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Textbook comprising lectures on macro-economic planning both in planned economies and in other economic systems - covers methodologys and theories, the use of econometrics and mathematical models in economic planning, and includes centralization and decentralization of decision making powers, economic policy formulation during times of uncertainty, etc. Diagrams and references.
Textbook comprising lectures on macro-economic planning both in planned economies and in other economic systems - covers methodologys and theories, the use of econometrics and mathematical models in economic planning, and includes centralization and decentralization of decision making powers, economic policy formulation during times of uncertainty, etc. Diagrams and references.
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.
Planning in a general sense is concerned with the design of communication and decision making mechanisms in organizations where information and choice are decentralized. Non-cooperative planning theory as it is developed in this book treats the incentive aspects hereof. It stresses how strategic behavior and opportunism may impede planning, and how this can be coped with via the organization of communication and decision making, the design of information and control systems, and the development of incentive schemes. In particular, the book contains a thorough investigation of incentive provision in information production.
This is one of the first books on the use of software agents to simulate bidding behavior in electronic auctions. It introduces market theory and computational economics together, and gives an overview on the most common and up-to-date agent-based simulation methods. The book will help the reader learn more about simulations in economics in general and common agent-based methods and tools in particular.
This book introduces multiagent planning under uncertainty as formalized by decentralized partially observable Markov decision processes (Dec-POMDPs). The intended audience is researchers and graduate students working in the fields of artificial intelligence related to sequential decision making: reinforcement learning, decision-theoretic planning for single agents, classical multiagent planning, decentralized control, and operations research.