Download or read book Exploring the Role Data driven Decision making Under Uncertainty written by Munyaradzi James Hove and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Decision making requires managers to carefully analyse the business environment and make sense of existing information in a bid to direct and influence particular courses of action for organisations. However, there is complexity of this process in uncertainty, such as that exemplified by the year 2020 due to the effects of the global COVID-19 pandemic. Within this context of uncertainty, and given the proliferation of big data, the role of data-driven decision-making under uncertainty is yet to be established. This research explored the role of data-driven decision-making under uncertainty, including the preconditions for, enablers and functional benefits thereof. This research was a qualitative study through 10 in-depth interviews with South African senior managers, who made use of data to support their decision-making processes. The understanding of the role of data-driven decision-making was explored using thematic analysis. The researcher presents an integrated model of the data-driven decision-making process under uncertainty, that can be adopted by organisations and decision-makers faced with uncertainty, in need of improved rationality, enhanced objectivity and more accurate probability modelling under uncertainty. This integrated model outlines key preconditions for data-driven decisioning under uncertainty and the challenges categorised as organisation specific, external to the organisation, inherent to the data and data management practices. The integrated model also outlines key enablers for data-driven decisioning under uncertainty as well as the perceived benefits, pivoting between strategic and application benefits.
Download or read book Decision Making Under Uncertainty written by Grani Adiwena Hanasusanto and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Decision Making under Uncertainty written by Kerstin Preuschoff and published by Frontiers Media SA. This book was released on 2015-06-16 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: Most decisions in life are based on incomplete information and have uncertain consequences. To successfully cope with real-life situations, the nervous system has to estimate, represent and eventually resolve uncertainty at various levels. A common tradeoff in such decisions involves those between the magnitude of the expected rewards and the uncertainty of obtaining the rewards. For instance, a decision maker may choose to forgo the high expected rewards of investing in the stock market and settle instead for the lower expected reward and much less uncertainty of a savings account. Little is known about how different forms of uncertainty, such as risk or ambiguity, are processed and learned about and how they are integrated with expected rewards and individual preferences throughout the decision making process. With this Research Topic we aim to provide a deeper and more detailed understanding of the processes behind decision making under uncertainty.
Download or read book Decision Making under Deep Uncertainty written by Vincent A. W. J. Marchau and published by Springer. This book was released on 2019-04-04 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book focuses on both the theory and practice associated with the tools and approaches for decisionmaking in the face of deep uncertainty. It explores approaches and tools supporting the design of strategic plans under deep uncertainty, and their testing in the real world, including barriers and enablers for their use in practice. The book broadens traditional approaches and tools to include the analysis of actors and networks related to the problem at hand. It also shows how lessons learned in the application process can be used to improve the approaches and tools used in the design process. The book offers guidance in identifying and applying appropriate approaches and tools to design plans, as well as advice on implementing these plans in the real world. For decisionmakers and practitioners, the book includes realistic examples and practical guidelines that should help them understand what decisionmaking under deep uncertainty is and how it may be of assistance to them. Decision Making under Deep Uncertainty: From Theory to Practice is divided into four parts. Part I presents five approaches for designing strategic plans under deep uncertainty: Robust Decision Making, Dynamic Adaptive Planning, Dynamic Adaptive Policy Pathways, Info-Gap Decision Theory, and Engineering Options Analysis. Each approach is worked out in terms of its theoretical foundations, methodological steps to follow when using the approach, latest methodological insights, and challenges for improvement. In Part II, applications of each of these approaches are presented. Based on recent case studies, the practical implications of applying each approach are discussed in depth. Part III focuses on using the approaches and tools in real-world contexts, based on insights from real-world cases. Part IV contains conclusions and a synthesis of the lessons that can be drawn for designing, applying, and implementing strategic plans under deep uncertainty, as well as recommendations for future work. The publication of this book has been funded by the Radboud University, the RAND Corporation, Delft University of Technology, and Deltares.
Download or read book Decision Making Under Uncertainty written by Mykel J. Kochenderfer and published by MIT Press. This book was released on 2015-07-24 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.
Download or read book Data Driven Business Decisions written by Chris J. Lloyd and published by Wiley. This book was released on 2012-01-13 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: A hands-on guide to the use of quantitative methods and software for making successful business decisions The appropriate use of quantitative methods lies at the core of successful decisions made by managers, researchers, and students in the field of business. Providing a framework for the development of sound judgment and the ability to utilize quantitative and qualitative approaches, Data Driven Business Decisions introduces readers to the important role that data plays in understanding business outcomes, addressing four general areas that managers need to know about: data handling and Microsoft Excel, uncertainty, the relationship between inputs and outputs, and complex decisions with trade-offs and uncertainty. Grounded in the author's own classroom approach to business statistics, the book reveals how to use data to understand the drivers of business outcomes, which in turn allows for data-driven business decisions. A basic, non-mathematical foundation in statistics is provided, outlining for readers the tools needed to link data with business decisions; account for uncertainty in the actions of others and in patterns revealed by data; handle data in Excel; translate their analysis into simple business terms; and present results in simple tables and charts. The author discusses key data analytic frameworks, such as decision trees and multiple regression, and also explores additional topics, including: Use of the Excel functions Solver and Goal Seek Partial correlation and auto-correlation Interactions and proportional variation in regression models Seasonal adjustment and what it reveals Basic portfolio theory as an introduction to correlations Chapters are introduced with case studies that integrate simple ideas into the larger business context, and are followed by further details, raw data, and motivating insights. Algebraic notation is used only when necessary, and throughout the book, the author utilizes real-world examples from diverse areas such as market surveys, finance, economics, and business ethics. Excel add-ins StatproGo and TreePlan are showcased to demonstrate execution of the techniques, and a related website features extensive programming instructions as well as insights, data sets, and solutions to problems included in the material. The enclosed CD contains the complete book in electronic format, including all presented data, supplemental material on the discussed case files, and links to exercises and solutions. Data Dr...
Download or read book Data Driven Decision Making written by Dr. Avinash S. Jagtap and published by Lulu.com. This book was released on with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Data Driven Decision Making Under Uncertainty with Applications in Healthcare and Energy Management written by Saeed Ghodsi and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Decision-making under uncertainty has been studied for a long time by the operations management research community. In the past, uncertainty models were often derived based on domain knowledge. However, the availability of vast amounts of data in the recent years has shifted interests towards data-driven approaches for uncertainty quantification. More specifically, statistical models are employed within this framework for characterizing the uncertain components of a stochastic optimization problem based on historical data. In this dissertation, we focus on applications of data-driven decision-making under uncertainty in the healthcare and energy management sectors. The first part of our work provides a mathematical framework for efficient call assignment under Direct Load Control (DLC) contracts (i.e. an incentive-based demand-response program that is widely used by utility firms for balancing the supply and demand of electricity during peak times). Specifically, we employ a model for forecasting energy consumption and develop a large-scale integer stochastic dynamic optimization problem. We then propose a novel hierarchical approximation scheme for efficient execution of the contracts. We evaluate the quality of our proposed approach using real-world data obtained from California Independent System Operator (CAISO), which is the umbrella organization of utility firms in California. A large utility firm in California has implemented our model and informed us that they have experienced a 4\% additional r duction in their cost. Following a similar predict-then-optimize methodological framework, the second part of this dissertation studies data-driven healthcare intervention planning. Specifically, we develop a continuous-time latent-space Markovian model for describing disease progression based on discrete-time irregularly-spaced observations. Our model is capable of incorporating the effect of interventions on progression of disease. We discuss the computational challenges of parameter estimation for this model and present a novel efficient estimation approach based on the Expectation-Maximization (EM) algorithm. A population-level optimization model for intervention planning in the behavioral healthcare sector is then developed using the fitted disease progression model. Afterward, we present an extension of the model, which is more appropriate for medical healthcare domains such as cancer maintenance therapy, and formulate an EM algorithm for estimating the model parameters. Finally, we develop an individual-level intervention planning problem based on the patient's historical data using the estimated model.
Download or read book Proceedings of International Conference on Information Technology and Applications written by Abrar Ullah and published by Springer Nature. This book was released on with total page 629 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Decision Making under Deep Uncertainty written by Vincent A. W. J. Marchau and published by Springer. This book was released on 2019-04-27 with total page 405 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book focuses on both the theory and practice associated with the tools and approaches for decisionmaking in the face of deep uncertainty. It explores approaches and tools supporting the design of strategic plans under deep uncertainty, and their testing in the real world, including barriers and enablers for their use in practice. The book broadens traditional approaches and tools to include the analysis of actors and networks related to the problem at hand. It also shows how lessons learned in the application process can be used to improve the approaches and tools used in the design process. The book offers guidance in identifying and applying appropriate approaches and tools to design plans, as well as advice on implementing these plans in the real world. For decisionmakers and practitioners, the book includes realistic examples and practical guidelines that should help them understand what decisionmaking under deep uncertainty is and how it may be of assistance to them. Decision Making under Deep Uncertainty: From Theory to Practice is divided into four parts. Part I presents five approaches for designing strategic plans under deep uncertainty: Robust Decision Making, Dynamic Adaptive Planning, Dynamic Adaptive Policy Pathways, Info-Gap Decision Theory, and Engineering Options Analysis. Each approach is worked out in terms of its theoretical foundations, methodological steps to follow when using the approach, latest methodological insights, and challenges for improvement. In Part II, applications of each of these approaches are presented. Based on recent case studies, the practical implications of applying each approach are discussed in depth. Part III focuses on using the approaches and tools in real-world contexts, based on insights from real-world cases. Part IV contains conclusions and a synthesis of the lessons that can be drawn for designing, applying, and implementing strategic plans under deep uncertainty, as well as recommendations for future work.
Download or read book Decision Intelligence written by Thorsten Heilig and published by John Wiley & Sons. This book was released on 2023-10-31 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dramatically improve your decisions with data and AI In Decision Intelligence: Transform Your Team and Organization with AI-Driven Decision-Making, a team of pioneering decision and AI strategists delivers a digestible and hands-on resource for professionals at every part of the decision-making journey. The book discusses the latest technology and approaches that bridge the gap between behavioral science, data science, and technological innovation. Discover how leaders from various industries and environments are using data and AI to make better future decisions, taking both human as well as business factors into account. This book covers: A demystifying behind-the-scenes peek inside how AI models, forecasts, and optimization for business challenges really work, and why they open up entirely new possibilities. A business-ready introduction to decision intelligence, exploring why traditional decision-making strategies are outdated and how to transition to decision-intelligence. The evolution of Decision Intelligence, coming from analytics and modern techniques like process mining and robotic process automation An examination of decision intelligence at the organizational level, including discussions of agile transformation, transparent organizational culture, and why psychological safety is a crucial enabler for new ways of decision-making in modern companies An overview of why (and where exactly) AI still needs human expertise and how to incorporate this topic in daily planning and decision making Decision Intelligence is essential reading for managers, executives, board members, other business leaders and soon-to-be leaders looking to improve the quality, adaptability, and speed of their decision-making. Praise for Decision Intelligence "In Decision Intelligence, Thorsten Heilig and Ilhan Scheer build a compelling case for the world of tomorrow’s version of decision-making.” ―Martin Lindstrom, New York Times best-selling author "Decision Intelligence will be one of the big topics for this decade and completely change the way organizations manage, plan, and operate. This book provides a comprehensive guide from the basics to the applications." ―Niklas Jansen, Entrepreneur and Tech Investor, Founding Partner Interface Capital and Co-Founder Blinkist "The book impressively demonstrates the potential and entry points into the world of AI-powered decision making. A very valuable reading for managers and their organizations". ―Michael Kleinemeier, Member of the Merck KG Board of Partners, former Member of the SAP SE Executive Board “The AI hype perfectly captured, easy to understand, de-mystified and mapped to clear use cases - a must-read for today's managers.” ―Dr. Daniela Gerd tom Markotten, Member of the Management Board for Digitalization and Technology, Deutsche Bahn AG
Download or read book Dynamics in Logistics written by Michael Freitag and published by Springer Nature. This book was released on 2021-12-02 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book highlights the interdisciplinary aspects of logistics research. Featuring empirical, methodological, and practice-oriented articles, it addresses the modelling, planning, optimization and control of processes. Chiefly focusing on supply chains, logistics networks, production systems, and systems and facilities for material flows, the respective contributions combine research on classical supply chain management, digitalized business processes, production engineering, electrical engineering, computer science and mathematical optimization. To celebrate 25 years of interdisciplinary and collaborative research conducted at the Bremen Research Cluster for Dynamics in Logistics (LogDynamics), in this book hand-picked experts currently or formerly affiliated with the Cluster provide retrospectives, present cutting-edge research, and outline future research directions.
Download or read book Decision making Under Uncertainty written by Jackie Baek and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The surge of data and technological advances over the past decade has immensely increased the use of algorithms to automate decisions for a plethora of problems. This thesis focuses on developing data-driven methodologies for sequential decision-making under uncertainty. Specifically, we develop solutions to address practical issues that can arise when operationalizing mathematical models, ranging from general methodologies to applications in healthcare and revenue management. First, we study an issue of fairness that arises in online learning. In online learning, it is well-known that good strategies must explore; but exploration is associated with a cost, stemming from playing actions that are eventually revealed to be sub-optimal. We study how this cost of exploration is distributed amongst groups in a bandit setting. We leverage the theory of axiomatic bargaining, and the Nash bargaining solution in particular, to formalize what might constitute a fair division of the cost of exploration across groups. On the one hand, we show that any regret-optimal policy strikingly results in the least fair outcome: such policies will perversely leverage the most 'disadvantaged' groups when they can. More constructively, we derive policies that are optimally fair and simultaneously enjoy a small 'price of fairness'. We illustrate the relative merits of our algorithmic framework with a case study on contextual bandits for warfarin dosing where we are concerned with the cost of exploration across multiple races and age groups. Next, we study the classical problem of minimizing regret for multi-armed bandits. In this classic problem, there are several existing policies that are provably asymptotically optimal, but it is well-known that the empirical performance of these policies can vary greatly. We develop a new policy that we dub TS-UCB, which is a policy that combines ideas from two prominent policies for multi-armed bandits, Thompson sampling and upper confidence bound. We show that TS-UCB achieves materially lower regret on a comprehensive suite of synthetic and real-world datasets, and we establish optimal regret guarantees for TS-UCB for both the K-armed and linear bandit models. Lastly, we study a decision-making problem in a revenue management setting. We study the network revenue management problem, an online allocation problem in which products are sold to a stream of arriving customers, where each product consumes a subset of capacity-constrained resources. We show that certain network structures can be exploited to improve both theoretical and empirical performance over existing, 'one-size-fits-all' approaches. Specifically, we study instances with a matroid sub-structure, which can be motivated by several classical supply chain constraints involving postponement and process flexibility. We prove that our policy improves over existing theoretical guarantees under this structure, and these results are empirically supported by numerical simulations.
Download or read book Completing the Forecast written by National Research Council and published by National Academies Press. This book was released on 2006-10-09 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: Uncertainty is a fundamental characteristic of weather, seasonal climate, and hydrological prediction, and no forecast is complete without a description of its uncertainty. Effective communication of uncertainty helps people better understand the likelihood of a particular event and improves their ability to make decisions based on the forecast. Nonetheless, for decades, users of these forecasts have been conditioned to receive incomplete information about uncertainty. They have become used to single-valued (deterministic) forecasts (e.g., "the high temperature will be 70 degrees Farenheit 9 days from now") and applied their own experience in determining how much confidence to place in the forecast. Most forecast products from the public and private sectors, including those from the National Oceanographic and Atmospheric Administration's National Weather Service, continue this deterministic legacy. Fortunately, the National Weather Service and others in the prediction community have recognized the need to view uncertainty as a fundamental part of forecasts. By partnering with other segments of the community to understand user needs, generate relevant and rich informational products, and utilize effective communication vehicles, the National Weather Service can take a leading role in the transition to widespread, effective incorporation of uncertainty information into predictions. "Completing the Forecast" makes recommendations to the National Weather Service and the broader prediction community on how to make this transition.
Download or read book Decision Making Under Uncertainty written by Claude Greengard and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the ideal world, major decisions would be made based on complete and reliable information available to the decision maker. We live in a world of uncertainties, and decisions must be made from information which may be incomplete and may contain uncertainty. The key mathematical question addressed in this volume is "how to make decision in the presence of quantifiable uncertainty." The volume contains articles on model problems of decision making process in the energy and power industry when the available information is noisy and/or incomplete. The major tools used in studying these problems are mathematical modeling and optimization techniques; especially stochastic optimization. These articles are meant to provide an insight into this rapidly developing field, which lies in the intersection of applied statistics, probability, operations research, and economic theory. It is hoped that the present volume will provide entry to newcomers into the field, and stimulation for further research.
Download or read book Multicriteria Decision Making Under Conditions of Uncertainty written by Petr Ekel and published by John Wiley & Sons. This book was released on 2019-11-07 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: A guide to the various models and methods to multicriteria decision-making in conditions of uncertainty presented in a systematic approach Multicriteria Decision-Making under Conditions of Uncertainty presents approaches that help to answer the fundamental questions at the center of all decision-making problems: "What to do?" and "How to do it?" The book explores methods of representing and handling diverse manifestations of the uncertainty factor and a multicriteria nature of problems that can arise in system design, planning, operation, and control. The authors—noted experts on the topic—and their book covers essential questions, including notions and fundamental concepts of fuzzy sets, models and methods of multiobjective as well as multiattribute decision-making, the classical approach to dealing with uncertainty of information and its generalization for analyzing multicriteria problems in condition of uncertainty, and more. This comprehensive book contains information on "harmonious solutions" in multiobjective problem-solving (analyzing “i>X, F> models), construction and analysis of “i>X, R/i” models, results aimed at generating robust solutions in analyzing multicriteria problems under uncertainty, and more. In addition, the book includes illustrative examples of various applications, including real-world case studies related to the authors’ various industrial projects. This important resource: Explains the design and processing aspect of fuzzy sets, including construction of membership functions, fuzzy numbers, fuzzy relations, aggregation operations, and fuzzy sets transformations Describes models of multiobjective decision-making (“i>X. M/i” models), their analysis on the basis of using the Bellman-Zadeh approach to decision-making in a fuzzy environment, and their diverse applications, including multicriteria allocation of resources Investigates models of multiattribute decision-making (“i>X, R/i” models) and their analysis on the basis of the construction and processing of fuzzy preference relations as well as demonstrating their applications to solve diverse classes of multiattribute problems Explores notions of payoff matrices and fuzzy-set-based generalization and modification of the classic approach to decision-making under conditions of uncertainty to generate robust solutions in analyzing multicriteria problems Written for students, researchers and practitioners in disciplines in which decision-making is of paramount relevance, Multicriteria Decision-Making under Conditions of Uncertainty presents a systematic and current approach that encompasses a range of models and methods as well as new applications.
Download or read book Exploring the Advancements and Future Directions of Digital Twins in Healthcare 6 0 written by Dubey, Archi and published by IGI Global. This book was released on 2024-07-18 with total page 468 pages. Available in PDF, EPUB and Kindle. Book excerpt: The healthcare industry is increasingly complex, demanding personalized treatments and efficient operational processes. Traditional research methods need help to keep pace with these demands, often leading to inefficiencies and suboptimal outcomes. Integrating digital twin technology presents a promising solution to these challenges, offering a virtual platform for modeling and simulating complex healthcare scenarios. However, the full potential of digital twins still needs to be explored mainly due to a lack of comprehensive guidance and practical insights for researchers and practitioners. Exploring the Advancements and Future Directions of Digital Twins in Healthcare 6.0 is not just a theoretical exploration. It is a practical guide that bridges the gap between theory and practice, offering real-world case studies, best practices, and insights into personalized medicine, real-time patient monitoring, and healthcare process optimization. By equipping you with the knowledge and tools needed to effectively integrate digital twins into your healthcare research and operations, this book is a valuable resource for researchers, academicians, medical practitioners, scientists, and students.
