Judgment Aggregation

Judgment Aggregation
Author: Philippe Mongin
Publisher:
Total Pages: 0
Release: 2019
Genre:
ISBN:

Judgment aggregation theory generalizes social choice theory by having the aggregation rule bear on judgments of all kinds instead of barely judgments of preference. The paper briefly sums it up, privileging the variant that formalizes judgment by a logical syntax. The theory derives from Kornhauser and Sager's doctrinal paradox and Pettit's discursive dilemma, which List and Pettit turned into an impossibility theorem - the first of a long list to come. After mentioning this stage, the paper restates three theorems that are representative of the current work, by Nehring and Puppe, Dokow and Holzman, and Dietrich and Mongin, respectively, and it concludes by explaining how Dietrich and List have recovered Arrow's theorem as a particular application of the theory.

Judgment Aggregation

Judgment Aggregation
Author: Davide Kantarcioglu
Publisher: Springer Nature
Total Pages: 133
Release: 2022-06-01
Genre: Computers
ISBN: 3031015681

Judgment aggregation is a mathematical theory of collective decision-making. It concerns the methods whereby individual opinions about logically interconnected issues of interest can, or cannot, be aggregated into one collective stance. Aggregation problems have traditionally been of interest for disciplines like economics and the political sciences, as well as philosophy, where judgment aggregation itself originates from, but have recently captured the attention of disciplines like computer science, artificial intelligence and multi-agent systems. Judgment aggregation has emerged in the last decade as a unifying paradigm for the formalization and understanding of aggregation problems. Still, no comprehensive presentation of the theory is available to date. This Synthesis Lecture aims at filling this gap presenting the key motivations, results, abstractions and techniques underpinning it. Table of Contents: Preface / Acknowledgments / Logic Meets Social Choice Theory / Basic Concepts / Impossibility / Coping with Impossibility / Manipulability / Aggregation Rules / Deliberation / Bibliography / Authors' Biographies / Index

Economics and Computation

Economics and Computation
Author: Jörg Rothe
Publisher: Springer Nature
Total Pages: 779
Release: 2024
Genre: Econometrics
ISBN: 3031600991

This textbook connects three vibrant areas at the interface between economics and computer science: algorithmic game theory, computational social choice, and fair division. It thus offers an interdisciplinary treatment of collective decision making from an economic and computational perspective. Part I introduces to algorithmic game theory, focusing on both noncooperative and cooperative game theory. Part II introduces to computational social choice, focusing on both preference aggregation (voting) and judgment aggregation. Part III introduces to fair division, focusing on the division of both a single divisible resource ("cake-cutting") and multiple indivisible and unshareable resources ("multiagent resource allocation"). In all these parts, much weight is given to the algorithmic and complexity-theoretic aspects of problems arising in these areas, and the interconnections between the three parts are of central interest.

Rankings and Decisions in Engineering

Rankings and Decisions in Engineering
Author: Fiorenzo Franceschini
Publisher: Springer Nature
Total Pages: 259
Release: 2022-02-28
Genre: Business & Economics
ISBN: 3030898652

This book focuses on decision-making problems in engineering. It investigates the ranking aggregation problem and the related features, such as input/output data, simplification hypotheses, importance hierarchy of experts. In addition to a well-structured overview of several interesting, consolidated methodological approaches, it presents innovative approaches that can also be applied profitably in other fields. The fascinating selection of topics included is based on research that has been developed in the past twenty years. The descriptions are supported by figures, tables, flowcharts, diagrams, examples and practical case studies. The book is an ideal resource for engineering academics, practitioners, technicians and students, who do not necessarily have an in-depth knowledge of decision-making. It is also a thought-provoking read for engineers and academics looking for innovative ways to improve engineering processes in a variety of fields, such as conceptual design, quality improvement, reliability engineering. “Today, rankings are exercised in all spheres of life, products are ranked on Amazon and similar platforms; services such as restaurants and hotels on platforms such as TripAdvisor; and other services such as lectures or even medical treatment on different specialized platforms. We often make our daily decisions based on these rankings. The quality of our decisions depends on our ability to select appropriate methods to fit the context and needs. We need to be familiar with the theory and practice of these methods to make them useful. To this purpose, this book is an important addition to the bookshelves of academics and professionals, not only from engineering. The connection between theory and practice is weaved throughout the book, making it useful for practitioners also.” Prof. Yoram Reich, Full Professor and Head of Systems Engineering research Initiative at Tel Aviv University (Israel), Editor-in-Chief of “Research in Engineering Design”

