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While many researchers have investigated the performance consequences of automated recommender systems, little research that has explored how these systems impact the de- cision making process. The purpose of this dissertation is to examine how people process information from an automated recommender system and raw information from the en- vironment using Systems Factorial Technology (SFT). Participants completed a speeded length judgment task with a reliable but imperfect aid. Experiment 1 focused on whether people process all the available information or are selective in their information search under certain circumstances (e.g., with performance incentives and with more experience with automation failures in training). Results indicate that participants likely use only one source of information, alternating between the automated aid and the environmental infor- mation. Additionally, performance incentives and less experience with automation failures can lead to slower but not necessarily more accurate performance with an automated aid. Experiment 2 focused on whether display design (e.g, proximity of information and density of information) can encourage serial or parallel processing of information. Unsurprisingly, the results indicate that integrating information on the display allows participants to process information more efficiently. Implications of this research not only sheds light on how peo- ple gather and process information with an automation aid but also how we might design systems to improve decision performance.
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.
1. Provides a multidisciplinary approach of building knowledge on DI; 2. Discusses the limits of the human brain and why computer models are better at making decisions; 3. Covers agent programs for AI-powered decision-making agents; 4. Presents a DI framework - flowchart and figures; 5. Includes detailed and comprehensive information on DI tools and technologies; 6. Gives an ethics-focused approach to building DI systems for the protection of human rights and wellbeing.
Dramatically improve your decisions with data and AI In Decision Intelligence: How to Transform Your Team and Organization with AI-Driven Decision-Making, a team of pioneering decision and AI strategists delivers a digestible and hands-on resource for professionals at every part of the decision-making journey. The book discusses the latest technology and approaches that bridge the gap between behavioral science, data science, and technological innovation. Discover how leaders from various industries and environments are using data and AI to make better future decisions, taking both human as well as business factors into account. This book covers: A demystifying behind-the-scenes peek inside how AI models, forecasts, and optimization for business challenges really work, and why they open up entirely new possibilities. A business-ready introduction to decision intelligence, exploring why traditional decision-making strategies are outdated and how to transition to decision-intelligence. The evolution of Decision Intelligence, coming from analytics and modern techniques like process mining and robotic process automation An examination of decision intelligence at the organizational level, including discussions of agile transformation, transparent organizational culture, and why psychological safety is a crucial enabler for new ways of decision-making in modern companies An overview of why (and where exactly) AI still needs human expertise and how to incorporate this topic in daily planning and decision making Decision Intelligence is essential reading for managers, executives, board members, other business leaders and soon-to-be leaders looking to improve the quality, adaptability, and speed of their decision-making.
Computer simulation-based education and training is a multi-billion dollar industry. With the increased complexity of organizational decision making, projected demand for computer simulation-based decisional aids is on the rise. The objective of this book is to enhance systematically our understanding of and gain insights into the general process by which human facilitated ILEs are effectively designed and used in improving users’ decision making in dynamic tasks. This book is divided into four major parts. Part I serves as an introduction to the subject of “decision making in dynamic tasks”, its importance and its complexity. Part II provides background material, drawing upon the relevant literature, for the development of an integrated process model on the effectiveness of human facilitated ILEs in improving decision making in dynamic tasks. Part III focuses on the design, development and application of Fish Bank ILE, in laboratory experiments, to gather empirical evidence for the validity of the process model. Finally, part IV presents a comprehensive analysis of the gathered data to provide a powerful basis for understating important phenomena of training with human facilitated simulation-based learning environments, thereby, help to drive critical lessons to be learned. This book provides the reader with both a comprehensive understanding of the phenomena encountered in decision making with human facilitated ILEs and a unique way of studying the effects of these phenomena on people’s ability to make better decision in complex, dynamic tasks. This book is intended to be of use to managers and practitioners, researchers and students of dynamic decision making. The background material of Part II provides a solid base to understand and organize the existing experimental research literature and approaches.
In 2000, the Conference on Automation joined forces with a partner group on situation awareness (SA). The rising complexity of systems demands that one can be aware of a large range of environmental and task-based stimulation in order to match what is done with what has to be done. Thus, SA and automation-based interaction fall naturally together and this conference is the second embodiment of this union. Moving into the 21st century, further diversification of the applications of automation will continue--for example, the revolution in genetic technology. Given the broad nature of this form of human-machine interaction, it is vital to apply past lessons to map a future for the symbiotic relationship between humans and the artifacts they create. It is as part of this ongoing endeavor that the present volume is offered.
This book contains a collection of innovative chapters emanating from topics raised during the 5th KES International Conference on Intelligent Decision Technologies (IDT), held during 2013 at Sesimbra, Portugal. The authors were invited to expand their original papers into a plethora of innovative chapters espousing IDT methodologies and applications. This book documents leading-edge contributions, representing advances in Knowledge-Based and Intelligent Information and Engineering System. It acknowledges that researchers recognize that society is familiar with modern Advanced Information Processing and increasingly expect richer IDT systems. Each chapter concentrates on the theory, design, development, implementation, testing or evaluation of IDT techniques or applications. Anyone that wants to work with IDT or simply process knowledge should consider reading one or more chapters and focus on their technique of choice. Most readers will benefit from reading additional chapters to access alternative technique that often represent alternative approaches. This book is suitable for anyone interested in or already working with IDT or Intelligent Decision Support Systems. It is also suitable for students and researchers seeking to learn more about modern Artificial Intelligence and Computational Intelligence techniques that support decision-making in modern computer systems.
This is a book about how management and control decisions are made by persons who collaborate and possibly use the support of an information system. The decision is the result of human conscious activities aiming at choosing a course of action for attaining a certain objective (or a set of objectives). The act of collaboration implies that several entities who work together and share responsibilities to jointly plan, implement and evaluate a program of activities to achieve the common goals. The book is intended to present a balanced view of the domain to include both well-established concepts and a selection of new results in the domains of methods and key technologies. It is meant to answer several questions, such as: a) “How are evolving the business models towards the ever more collaborative schemes?”; b) “What is the role of the decision-maker in the new context?” c) “What are the basic attributes and trends in the domain of decision-supporting information systems?”; d) “Which are the basic methods to aggregate the individual preferences?” e)“What is the impact of modern information and communication technologies on the design and usage of decision support systems for groups of people?”.
Human factors, also known as human engineering or human factors engineering, is the application of behavioral and biological sciences to the design of machines and human-machine systems. Automation refers to the mechanization and integration of the sensing of environmental variables, data processing and decision making and mechanical action. This book deals with all the issues involved in human-automation systems from design to control and performance of both humans and machines.