for solving these problems optimally with examples.

This book mainly introduces the latest development of generalized intuitionistic multiplicative fuzzy calculus and its application. The book pursues three major objectives: (1) to introduce the calculus models with concrete mathematical expressions for generalized intuitionistic multiplicative fuzzy information; (2) to introduce new information fusion methods based on the definite integral models; and (3) to clarify the involved approaches bymilitary case. The book is especially valuable for readers to understand how the theoretical framework of generalized intuitionistic multiplicative fuzzy calculus is constructed, not only discrete or continuous but also correlative (generalized) intuitionistic (multiplicative) fuzzy information is aggregated based on the definite integral models and the theory with a military practice is integrated, which would deepen the understanding and give researchers more inspiration in practical decision analysis under uncertainties.

Why are vast sums spent on controlling some risks but not others? Is there any logic to the techniques we use in risk regulation? These are key questions explored in The Government of Risk. This book exposes the components of risk regulation systems and examines their interaction and explanation. The approach employed is of a high policy relevance as well as of considerable theoretical importance.

Analytics is one of a number of terms which are used to describe a data-driven more scientific approach to management. Ability in analytics is an essential management skill: knowledge of data and analytics helps the manager to analyze decision situations, prevent problem situations from arising, identify new opportunities, and often enables many millions of dollars to be added to the bottom line for the organization. The objective of this book is to introduce analytics from the perspective of the general manager of a corporation. Rather than examine the details or attempt an encyclopaedic review of the field, this text emphasizes the strategic role that analytics is playing in globally competitive corporations today. The chapters of this book are organized in two main parts. The first part introduces a problem area and presents some basic analytical concepts that have been successfully used to address the problem area. The objective of this material is to provide the student, the manager of the future, with a general understanding of the tools and techniques used by the analyst.

Based on courses developed by the author over several years, this book provides access to a broad area of research that is not available in separate articles or books of readings. Topics covered include the meaning and measurement of risk, general single-period portfolio problems, mean-variance analysis and the Capital Asset Pricing Model, the Arbitrage Pricing Theory, complete markets, multiperiod portfolio problems and the Intertemporal Capital Asset Pricing Model, the Black-Scholes option pricing model and contingent claims analysis, 'risk-neutral' pricing with Martingales, Modigliani-Miller and the capital structure of the firm, interest rates and the term structure, and others.

Computing has become essential for the modeling, analysis, and optimization of systems. This book is devoted to algorithms, computational analysis, and decision models. The chapters are organized in two parts: optimization models of decisions and models of pricing and equilibria.

In real-life scenarios, service management involves complex decision-making processes usually affected by random or stochastic variables. Under such uncertain conditions, the development and use of robust and flexible strategies, algorithms, and methods can provide the quantitative information necessary to make better business decisions. Decision Making in Service Industries: A Practical Approach explores the challenges that must be faced to provide intelligent strategies for efficient management and decision making that will increase your organization s competitiveness and profitability.
The book provides insight and understanding into practical and methodological issues related to decision-making processes under uncertainty in service industries. It examines current and future trends regarding how these decision-making processes can be efficiently performed for better design of service systems by using probabilistic algorithms as well as hybrid and simulation-based approaches.

Traditionally, many quantitative tools have been developed to make decisions in production companies. This book explores how to use these tools for making decisions inside service industries. Thus, the authors tackle strategic, tactical, and operational problems in service companies with the help of suitable quantitative models such as heuristic and metaheuristic algorithms, simulation, or queuing theory.

Generally speaking, decision making is a hard task in business fields. Making the issue more complex, most service companies problems are related to the uncertainty of the service demand. This book sheds light on these types of decision problems. It provides studies that demonstrate the suitability of quantitative methods to make the right decisions. Consequently, this book presents the business analytics needed to make strategic decisions in service industries.

Meta-analysis, decision analysis, and cost-effectiveness analysis are the cornerstones of evidence-based medicine. These related quantitative methods have become essential tools in the formulation of clinical and public policy based on the synthesis of evidence. All three methods are taught with increasing frequency in medical schools and schools of public health and in health policy courses at the undergraduate and graduate level. This book is a lucid introduction, and will serve the needs of students taking introductory courses that cover these topics. It will also be useful to clinicians and policymakers who need to understand the quantitative underpinnings of the methods in order to best apply the information that derives from them. The second edition of this popular book adds new material on cumulative meta-analysis as a method to explore heterogeneity. The coverage of cost-effectiveness analysis has been brought into close alignment with recommendations of the U.S. Public Health Panel on Cost-Effectiveness Analysis in Health and Medicine. Many of the examples have been replaced with more current examples, and all of the material has been updated to reflect recent advances in the methods and the emergence of consensus about some previously controversial issues. analysis. These three closely related methods have become even more important for synthesizing research since the first edition was published in 1994. And they have gained legitimacy as tools for guiding health policy. In the Second Edition, Petitti has added new material on cumlative meta-analysis and the exploration of heterogeneity, incorporated recommendations for standardizing the conduct of cost-effectiveness analysis, and updated the rest of the text.