Noise

Noise
Author: Daniel Kahneman
Publisher: Little, Brown
Total Pages: 429
Release: 2021-05-18
Genre: Business & Economics
ISBN: 031645138X

From the Nobel Prize-winning author of Thinking, Fast and Slow and the coauthor of Nudge, a revolutionary exploration of why people make bad judgments and how to make better ones—"a tour de force” (New York Times). Imagine that two doctors in the same city give different diagnoses to identical patients—or that two judges in the same courthouse give markedly different sentences to people who have committed the same crime. Suppose that different interviewers at the same firm make different decisions about indistinguishable job applicants—or that when a company is handling customer complaints, the resolution depends on who happens to answer the phone. Now imagine that the same doctor, the same judge, the same interviewer, or the same customer service agent makes different decisions depending on whether it is morning or afternoon, or Monday rather than Wednesday. These are examples of noise: variability in judgments that should be identical. In Noise, Daniel Kahneman, Olivier Sibony, and Cass R. Sunstein show the detrimental effects of noise in many fields, including medicine, law, economic forecasting, forensic science, bail, child protection, strategy, performance reviews, and personnel selection. Wherever there is judgment, there is noise. Yet, most of the time, individuals and organizations alike are unaware of it. They neglect noise. With a few simple remedies, people can reduce both noise and bias, and so make far better decisions. Packed with original ideas, and offering the same kinds of research-based insights that made Thinking, Fast and Slow and Nudge groundbreaking New York Times bestsellers, Noise explains how and why humans are so susceptible to noise in judgment—and what we can do about it.

Model Rules of Professional Conduct

Model Rules of Professional Conduct
Author: American Bar Association. House of Delegates
Publisher: American Bar Association
Total Pages: 216
Release: 2007
Genre: Law
ISBN: 9781590318737

The Model Rules of Professional Conduct provides an up-to-date resource for information on legal ethics. Federal, state and local courts in all jurisdictions look to the Rules for guidance in solving lawyer malpractice cases, disciplinary actions, disqualification issues, sanctions questions and much more. In this volume, black-letter Rules of Professional Conduct are followed by numbered Comments that explain each Rule's purpose and provide suggestions for its practical application. The Rules will help you identify proper conduct in a variety of given situations, review those instances where discretionary action is possible, and define the nature of the relationship between you and your clients, colleagues and the courts.

Group Agency

Group Agency
Author: Christian List
Publisher: Oxford University Press
Total Pages: 249
Release: 2011-04-07
Genre: Law
ISBN: 0199591563

Are companies, churches, and states genuine agents? How do we explain their behaviour? Can we treat them as accountable for their actions? List and Pettit offer original arguments, grounded in cutting-edge work on social choice, economics, and philosophy, to show there really are group agents, over and above the individual agents who compose them.

Eliciting and Analyzing Expert Judgment

Eliciting and Analyzing Expert Judgment
Author: Mary A. Meyer
Publisher: SIAM
Total Pages: 471
Release: 2001-01-01
Genre: Mathematics
ISBN: 0898714745

Expert judgment is invaluable for assessing products, systems, and situations for which measurements or test results are sparse or nonexistent. Eliciting and Analyzing Expert Judgment: A Practical Guide takes the reader step by step through the techniques of eliciting and analyzing expert judgment, with special attention given to helping the reader develop elicitation methods and tools adaptable to a variety of unique situations and work areas. The analysis procedures presented in the book may require a basic understanding of statistics and probabilities, but the authors have provided detailed explanations of the techniques used and have taken special care to define all statistical jargon. Originally published in 1991, this book is designed so that those familiar with the use of expert judgment can quickly find the material appropriate for their advanced background.

Reinforcement Learning, second edition

Reinforcement Learning, second edition
Author: Richard S. Sutton
Publisher: MIT Press
Total Pages: 549
Release: 2018-11-13
Genre: Computers
ISBN: 0262352702

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.