[PDF] Predicting Human Decision Making eBook

Author : Ariel Geib
Publisher : Springer Nature
Page : 134 pages
File Size : 12,42 MB
Release : 2022-05-31
Category : Computers
ISBN : 3031015789

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Human decision-making often transcends our formal models of "rationality." Designing intelligent agents that interact proficiently with people necessitates the modeling of human behavior and the prediction of their decisions. In this book, we explore the task of automatically predicting human decision-making and its use in designing intelligent human-aware automated computer systems of varying natures—from purely conflicting interaction settings (e.g., security and games) to fully cooperative interaction settings (e.g., autonomous driving and personal robotic assistants). We explore the techniques, algorithms, and empirical methodologies for meeting the challenges that arise from the above tasks and illustrate major benefits from the use of these computational solutions in real-world application domains such as security, negotiations, argumentative interactions, voting systems, autonomous driving, and games. The book presents both the traditional and classical methods as well as the most recent and cutting edge advances, providing the reader with a panorama of the challenges and solutions in predicting human decision-making.

Author : Ariel Rosenfeld
Publisher : Morgan & Claypool Publishers
Page : 152 pages
File Size : 47,57 MB
Release : 2018-01-22
Category : Computers
ISBN : 1681732750

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Human decision-making often transcends our formal models of "rationality." Designing intelligent agents that interact proficiently with people necessitates the modeling of human behavior and the prediction of their decisions. In this book, we explore the task of automatically predicting human decision-making and its use in designing intelligent human-aware automated computer systems of varying natures—from purely conflicting interaction settings (e.g., security and games) to fully cooperative interaction settings (e.g., autonomous driving and personal robotic assistants). We explore the techniques, algorithms, and empirical methodologies for meeting the challenges that arise from the above tasks and illustrate major benefits from the use of these computational solutions in real-world application domains such as security, negotiations, argumentative interactions, voting systems, autonomous driving, and games. The book presents both the traditional and classical methods as well as the most recent and cutting edge advances, providing the reader with a panorama of the challenges and solutions in predicting human decision-making.

Author : Panel on Modeling Human Behavior and Command Decision Making: Representations for Military Simulations
Publisher : National Academies Press
Page : 433 pages
File Size : 34,58 MB
Release : 1998-08-14
Category : Business & Economics
ISBN : 0309523893

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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 : Daniel R. Sarewitz
Publisher :
Page : 434 pages
File Size : 10,72 MB
Release : 2000-04
Category : Education
ISBN :

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Based upon ten case studies, Prediction explores how science-based predictions guide policy making and what this means in terms of global warming, biogenetically modifying organisms and polluting the environment with chemicals.

Author : K.C. Santosh
Publisher : Springer Nature
Page : 137 pages
File Size : 46,90 MB
Release : 2020-12-11
Category : Technology & Engineering
ISBN : 9811596824

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The book aims to outline the issues of AI and COVID-19, involving predictions,medical support decision-making, and possible impact on human life. Starting withmajor COVID-19 issues and challenges, it takes possible AI-based solutions forseveral problems, such as public health surveillance, early (epidemic) prediction,COVID-19 positive case detection, and robotics integration against COVID-19.Beside mathematical modeling, it includes the necessity of changes in innovationsand possible COVID-19 impacts. The book covers a clear understanding of AI-driven tools and techniques, where pattern recognition, anomaly detection, machinelearning, and data analytics are considered. It aims to include the wide range ofaudiences from computer science and engineering to healthcare professionals.

Author : John D. Lee
Publisher : Oxford University Press
Page : 659 pages
File Size : 24,52 MB
Release : 2013-03-07
Category : Computers
ISBN : 0199757186

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This handbook is the first to provide comprehensive coverage of original state-of-the-science research, analysis, and design of integrated, human-technology systems.

Author : Mounica Devaguptapu
Publisher :
Page : 49 pages
File Size : 36,23 MB
Release : 2020
Category :
ISBN :

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"Human sequential decision-making involves two essential questions: (i) ''what to choose next?'', and (ii) ''when to stop?''. Assuming that the human agents choose an alternative according to their preference order, our goal is to model and learn how human agents choose their stopping time while making sequential decisions. In contrary to traditional assumptions in the literature regarding how humans exhibit satisficing behavior on instantaneous utilities, we assume that humans employ a discounted satisficing heuristic to compute their stopping time, i.e., the human agent stops working if the total accumulated utility goes beyond a dynamic threshold that gets discounted with time. In this thesis, we model the stopping time in 3 scenarios where the payoff of the human worker is assumed as (i) single-attribute utility, (ii) multi-attribute utility with known weights, and (iii) multi-attribute utility with unknown weights. We propose algorithms to estimate the model parameters followed by predicting the stopping time in all three scenarios and present the simulation results to demonstrate the error performance. Simulation results are presented to demonstrate the convergence of prediction error of stopping time, in spite of the fact that model parameters converge to biased estimates. This observation is later justified using an illustrative example to show that there are multiple discounted satisficing models that explain the same stopping time decision. A novel web application is also developed to emulate a crowd-sourcing platform in our lab to capture multi-attribute information regarding the task in order to perform validations of the proposed algorithms on real data"--Abstract, page iii.