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This book appeals to students, researchers and professionals working in philosophy and related fields on decision theory applied to artificial intelligence. These chapters stem from the topical conference series, ‘Decision Theory and the Future of AI’ which began in 2017 as a collaboration between the Leverhulme Centre for the Future of Intelligence (CFI) and the Centre for the Study of Existential Risk (CSER) at Cambridge, and the Munich Center for Mathematical Philosophy (MCMP) at LMU Munich. The range of topics, and even more so the range of authors and their home disciplines and affiliations, are a tribute to the richness of the territory, both in intellectual and in community-building terms. Previously published in Synthese Volume 198, supplement issue 27, November 2021 Chapter Approval-directed agency and the decision theory of Newcomb-like problems is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
How society can shape individual actions in times of uncertainty When we make decisions, our thinking is informed by societal norms, “guardrails” that guide our decisions, like the laws and rules that govern us. But what are good guardrails in today’s world of overwhelming information flows and increasingly powerful technologies, such as artificial intelligence? Based on the latest insights from the cognitive sciences, economics, and public policy, Guardrails offers a novel approach to shaping decisions by embracing human agency in its social context. In this visionary book, Urs Gasser and Viktor Mayer-Schönberger show how the quick embrace of technological solutions can lead to results we don’t always want, and they explain how society itself can provide guardrails more suited to the digital age, ones that empower individual choice while accounting for the social good, encourage flexibility in the face of changing circumstances, and ultimately help us to make better decisions as we tackle the most daunting problems of our times, such as global injustice and climate change. Whether we change jobs, buy a house, or quit smoking, thousands of decisions large and small shape our daily lives. Decisions drive our economies, seal the fate of democracies, create war or peace, and affect the well-being of our planet. Guardrails challenges the notion that technology should step in where our own decision making fails, laying out a surprisingly human-centered set of principles that can create new spaces for better decisions and a more equitable and prosperous society.
Intelligent Decision Support Systems have the potential to transform human decision making by combining research in artificial intelligence, information technology, and systems engineering. The field of intelligent decision making is expanding rapidly due, in part, to advances in artificial intelligence and network-centric environments that can deliver the technology. Communication and coordination between dispersed systems can deliver just-in-time information, real-time processing, collaborative environments, and globally up-to-date information to a human decision maker. At the same time, artificial intelligence techniques have demonstrated that they have matured sufficiently to provide computational assistance to humans in practical applications. This book includes contributions from leading researchers in the field beginning with the foundations of human decision making and the complexity of the human cognitive system. Researchers contrast human and artificial intelligence, survey computational intelligence, present pragmatic systems, and discuss future trends. This book will be an invaluable resource to anyone interested in the current state of knowledge and key research gaps in the rapidly developing field of intelligent decision support.
One of the goals of artificial intelligence (AI) is creating autonomous agents that must make decisions based on uncertain and incomplete information. The goal is to design rational agents that must take the best action given the information available and their goals. Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions provides an introduction to different types of decision theory techniques, including MDPs, POMDPs, Influence Diagrams, and Reinforcement Learning, and illustrates their application in artificial intelligence. This book provides insights into the advantages and challenges of using decision theory models for developing intelligent systems.
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.
Presents recent advances in both models and systems for intelligent decision making. Organisations often face complex decisions requiring the assessment of large amounts of data. In recent years Multicriteria Decision Aid (MCDA) and Artificial Intelligence (AI) techniques have been applied with considerable success to support decision making in a wide range of complex real-world problems. The integration of MCDA and AI provides new capabilities relating to the structuring of complex decision problems in static and distributed environments. These include the handling of massive data sets, the modelling of ill-structured information, the construction of advanced decision models, and the development of efficient computational optimization algorithms for problem solving. This book covers a rich set of topics, including intelligent decision support technologies, data mining models for decision making, evidential reasoning, evolutionary multiobjective optimization, fuzzy modelling, as well as applications in management and engineering. Multicriteria Decision Aid and Artificial Intelligence: Covers all of the recent advances in intelligent decision making. Includes a presentation of hybrid models and algorithms for preference modelling and optimisation problems. Provides illustrations of new intelligent technologies and architectures for decision making in static and distributed environments. Explores the general topics on preference modelling and learning, along with the coverage of the main techniques and methodologies and applications. Is written by experts in the field. This book provides an excellent reference tool for the increasing number of researchers and practitioners interested in the integration of MCDA and AI for the development of effective hybrid decision support methodologies and systems. Academics and post-graduate students in the fields of operational research, artificial intelligence and management science or decision analysis will also find this book beneficial.
Many real-world decision problems have multiple objectives. For example, when choosing a medical treatment plan, we want to maximize the efficacy of the treatment, but also minimize the side effects. These objectives typically conflict, e.g., we can often increase the efficacy of the treatment, but at the cost of more severe side effects. In this book, we outline how to deal with multiple objectives in decision-theoretic planning and reinforcement learning algorithms. To illustrate this, we employ the popular problem classes of multi-objective Markov decision processes (MOMDPs) and multi-objective coordination graphs (MO-CoGs). First, we discuss different use cases for multi-objective decision making, and why they often necessitate explicitly multi-objective algorithms. We advocate a utility-based approach to multi-objective decision making, i.e., that what constitutes an optimal solution to a multi-objective decision problem should be derived from the available information about user utility. We show how different assumptions about user utility and what types of policies are allowed lead to different solution concepts, which we outline in a taxonomy of multi-objective decision problems. Second, we show how to create new methods for multi-objective decision making using existing single-objective methods as a basis. Focusing on planning, we describe two ways to creating multi-objective algorithms: in the inner loop approach, the inner workings of a single-objective method are adapted to work with multi-objective solution concepts; in the outer loop approach, a wrapper is created around a single-objective method that solves the multi-objective problem as a series of single-objective problems. After discussing the creation of such methods for the planning setting, we discuss how these approaches apply to the learning setting. Next, we discuss three promising application domains for multi-objective decision making algorithms: energy, health, and infrastructure and transportation. Finally, we conclude by outlining important open problems and promising future directions.
A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.
Personal motivation. The dream of creating artificial devices that reach or outperform human inteUigence is an old one. It is also one of the dreams of my youth, which have never left me. What makes this challenge so interesting? A solution would have enormous implications on our society, and there are reasons to believe that the AI problem can be solved in my expected lifetime. So, it's worth sticking to it for a lifetime, even if it takes 30 years or so to reap the benefits. The AI problem. The science of artificial intelligence (AI) may be defined as the construction of intelligent systems and their analysis. A natural definition of a system is anything that has an input and an output stream. Intelligence is more complicated. It can have many faces like creativity, solving prob lems, pattern recognition, classification, learning, induction, deduction, build ing analogies, optimization, surviving in an environment, language processing, and knowledge. A formal definition incorporating every aspect of intelligence, however, seems difficult. Most, if not all known facets of intelligence can be formulated as goal driven or, more precisely, as maximizing some utility func tion. It is, therefore, sufficient to study goal-driven AI; e. g. the (biological) goal of animals and humans is to survive and spread. The goal of AI systems should be to be useful to humans.
This book constitutes the conference proceedings of the 7th International Conference on Algorithmic Decision Theory, ADT 2021, held in Toulouse, France, in November 2021. The 27 full papers presented were carefully selected from 58 submissions. The papers focus on algorithmic decision theory broadly defined, seeking to bring together researchers and practitioners coming from diverse areas of computer science, economics and operations research in order to improve the theory and practice of modern decision support.