The authors of this book argue that firms succeed or fail in their industries according to the degree that they are able to change what they do to meet changing market decisions. The authors present a framework for managing the process of organizational transformation, and the tools that are necessary to manage that change.

Customer and Business Analytics: Applied Data Mining for Business Decision Making Using R explains and demonstrates, via the accompanying open-source software, how advanced analytical tools can address various business problems. It also gives insight into some of the challenges faced when deploying these tools. Extensively classroom-tested, the text is ideal for students in customer and business analytics or applied data mining as well as professionals in small- to medium-sized organizations. The book offers an intuitive understanding of how different analytics algorithms work. Where necessary, the authors explain the underlying mathematics in an accessible manner. Each technique presented includes a detailed tutorial that enables hands-on experience with real data. The authors also discuss issues often encountered in applied data mining projects and present the CRISP-DM process model as a practical framework for organizing these projects. Showing how data mining can improve the performance of organizations, this book and its R-based software provide the skills and tools needed to successfully develop advanced analytics capabilities.

Games, or contexts of strategic interaction, pervade and suffuse our lives and the lives of all organisms. How are we to make sense of and cope with such situations? How should an agent play? When will and when won't cooperation arise and be maintained? Using examples and a careful digestion of the literature, Agents, Games, and Evolution: Strategies at Work and Play addresses these encompassing themes throughout, and is organized into four parts: Part I introduces classical game theory and strategy selection. It compares ideally rational and the "naturalist" approach used by this book, which focuses on how actual agents chose their strategies, and the effects of these strategies on model systems. Part II explores a number of basic games, using models in which agents have fixed strategies. This section draws heavily on the substantial literature associated with the relevant application areas in the social sciences. Part III reviews core results and applications of agent-based models in which strategic interaction is present and for which design issues have genuine practical import. This section draws heavily on the substantial literature associated with the application area to hand. Part IV addresses miscellaneous topics in strategic interaction, including lying in negotiations, reasoning by backward induction, and evolutionary models. Modeled after the authors' Agents, Games, and Evolution course at the University of Pennsylvania, this book keeps mathematics to a minimum, focusing on computational strategies and useful methods for dealing with a variety of situations.

In the last few years, competition has become increasingly more complex, variable and dynamic, as can be seen in phenomena like globalization and technological acceleration. To cope with the dynamism and uncertainty of competition, enterprises need capabilities that enable them to respond to competition, as well as to improve their analytical skills and knowledge in order to better manage new strategic projects.

Strategic analysis uses both quantitative and qualitative tools to understand both competitive contexts and available company resources. In Strategic Analysis: Processes and Tools, author Andrea Beretta Zanoni develops a theory of strategic analysis and offers models for the application of strategic analysis tools during all phases of the process including planning and decision-making, the development of control, and the formulation of a strategic diagnosis.

Under intense scrutiny for the last few decades, Multiple Objective Decision Making (MODM) has been useful for dealing with the multiple-criteria decisions and planning problems associated with many important applications in fields including management science, engineering design, and transportation. Rough set theory has also proved to be an effective mathematical tool to counter the vague description of objects in fields such as artificial intelligence, expert systems, civil engineering, medical data analysis, data mining, pattern recognition, and decision theory.

Rough Multiple Objective Decision Making is perhaps the first book to combine state-of-the-art application of rough set theory, rough approximation techniques, and MODM. It illustrates traditional techniques-and some that employ simulation-based intelligent algorithms-to solve a wide range of realistic problems. Application of rough theory can remedy two types of uncertainty (randomness and fuzziness) which present significant drawbacks to existing decision-making methods, so the authors illustrate the use of rough sets to approximate the feasible set, and they explore use of rough intervals to demonstrate relative coefficients and parameters involved in bi-level MODM. The book reviews relevant literature and introduces models for both random and fuzzy rough MODM, applying proposed models and algorithms to problem solutions.

Given the broad range of uses for decision making, the authors offer background and guidance for rough approximation to real-world problems, with case studies that focus on engineering applications, including construction site layout planning, water resource allocation, and resource-constrained project scheduling. The text presents a general framework of rough MODM, including basic theory, models, and algorithms, as well as a proposed methodological system and discussion of future research.