Handbook of Computational Social Choice PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Handbook of Computational Social Choice PDF full book. Access full book title Handbook of Computational Social Choice by Felix Brandt. Download full books in PDF and EPUB format.
Author: Felix Brandt
Publisher: Cambridge University Press
ISBN: 1316489752
Category : Computers
Languages : en
Pages : 553
Get Book Here
Book Description
The rapidly growing field of computational social choice, at the intersection of computer science and economics, deals with the computational aspects of collective decision making. This handbook, written by thirty-six prominent members of the computational social choice community, covers the field comprehensively. Chapters devoted to each of the field's major themes offer detailed introductions. Topics include voting theory (such as the computational complexity of winner determination and manipulation in elections), fair allocation (such as algorithms for dividing divisible and indivisible goods), coalition formation (such as matching and hedonic games), and many more. Graduate students, researchers, and professionals in computer science, economics, mathematics, political science, and philosophy will benefit from this accessible and self-contained book.
Author: Felix Brandt
Publisher: Cambridge University Press
ISBN: 1316489752
Category : Computers
Languages : en
Pages : 553
Get Book Here
Book Description
The rapidly growing field of computational social choice, at the intersection of computer science and economics, deals with the computational aspects of collective decision making. This handbook, written by thirty-six prominent members of the computational social choice community, covers the field comprehensively. Chapters devoted to each of the field's major themes offer detailed introductions. Topics include voting theory (such as the computational complexity of winner determination and manipulation in elections), fair allocation (such as algorithms for dividing divisible and indivisible goods), coalition formation (such as matching and hedonic games), and many more. Graduate students, researchers, and professionals in computer science, economics, mathematics, political science, and philosophy will benefit from this accessible and self-contained book.
Author: Francesca Rossi
Publisher: Springer
ISBN: 364204428X
Category : Mathematics
Languages : en
Pages : 471
Get Book Here
Book Description
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.
Author: D. Marc Kilgour
Publisher: Springer Science & Business Media
ISBN: 9048190975
Category : Mathematics
Languages : en
Pages : 473
Get Book Here
Book Description
Publication of the Handbook of Group Decision and Negotiation marks a milestone in the evolution of the group decision and negotiation (GDN) eld. On this occasion, editors Colin Eden and Marc Kilgour asked me to write a brief history of the eld to provide background and context for the volume. They said that I am in a good position to do so: Actively involved in creating the GDN Section and serving as its chair; founding and leading the GDN journal, Group Decision and Negotiation as editor-in-chief, and the book series, “Advances in Group Decision and Negotiation” as editor; and serving as general chair of the GDN annual meetings. I accepted their invitation to write a brief history. In 1989 what is now the Institute for Operations Research and the Management Sciences (INFORMS) established its Section on Group Decision and Negotiation. The journal Group Decision and Negotiation was founded in 1992, published by Springer in cooperation with INFORMS and the GDN Section. In 2003, as an ext- sion of the journal, the Springer book series, “Advances in Group Decision and Negotiation” was inaugurated.
Author: Pierre Marquis
Publisher: Springer Nature
ISBN: 3030061647
Category : Technology & Engineering
Languages : en
Pages : 808
Get Book Here
Book Description
The purpose of this book is to provide an overview of AI research, ranging from basic work to interfaces and applications, with as much emphasis on results as on current issues. It is aimed at an audience of master students and Ph.D. students, and can be of interest as well for researchers and engineers who want to know more about AI. The book is split into three volumes: - the first volume brings together twenty-three chapters dealing with the foundations of knowledge representation and the formalization of reasoning and learning (Volume 1. Knowledge representation, reasoning and learning) - the second volume offers a view of AI, in fourteen chapters, from the side of the algorithms (Volume 2. AI Algorithms) - the third volume, composed of sixteen chapters, describes the main interfaces and applications of AI (Volume 3. Interfaces and applications of AI). Implementing reasoning or decision making processes requires an appropriate representation of the pieces of information to be exploited. This first volume starts with a historical chapter sketching the slow emergence of building blocks of AI along centuries. Then the volume provides an organized overview of different logical, numerical, or graphical representation formalisms able to handle incomplete information, rules having exceptions, probabilistic and possibilistic uncertainty (and beyond), as well as taxonomies, time, space, preferences, norms, causality, and even trust and emotions among agents. Different types of reasoning, beyond classical deduction, are surveyed including nonmonotonic reasoning, belief revision, updating, information fusion, reasoning based on similarity (case-based, interpolative, or analogical), as well as reasoning about actions, reasoning about ontologies (description logics), argumentation, and negotiation or persuasion between agents. Three chapters deal with decision making, be it multiple criteria, collective, or under uncertainty. Two chapters cover statistical computational learning and reinforcement learning (other machine learning topics are covered in Volume 2). Chapters on diagnosis and supervision, validation and explanation, and knowledge base acquisition complete the volume.
