Guide to Computational Modelling for Decision Processes
Guide to Computational Modelling for Decision Processes

Author: Stuart Berry

Publisher: Springer

Published: 2017-04-13

Total Pages: 390

ISBN-13: 3319554174

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This interdisciplinary reference and guide provides an introduction to modeling methodologies and models which form the starting point for deriving efficient and effective solution techniques, and presents a series of case studies that demonstrate how heuristic and analytical approaches may be used to solve large and complex problems. Topics and features: introduces the key modeling methods and tools, including heuristic and mathematical programming-based models, and queueing theory and simulation techniques; demonstrates the use of heuristic methods to not only solve complex decision-making problems, but also to derive a simpler solution technique; presents case studies on a broad range of applications that make use of techniques from genetic algorithms and fuzzy logic, tabu search, and queueing theory; reviews examples incorporating system dynamics modeling, cellular automata and agent-based simulations, and the use of big data; supplies expanded descriptions and examples in the appendices.

Goal-Directed Decision Making
Goal-Directed Decision Making

Author: Richard W. Morris

Publisher: Academic Press

Published: 2018-08-23

Total Pages: 486

ISBN-13: 0128120991

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Goal-Directed Decision Making: Computations and Neural Circuits examines the role of goal-directed choice. It begins with an examination of the computations performed by associated circuits, but then moves on to in-depth examinations on how goal-directed learning interacts with other forms of choice and response selection. This is the only book that embraces the multidisciplinary nature of this area of decision-making, integrating our knowledge of goal-directed decision-making from basic, computational, clinical, and ethology research into a single resource that is invaluable for neuroscientists, psychologists and computer scientists alike. The book presents discussions on the broader field of decision-making and how it has expanded to incorporate ideas related to flexible behaviors, such as cognitive control, economic choice, and Bayesian inference, as well as the influences that motivation, context and cues have on behavior and decision-making. - Details the neural circuits functionally involved in goal-directed decision-making and the computations these circuits perform - Discusses changes in goal-directed decision-making spurred by development and disorders, and within real-world applications, including social contexts and addiction - Synthesizes neuroscience, psychology and computer science research to offer a unique perspective on the central and emerging issues in goal-directed decision-making

Decision Making, Affect, and Learning
Decision Making, Affect, and Learning

Author: Mauricio R. Delgado

Publisher: Attention and Performance

Published: 2011-03-24

Total Pages: 576

ISBN-13: 0199600430

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Focuses on decision making and emotional processing, investigating the psychological and neural systems underlying decision making, and the relationship with reward, affect, and learning. Considers neurodevelopmental and clinical aspects and looks at the applied aspects for other disciplines, including neuroeconomics.

Automatic Control, Robotics, and Information Processing
Automatic Control, Robotics, and Information Processing

Author: Piotr Kulczycki

Publisher: Springer Nature

Published: 2020-09-03

Total Pages: 843

ISBN-13: 3030485870

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This book presents a wide and comprehensive range of issues and problems in various fields of science and engineering, from both theoretical and applied perspectives. The desire to develop more effective and efficient tools and techniques for dealing with complex processes and systems has been a natural inspiration for the emergence of numerous fields of science and technology, in particular control and automation and, more recently, robotics. The contributions gathered here concern the development of methods and algorithms to determine best practices regarding broadly perceived decisions or controls. From an engineering standpoint, many of them focus on how to automate a specific process or complex system. From a tools-based perspective, several contributions address the development of analytic and algorithmic methods and techniques, devices and systems that make it possible to develop and subsequently implement the automation and robotization of crucial areas of human activity. All topics discussed are illustrated with sample applications.

Cloud Data Centers and Cost Modeling
Cloud Data Centers and Cost Modeling

Author: Caesar Wu

Publisher: Morgan Kaufmann

Published: 2015-02-27

Total Pages: 848

ISBN-13: 0128016884

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Cloud Data Centers and Cost Modeling establishes a framework for strategic decision-makers to facilitate the development of cloud data centers. Just as building a house requires a clear understanding of the blueprints, architecture, and costs of the project; building a cloud-based data center requires similar knowledge. The authors take a theoretical and practical approach, starting with the key questions to help uncover needs and clarify project scope. They then demonstrate probability tools to test and support decisions, and provide processes that resolve key issues. After laying a foundation of cloud concepts and definitions, the book addresses data center creation, infrastructure development, cost modeling, and simulations in decision-making, each part building on the previous. In this way the authors bridge technology, management, and infrastructure as a service, in one complete guide to data centers that facilitates educated decision making. - Explains how to balance cloud computing functionality with data center efficiency - Covers key requirements for power management, cooling, server planning, virtualization, and storage management - Describes advanced methods for modeling cloud computing cost including Real Option Theory and Monte Carlo Simulations - Blends theoretical and practical discussions with insights for developers, consultants, and analysts considering data center development

