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.

A broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them. Automated decision-making systems or decision-support systems—used in applications that range from aircraft collision avoidance to breast cancer screening—must be designed to account for various sources of uncertainty while carefully balancing multiple objectives. This textbook provides a broad introduction to algorithms for decision making under uncertainty, covering the underlying mathematical problem formulations and the algorithms for solving them. The book first addresses the problem of reasoning about uncertainty and objectives in simple decisions at a single point in time, and then turns to sequential decision problems in stochastic environments where the outcomes of our actions are uncertain. It goes on to address model uncertainty, when we do not start with a known model and must learn how to act through interaction with the environment; state uncertainty, in which we do not know the current state of the environment due to imperfect perceptual information; and decision contexts involving multiple agents. The book focuses primarily on planning and reinforcement learning, although some of the techniques presented draw on elements of supervised learning and optimization. Algorithms are implemented in the Julia programming language. Figures, examples, and exercises convey the intuition behind the various approaches presented.

Computer science and economics have engaged in a lively interaction over the past fifteen years, resulting in the new field of algorithmic game theory. Many problems that are central to modern computer science, ranging from resource allocation in large networks to online advertising, involve interactions between multiple self-interested parties. Economics and game theory offer a host of useful models and definitions to reason about such problems. The flow of ideas also travels in the other direction, and concepts from computer science are increasingly important in economics. This book grew out of the author's Stanford University course on algorithmic game theory, and aims to give students and other newcomers a quick and accessible introduction to many of the most important concepts in the field. The book also includes case studies on online advertising, wireless spectrum auctions, kidney exchange, and network management.

This book constitutes the refereed proceedings of the Second International Conference on Algorithmic Decision Theory, ADT 2011, held in Piscataway, NJ, USA, in October 2011. The 24 revised full papers presented were carefully reviewed and selected from 50 submissions.

This book constitutes the conference proceedings of the 6th International Conference on Algorithmic Decision Theory, ADT 2019, held in Durham, NC, USA, in October 2019. The 10 full papers presented together with 7 short papers were carefully selected from 31 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.

This book constitutes the thoroughly refereed conference proceedings of the Third International Conference on Algorithmic Decision Theory, ADT 2013, held in November 2013 in Bruxelles, Belgium. The 33 revised full papers presented were carefully selected from more than 70 submissions, covering preferences in reasoning and decision making, uncertainty and robustness in decision making, multi-criteria decision analysis and optimization, collective decision making, learning and knowledge extraction for decision support.

This volume contains the papers presented at ADT 2009, the first International Conference on Algorithmic Decision Theory. The conference was held in San Servolo, a small island of the Venice lagoon, during October 20-23, 2009. The program of the conference included oral presentations, posters, invited talks, and tutorials. The conference received 65 submissions of which 39 papers were accepted (9 papers were posters). The topics of these papers range from computational social choice preference modeling, from uncertainty to preference learning, from multi-criteria decision making to game theory.

This book constitutes the thoroughly refereed conference proceedings of the 4th International Conference on Algorithmic Decision Theory , ADT 2015, held in September 2015 in Lexington, USA. The 32 full papers presented were carefully selected from 76 submissions. The papers are organized in topical sections such as preferences; manipulation, learning and other issues; utility and decision theory; argumentation; bribery and control; social choice; allocation and other problems; doctoral consortium.

This book constitutes the conference proceedings of the 5th International Conference on Algorithmic Decision Theory , ADT 2017, held in Luxembourg, in October 2017.The 22 full papers presented together with 6 short papers, 4 keynote abstracts, and 6 Doctoral Consortium papers, were carefully selected from 45 submissions. The papers are organized in topical sections on preferences and multi-criteria decision aiding; decision making and voting; game theory and decision theory; and allocation and matching.