Author: David P. Williamson
Publisher: Cambridge University Press
ISBN: 9780521195270
Category : Computers
Languages : en
Pages : 518
Get Book Here
Book Description
Discrete optimization problems are everywhere, from traditional operations research planning problems, such as scheduling, facility location, and network design; to computer science problems in databases; to advertising issues in viral marketing. Yet most such problems are NP-hard. Thus unless P = NP, there are no efficient algorithms to find optimal solutions to such problems. This book shows how to design approximation algorithms: efficient algorithms that find provably near-optimal solutions. The book is organized around central algorithmic techniques for designing approximation algorithms, including greedy and local search algorithms, dynamic programming, linear and semidefinite programming, and randomization. Each chapter in the first part of the book is devoted to a single algorithmic technique, which is then applied to several different problems. The second part revisits the techniques but offers more sophisticated treatments of them. The book also covers methods for proving that optimization problems are hard to approximate. Designed as a textbook for graduate-level algorithms courses, the book will also serve as a reference for researchers interested in the heuristic solution of discrete optimization problems.
Author: Marc Mézard
Publisher: Oxford University Press
ISBN: 019857083X
Category : Computers
Languages : en
Pages : 584
Get Book Here
Book Description
A very active field of research is emerging at the frontier of statistical physics, theoretical computer science/discrete mathematics, and coding/information theory. This book sets up a common language and pool of concepts, accessible to students and researchers from each of these fields.
Author: Geoffrey Grimmett
Publisher: Cambridge University Press
ISBN: 1108542999
Category : Mathematics
Languages : en
Pages : 279
Get Book Here
Book Description
This introduction to some of the principal models in the theory of disordered systems leads the reader through the basics, to the very edge of contemporary research, with the minimum of technical fuss. Topics covered include random walk, percolation, self-avoiding walk, interacting particle systems, uniform spanning tree, random graphs, as well as the Ising, Potts, and random-cluster models for ferromagnetism, and the Lorentz model for motion in a random medium. This new edition features accounts of major recent progress, including the exact value of the connective constant of the hexagonal lattice, and the critical point of the random-cluster model on the square lattice. The choice of topics is strongly motivated by modern applications, and focuses on areas that merit further research. Accessible to a wide audience of mathematicians and physicists, this book can be used as a graduate course text. Each chapter ends with a range of exercises.
Author: Rupert Freeman
Publisher: Springer Nature
ISBN: 3031739035
Category :
Languages : en
Pages : 306
Get Book Here
Book Description
Author: Russell Lyons
Publisher: Cambridge University Press
ISBN: 1316785335
Category : Mathematics
Languages : en
Pages : 1023
Get Book Here
Book Description
Starting around the late 1950s, several research communities began relating the geometry of graphs to stochastic processes on these graphs. This book, twenty years in the making, ties together research in the field, encompassing work on percolation, isoperimetric inequalities, eigenvalues, transition probabilities, and random walks. Written by two leading researchers, the text emphasizes intuition, while giving complete proofs and more than 850 exercises. Many recent developments, in which the authors have played a leading role, are discussed, including percolation on trees and Cayley graphs, uniform spanning forests, the mass-transport technique, and connections on random walks on graphs to embedding in Hilbert space. This state-of-the-art account of probability on networks will be indispensable for graduate students and researchers alike.
Author: Avrim Blum
Publisher: Cambridge University Press
ISBN: 1108617360
Category : Computers
Languages : en
Pages : 433
Get Book Here
Book Description
This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.
