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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: 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: 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: Richard W. Morris Publisher: Academic Press ISBN: 0128120991 Category : Psychology Languages : en Pages : 486
Book Description
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
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: Thomas Augustin Publisher: John Wiley & Sons ISBN: 1118763149 Category : Mathematics Languages : en Pages : 448
Book Description
In recent years, the theory has become widely accepted and has beenfurther developed, but a detailed introduction is needed in orderto make the material available and accessible to a wide audience.This will be the first book providing such an introduction,covering core theory and recent developments which can be appliedto many application areas. All authors of individual chapters areleading researchers on the specific topics, assuring high qualityand up-to-date contents. An Introduction to Imprecise Probabilities provides acomprehensive introduction to imprecise probabilities, includingtheory and applications reflecting the current state if the art.Each chapter is written by experts on the respective topics,including: Sets of desirable gambles; Coherent lower (conditional)previsions; Special cases and links to literature; Decision making;Graphical models; Classification; Reliability and risk assessment;Statistical inference; Structural judgments; Aspects ofimplementation (including elicitation and computation); Models infinance; Game-theoretic probability; Stochastic processes(including Markov chains); Engineering applications. Essential reading for researchers in academia, researchinstitutes and other organizations, as well as practitionersengaged in areas such as risk analysis and engineering.
Author: Charles S. Taber Publisher: SAGE ISBN: 9780803972704 Category : Computers Languages : en Pages : 108
Book Description
In this introduction to computational modelling the authors provide a concise description of computational methods, including dynamic simulation, knowledge-based models and machine learning, as a single broad class of research tools.
Author: Helmut Prendinger Publisher: Springer Science & Business Media ISBN: 3540854827 Category : Computers Languages : en Pages : 572
Book Description
This book constitutes the refereed proceedings of the 8th International Workshop on Intelligent Virtual Agents, IVA 2008, held in Tokyo, Japan, in September 2008. The 18 revised full papers and 28 revised short papers presented together 42 poster papers were carefully reviewed and selected from 99 submissions. The papers are organized in topical sections on motion and empathy; narrative and augmented reality; conversation and negotiation; nonverbal behavior; models of culture and personality; markup and representation languages; architectures for robotic agents; cognitive architectures; agents for healthcare and training; and agents in games, museums and virtual worlds.
Author: Panel on Modeling Human Behavior and Command Decision Making: Representations for Military Simulations Publisher: National Academies Press ISBN: 0309523893 Category : Business & Economics Languages : en Pages : 433
Book Description
Simulations are widely used in the military for training personnel, analyzing proposed equipment, and rehearsing missions, and these simulations need realistic models of human behavior. This book draws together a wide variety of theoretical and applied research in human behavior modeling that can be considered for use in those simulations. It covers behavior at the individual, unit, and command level. At the individual soldier level, the topics covered include attention, learning, memory, decisionmaking, perception, situation awareness, and planning. At the unit level, the focus is on command and control. The book provides short-, medium-, and long-term goals for research and development of more realistic models of human behavior.
Author: Philippe Besnard Publisher: IOS Press ISBN: 1586038591 Category : Computers Languages : en Pages : 440
Book Description
Focuses on the aim to develop software tools to assist users in constructing and evaluating arguments and counterarguments and/or to develop automated systems for constructing and evaluating arguments and counterarguments. This book includes articles, which provide a snapshot of research questions in the area of computational models of argument.
Author: Paul K. Davis Publisher: John Wiley & Sons ISBN: 1119484987 Category : Technology & Engineering Languages : en Pages : 995
Book Description
This volume describes frontiers in social-behavioral modeling for contexts as diverse as national security, health, and on-line social gaming. Recent scientific and technological advances have created exciting opportunities for such improvements. However, the book also identifies crucial scientific, ethical, and cultural challenges to be met if social-behavioral modeling is to achieve its potential. Doing so will require new methods, data sources, and technology. The volume discusses these, including those needed to achieve and maintain high standards of ethics and privacy. The result should be a new generation of modeling that will advance science and, separately, aid decision-making on major social and security-related subjects despite the myriad uncertainties and complexities of social phenomena. Intended to be relatively comprehensive in scope, the volume balances theory-driven, data-driven, and hybrid approaches. The latter may be rapidly iterative, as when artificial-intelligence methods are coupled with theory-driven insights to build models that are sound, comprehensible and usable in new situations. With the intent of being a milestone document that sketches a research agenda for the next decade, the volume draws on the wisdom, ideas and suggestions of many noted researchers who draw in turn from anthropology, communications, complexity science, computer science, defense planning, economics, engineering, health systems, medicine, neuroscience, physics, political science, psychology, public policy and sociology. In brief, the volume discusses: Cutting-edge challenges and opportunities in modeling for social and behavioral science Special requirements for achieving high standards of privacy and ethics New approaches for developing theory while exploiting both empirical and computational data Issues of reproducibility, communication, explanation, and validation Special requirements for models intended to inform decision making about complex social systems
Author: Koosha Khalvati
Author: Koosha Khalvati
Author: Collin M. Heller
Author: Richard W. Morris
Author: Mykel J. Kochenderfer
Author: Thomas Augustin
Author: Charles S. Taber
Author: Helmut Prendinger
Author: Panel on Modeling Human Behavior and Command Decision Making: Representations for Military Simulations
Author: Philippe Besnard
Author: Paul K. Davis