Computational Modeling for Industrial-Organizational Psychologists
Computational Modeling for Industrial-Organizational Psychologists

Author: Jeffrey B. Vancouver

Publisher: Taylor & Francis

Published: 2023-11-02

Total Pages: 342

ISBN-13: 1003815251

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This collection provides a primer to the process and promise of computational modeling for industrial-organizational psychologists. With contributions by global experts in the field, the book is designed to expand readers’ appreciation for computational modeling via chapters focused on key modeling achievements in domains relevant to industrial-organizational psychology, including decision making in organizations, diversity and inclusion, learning and training, leadership, and teams. To move the use of computational modeling forward, the book includes specific how-to-chapters on two of the most commonly used modeling approaches: agent-based modeling and system dynamics modeling. It also gives guidance on how to evaluate these models qualitatively and quantitatively, and offers advice on how to read, review, and publish papers with computational models. The authors provide an extensive description of the myriad of values computational modeling can bring to the field, highlighting how they offer a more transparent, precise way to represent theories and can be simulated to offer a test of the internal consistency of a theory and allow for predictions. This is accompanied by an overview of the history of computational modeling as it relates to I-O psychology. Throughout, the authors reflect on computational modeling’s journey, looking back to its history as they imagine its future in I-O psychology. Each contribution demonstrates the value and opportunities computational modeling can provide the individual researcher, research teams, and fields of I-O psychology and management. This volume is an ideal resource for anyone interested in computational modeling, from scholarly consumers to computational model creators.

Decision Making Under Uncertainty
Decision Making Under Uncertainty

Author: Mykel J. Kochenderfer

Publisher: MIT Press

Published: 2015-07-24

Total Pages: 350

ISBN-13: 0262331713

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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.

Interpretable Machine Learning
Interpretable Machine Learning

Author: Christoph Molnar

Publisher: Lulu.com

Published: 2020

Total Pages: 320

ISBN-13: 0244768528

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This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Guide to Business Modelling
Guide to Business Modelling

Author: John Tennent

Publisher: The Economist

Published: 2014-04-01

Total Pages: 345

ISBN-13: 1610395115

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Full of practical help on how to build the best, most flexible, and easy-to-use business models that can be used to analyze the upsides and downsides of any business project, this new edition of the Guide to Business Modeling is essential reading for the twenty-first century business leader. This radically revised guide to the increasingly important fine art of building business models using spreadsheets, the book describes models for evaluating everything from a modest business development to a major acquisition. Fully Excel 2010 aligned with enhanced Excel and business content More model evaluation techniques to help with business decision-making Helpful key point summaries New website from which model examples given in the book can be downloaded For anyone who wants to get ahead in business and especially for those with bottom-line responsibilities, this new edition of Guide to Business Modeling is the essential guide to how to build spreadsheet models for assessing business risks and opportunities.

Algorithms for Decision Making
Algorithms for Decision Making

Author: Mykel J. Kochenderfer

Publisher: MIT Press

Published: 2022-08-16

Total Pages: 701

ISBN-13: 0262047012

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A broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them. Automated decision-making systems or decision-support systems—used in applications that range from aircraft collision avoidance to breast cancer screening—must be designed to account for various sources of uncertainty while carefully balancing multiple objectives. This textbook provides a broad introduction to algorithms for decision making under uncertainty, covering the underlying mathematical problem formulations and the algorithms for solving them. The book first addresses the problem of reasoning about uncertainty and objectives in simple decisions at a single point in time, and then turns to sequential decision problems in stochastic environments where the outcomes of our actions are uncertain. It goes on to address model uncertainty, when we do not start with a known model and must learn how to act through interaction with the environment; state uncertainty, in which we do not know the current state of the environment due to imperfect perceptual information; and decision contexts involving multiple agents. The book focuses primarily on planning and reinforcement learning, although some of the techniques presented draw on elements of supervised learning and optimization. Algorithms are implemented in the Julia programming language. Figures, examples, and exercises convey the intuition behind the various approaches presented.