Author : Harold William Kuhn
Publisher : Princeton University Press
Page : 408 pages
File Size : 25,58 MB
Release : 2016-03-02
Category : Mathematics
ISBN : 1400881978
These two new collections, numbers 28 and 29 respectively in the Annals of Mathematics Studies, continue the high standard set by the earlier Annals Studies 20 and 24 by bringing together important contributions to the theories of games and of nonlinear differential equations.
Author : Harold William Kuhn
Publisher : Princeton University Press
Page : 410 pages
File Size : 21,18 MB
Release : 1953-03-21
Category : Mathematics
ISBN : 9780691079356
These two new collections, numbers 28 and 29 respectively in the Annals of Mathematics Studies, continue the high standard set by the earlier Annals Studies 20 and 24 by bringing together important contributions to the theories of games and of nonlinear differential equations.
Author : Elena Parilina
Publisher : Springer Nature
Page : 263 pages
File Size : 18,92 MB
Release : 2022-11-23
Category : Mathematics
ISBN : 3031164555
This textbook provides a comprehensive overview of noncooperative and cooperative dynamic games involving uncertain parameter values, with the stochastic process being described by an event tree. Primarily intended for graduate students of economics, management science and engineering, the book is self-contained, as it defines and illustrates all relevant concepts originally introduced in static games before extending them to a dynamic framework. It subsequently addresses the sustainability of cooperative contracts over time and introduces a range of mechanisms to help avoid such agreements breaking down before reaching maturity. To illustrate the concepts discussed, the book provides various examples of how dynamic games played over event trees can be applied to environmental economics, management science, and engineering.
Author : Xiaotie Deng
Publisher : Springer
Page : 292 pages
File Size : 24,13 MB
Release : 2018-08-26
Category : Computers
ISBN : 3319996606
This book constitutes the refereed proceedings of the 11th International Symposium on Algorithmic Game Theory, SAGT 2018, held in Beijing, China, in September 2018. The 19 full papers presented together with 6 short papers and 5 plenary talks were carefully reviewed and selected from 54 submissions. The papers cover various important aspects of algorithmic game theory including market equilibrium, auctions and applications, two sided markets, cake-cutting, cooperative games, voting games, multi-agent scheduling, price of stability, various mechanism design problems: online-dynamics and multi-stages as well as revenue maximization and resource allocation and applications.
Author : Luca Longo
Publisher : Springer Nature
Page : 529 pages
File Size : 32,51 MB
Release :
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ISBN : 3031637976
Author : Giancarlo Fortino
Publisher : Springer Nature
Page : 786 pages
File Size : 44,55 MB
Release :
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ISBN : 9819708923
Author : Anna R. Karlin
Publisher : American Mathematical Soc.
Page : 400 pages
File Size : 16,67 MB
Release : 2017-04-27
Category : Mathematics
ISBN : 1470419823
We live in a highly connected world with multiple self-interested agents interacting and myriad opportunities for conflict and cooperation. The goal of game theory is to understand these opportunities. This book presents a rigorous introduction to the mathematics of game theory without losing sight of the joy of the subject. This is done by focusing on theoretical highlights (e.g., at least six Nobel Prize winning results are developed from scratch) and by presenting exciting connections of game theory to other fields such as computer science (algorithmic game theory), economics (auctions and matching markets), social choice (voting theory), biology (signaling and evolutionary stability), and learning theory. Both classical topics, such as zero-sum games, and modern topics, such as sponsored search auctions, are covered. Along the way, beautiful mathematical tools used in game theory are introduced, including convexity, fixed-point theorems, and probabilistic arguments. The book is appropriate for a first course in game theory at either the undergraduate or graduate level, whether in mathematics, economics, computer science, or statistics. The importance of game-theoretic thinking transcends the academic setting—for every action we take, we must consider not only its direct effects, but also how it influences the incentives of others.
Author : Carsten Lange
Publisher : CRC Press
Page : 369 pages
File Size : 16,7 MB
Release : 2024-05-20
Category : Mathematics
ISBN : 1040021247
This textbook is a comprehensive guide to machine learning and artificial intelligence tailored for students in business and economics. It takes a hands-on approach to teach machine learning, emphasizing practical applications over complex mathematical concepts. Students are not required to have advanced mathematics knowledge such as matrix algebra or calculus. The author introduces machine learning algorithms, utilizing the widely used R language for statistical analysis. Each chapter includes examples, case studies, and interactive tutorials to enhance understanding. No prior programming knowledge is needed. The book leverages the tidymodels package, an extension of R, to streamline data processing and model workflows. This package simplifies commands, making the logic of algorithms more accessible by minimizing programming syntax hurdles. The use of tidymodels ensures a unified experience across various machine learning models. With interactive tutorials that students can download and follow along at their own pace, the book provides a practical approach to apply machine learning algorithms to real-world scenarios. In addition to the interactive tutorials, each chapter includes a Digital Resources section, offering links to articles, videos, data, and sample R code scripts. A companion website further enriches the learning and teaching experience: https://ai.lange-analytics.com. This book is not just a textbook; it is a dynamic learning experience that empowers students and instructors alike with a practical and accessible approach to machine learning in business and economics. Key Features: Unlocks machine learning basics without advanced mathematics — no calculus or matrix algebra required. Demonstrates each concept with R code and real-world data for a deep understanding — no prior programming knowledge is needed. Bridges the gap between theory and real-world applications with hands-on interactive projects and tutorials in every chapter, guided with hints and solutions. