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Exploring The Role Data Driven Decision Making Under Uncertainty
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Book Synopsis Exploring the Role Data-driven Decision-making Under Uncertainty by : Munyaradzi James Hove
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 Data Driven written by Thomas C. Redman and published by Harvard Business Press. This book was released on 2008-09-22 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: Your company's data has the potential to add enormous value to every facet of the organization -- from marketing and new product development to strategy to financial management. Yet if your company is like most, it's not using its data to create strategic advantage. Data sits around unused -- or incorrect data fouls up operations and decision making. In Data Driven, Thomas Redman, the "Data Doc," shows how to leverage and deploy data to sharpen your company's competitive edge and enhance its profitability. The author reveals: · The special properties that make data such a powerful asset · The hidden costs of flawed, outdated, or otherwise poor-quality data · How to improve data quality for competitive advantage · Strategies for exploiting your data to make better business decisions · The many ways to bring data to market · Ideas for dealing with political struggles over data and concerns about privacy rights Your company's data is a key business asset, and you need to manage it aggressively and professionally. Whether you're a top executive, an aspiring leader, or a product-line manager, this eye-opening book provides the tools and thinking you need to do that.
Book Synopsis Decision Making Under Uncertainty by : Grani Adiwena Hanasusanto
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:
Book Synopsis Decision Making under Deep Uncertainty by : Vincent A. W. J. Marchau
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.
Book Synopsis Decision Making under Uncertainty by : Kerstin Preuschoff
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.
Book Synopsis Decision Making Under Uncertainty by : Mykel J. Kochenderfer
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.
Book Synopsis Data Driven Business Decisions by : Chris J. Lloyd
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...
Book Synopsis HBR Guide to Data Analytics Basics for Managers (HBR Guide Series) by : Harvard Business Review
Download or read book HBR Guide to Data Analytics Basics for Managers (HBR Guide Series) written by Harvard Business Review and published by Harvard Business Press. This book was released on 2018-03-13 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Don't let a fear of numbers hold you back. Today's business environment brings with it an onslaught of data. Now more than ever, managers must know how to tease insight from data--to understand where the numbers come from, make sense of them, and use them to inform tough decisions. How do you get started? Whether you're working with data experts or running your own tests, you'll find answers in the HBR Guide to Data Analytics Basics for Managers. This book describes three key steps in the data analysis process, so you can get the information you need, study the data, and communicate your findings to others. You'll learn how to: Identify the metrics you need to measure Run experiments and A/B tests Ask the right questions of your data experts Understand statistical terms and concepts Create effective charts and visualizations Avoid common mistakes
Book Synopsis Data-Driven Decision Making by : Dr. Avinash S. Jagtap
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:
Book Synopsis Data-Driven Decision-Making Under Uncertainty with Applications in Healthcare and Energy Management by : Saeed Ghodsi
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.
Book Synopsis Dynamics in Logistics by : Michael Freitag
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.
Book Synopsis Proceedings of International Conference on Information Technology and Applications by : Abrar Ullah
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:
Book Synopsis Decision Making under Deep Uncertainty by : Vincent A. W. J. Marchau
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.
Book Synopsis Decision Intelligence by : Thorsten Heilig
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
Book Synopsis Uncertainty Deconstructed by : Bruce Garvey
Download or read book Uncertainty Deconstructed written by Bruce Garvey and published by Springer Nature. This book was released on 2022-08-26 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book argues that uncertainty is not really uncertainty at all but just demonstrates a lack of vision and willingness to think about the unthinkable – good and bad. The task of accepting that uncertainty is about exploring the possible, rather than the impossible has to be taken on board by strategists, policy developers, and political leaders, if we are to meet the challenges that an ever changing world is throwing at us. The term “unknown – unknowns” is ubiquitous, albeit the vast majority of future uncertain events do not fall into this category. However, it has been used to absolve decision makers from criticism post-event, whereas poor foresight is the prime culprit and that most future uncertainties are “known-unknowns” or “inevitable surprises”. This re-positioning of uncertainties can help mitigate the impact of such risks through better foresight aware contingency planning. The enemy is not uncertainty itself but our lack of imagination when trying to visualize the future – we need to transform our behaviour. To better understand uncertainty we have to deconstruct it and get to grips with its component parts. Three main questions are posed and practical approaches presented: What are the main structural components that make up the conditions under which uncertainty operates? What scenario lenses can be used when exploring uncertainty? What behavioural factors do we need to consider when analysing the human responses to uncertainty? Practitioners, having to deal with making better decisions under uncertainty, will find the book a useful guide. Endorsements for the book: "With this book, Bruce Garvey performs a great service for consultants, planners and, indeed, anyone whose job involves a degree of speculation about what will happen in the future. Through a comprehensive survey of methods, tools and techniques, he provides a practical guide to unpacking the uncertainty that besets all human endeavour. This is no dry academic treatise: it deals with highly contemporary topics such as “fake news” – part of a fascinating dissection of “dark data” – and how our biases and preconceptions shape our views. The book finishes with three case studies dealing with the Covid-19 pandemic, social mobility and inequality, and achieving net zero – all topics that are sorely in need of the critical thinking and analysis skills described previously. No one can completely eliminate “20:20 hindsight” from all business decisions but readers applying the lessons of this book may find themselves saying “if only we’d known...” less frequently." -- Nick Bush, Director - CMCE (Centre for Management Consulting Excellence) "Academic literature and practical guides to uncertainty management are disparate: this exciting edition brings it all together. Principal author, Bruce Garvey, recognises the erroneous attribution of many recent events to unforeseeable uncertainty (‘unknown unknowns’), calling these out as inevitable surprises (or ‘unknown knowns’), a category of uncertainty that is typically overlooked. Garvey describes critical dimensions of uncertainty, before examining scenarios and behavioural aspects, the latter being a ‘hidden influencer’ which is too often neglected. The guidebook contains a variety of methods, tools and techniques, including several that deserve more use, and contains a detailed glossary and reference list. Practical advice covers topics such as identifying weak signals for use in scenario development and overcoming cognitive dissonance. This well-structured and engagingly written guide should serve as a standard text for students, academics and practitioners across policy making, business, and industry." -- Dr. Geoff Darch, Water Resources Strategy Manager, Anglian Water. Co-Founder, Analysis under Uncertainty for Decision-Makers (AU4DM) Network "This is a valuable companion volume to John Kay and Mervyn King's Radical Uncertainty - and it is a necessary corrective to the physics envy of disciplines such as economics which achieve a false sense of certainty by creating highly plausible but unreliable simplifications of things through over generalisation - leading to simplistic proposals for interventions which can only rightly be judged through a lens of complexity and probability. I would like to be more optimistic about the ultimate effects of books of this kind - and in some fields, perhaps in military decision-making and defence I am quite optimistic. In such fields, people tend to approach decision-making through the assumption that things will go wrong, and that the effects of any mistakes will be very keenly, perhaps fatally experienced. In business and softer social policy-making, I fear the battle will be much harder. In such fields as politics and business, it is often better for the reputation "as Keynes remarked, "to fail conventionally than to succeed unconventionally." In such fields, it is more important to make defensible decisions than to make good decisions, so an artificial sense of logical certainty will perhaps always hold an unhealthy appeal. But here's hoping anyway!" -- Rory Sutherland, Vice Chairman, Ogilvy Group "Here is a most insightful book, which holistically examines the ‘world of uncertainty', particularly as it impacts sense- to decision-making processes for many different stakeholders. Both scholars and practitioners, strategists to operators, soon gain from reading. Journeying from theory to practice, we embark on a comprehensive definition of uncertainty to subsequently become better equipped for its greater contemporary navigation when going forward, all elucidated by several well-structured scenarios and case-study examples. How uncertainty relates to risk (both qualitative and quantitative) is systematically charted, articulating their close interactivity. Forming a successful guide, this book has much enduring reference value and is therefore deserving of being readily retrievable as events and developments benefit from their improved understanding. Uncertainty can demonstrably be negotiated much more effectively. Alternative situations and conditions of denial, lamented as ‘we should have (fore)seen that’, no longer stand as acceptable when it comes to anticipating futures ahead. With this book, further help is now at hand." -- Adam D.M. Svendsen, PhD, International Intelligence & Defence Strategist, Researcher, Analyst, Educator & Consultant
Book Synopsis Completing the Forecast by : National Research Council
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.
Book Synopsis Decision-making Under Uncertainty by : Jackie Baek
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.
