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Author: Stuart Berry Publisher: Springer ISBN: 3319554174 Category : Computers Languages : en Pages : 390
Book Description
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.
Author: Stuart Berry Publisher: Springer ISBN: 3319554174 Category : Computers Languages : en Pages : 390
Book Description
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.
Author: Koosha Khalvati Publisher: ISBN: Category : Languages : en Pages : 131
Book Description
Existing computational models of decision making are often limited to particular experimental setups. The limitation is mainly due to the inability to capture the decision maker's uncertainty about the situation. We propose a computational framework for studying decision making under uncertainty in neuroscience and psychology. Our framework is heavily focused on the probabilistic assessment of the decision maker, i.e., their "belief", about the state of the world. Specifically, it is based on Partially Observable Markov Decision Processes (POMDPs), which combines Bayesian reasoning and reward maximization to choose actions. We demonstrate the viability of our belief-based decision making framework using data from various experiments in perceptual and social decision making. Our framework explains the relationship between decision makers' actual performance and their belief about it, called decision confidence, in perceptual decision making experiments. It also shows why this assessment could deviate from reality in many situations. Such deviations have been often interpreted as evidence for sub-optimal decision making or distinct processes that underlie choice and confidence. Our framework challenges these interpretations by showing that a normative Bayesian decision maker optimizing the gained reward elicits the same discrepancies. Moreover, our method outperforms existing models in quantitatively predicting human behavior in a social decision making task and provides insight into the underlying process. Our results suggest that in decision making tasks involving large groups, humans employ Bayesian inference to model the "group's mind" and make predictions of others' decisions. Finally, we extend our method to multiple reasoning levels about others (levels of theory of mind) and make the connection to conformity as a strategy for decision making in groups. This extended framework can explain human actions in various collective group decision making tasks, providing a new theory for cooperation and coordination in large groups.
Author: John Tennent Publisher: The Economist ISBN: 1610395115 Category : Business & Economics Languages : en Pages : 345
Book Description
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.
Author: National Research Council Publisher: National Academies Press ISBN: 0309110009 Category : Political Science Languages : en Pages : 286
Book Description
Many regulations issued by the U.S. Environmental Protection Agency (EPA) are based on the results of computer models. Models help EPA explain environmental phenomena in settings where direct observations are limited or unavailable, and anticipate the effects of agency policies on the environment, human health and the economy. Given the critical role played by models, the EPA asked the National Research Council to assess scientific issues related to the agency's selection and use of models in its decisions. The book recommends a series of guidelines and principles for improving agency models and decision-making processes. The centerpiece of the book's recommended vision is a life-cycle approach to model evaluation which includes peer review, corroboration of results, and other activities. This will enhance the agency's ability to respond to requirements from a 2001 law on information quality and improve policy development and implementation.
Author: Collin M. Heller Publisher: ISBN: Category : Decision making Languages : en Pages :
Book Description
The research objective of this thesis is to formulate and demonstrate a computational framework for modeling the design decisions of engineers. This framework is intended to be descriptive in nature as opposed to prescriptive or normative; the output of the model represents a plausible result of a designer's decision making process. The framework decomposes the decision into three elements: the problem statement, the designer's beliefs about the alternatives, and the designer's preferences. Multi-attribute utility theory is used to capture designer preferences for multiple objectives under uncertainty. Machine-learning techniques are used to store the designer's knowledge and to make Bayesian inferences regarding the attributes of alternatives. These models are integrated into the framework of a Markov decision process to simulate multiple sequential decisions. The overall framework enables the designer's decision problem to be transformed into an optimization problem statement; the simulated designer selects the alternative with the maximum expected utility. Although utility theory is typically viewed as a normative decision framework, the perspective in this research is that the approach can be used in a descriptive context for modeling rational and non-time critical decisions by engineering designers. This approach is intended to enable the formalisms of utility theory to be used to design human subjects experiments involving engineers in design organizations based on pairwise lotteries and other methods for preference elicitation. The results of these experiments would substantiate the selection of parameters in the model to enable it to be used to diagnose potential problems in engineering design projects. The purpose of the decision-making framework is to enable the development of a design process simulation of an organization involved in the development of a large-scale complex engineered system such as an aircraft or spacecraft. The decision model will allow researchers to determine the broader effects of individual engineering decisions on the aggregate dynamics of the design process and the resulting performance of the designed artifact itself. To illustrate the model's applicability in this context, the framework is demonstrated on three example problems: a one-dimensional decision problem, a multidimensional turbojet design problem, and a variable fidelity analysis problem. Individual utility functions are developed for designers in a requirements-driven design problem and then combined into a multi-attribute utility function. Gaussian process models are used to represent the designer's beliefs about the alternatives, and a custom covariance function is formulated to more accurately represent a designer's uncertainty in beliefs about the design attributes.
Author: Mikael Collan Publisher: Springer ISBN: 3319602071 Category : Technology & Engineering Languages : en Pages : 491
Book Description
This book offers a concise introduction and comprehensive overview of the state of the art in the field of decision-making and consensus modeling, with a special emphasis on fuzzy methods. It consists of a collection of authoritative contributions reporting on the decision-making process from different perspectives: from psychology to social and political sciences, from decision sciences to data mining, and from computational sciences in general, to artificial and computational intelligence and systems. Written as a homage to Mario Fedrizzi for his scholarly achievements, creative ideas and long lasting services to different scientific communities, it introduces key theoretical concepts, describes new models and methods, and discusses a range of promising real-world applications in the field of decision-making science. It is a timely reference guide and a source of inspiration for advanced students and researchers
Author: Caesar Wu Publisher: Morgan Kaufmann ISBN: 0128016884 Category : Computers Languages : en Pages : 848
Book Description
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
Author: Institute of Medicine Publisher: National Academies Press ISBN: 0309317258 Category : Medical Languages : en Pages : 269
Book Description
Tobacco consumption continues to be the leading cause of preventable disease and death in the United States. The Food and Drug Administration (FDA) regulates the manufacture, distribution, and marketing of tobacco products - specifically cigarettes, cigarette tobacco, roll-your-own tobacco, and smokeless tobacco - to protect public health and reduce tobacco use in the United States. Given the strong social component inherent to tobacco use onset, cessation, and relapse, and given the heterogeneity of those social interactions, agent-based models have the potential to be an essential tool in assessing the effects of policies to control tobacco. Assessing the Use of Agent-Based Models for Tobacco Regulation describes the complex tobacco environment; discusses the usefulness of agent-based models to inform tobacco policy and regulation; presents an evaluation framework for policy-relevant agent-based models; examines the role and type of data needed to develop agent-based models for tobacco regulation; provides an assessment of the agent-based model developed for FDA; and offers strategies for using agent-based models to inform decision making in the future.
Author: Mykel J. Kochenderfer Publisher: MIT Press ISBN: 0262331713 Category : Computers Languages : en Pages : 350
Book Description
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.
Author: Stuart Berry
Author: Stuart Berry
Author: Koosha Khalvati
Author: John Tennent
Author: National Research Council
Author: Collin M. Heller
Author: Mikael Collan
Author: Caesar Wu
Author: Institute of Medicine
Author: Mykel J. Kochenderfer
Author: David L. Yazujian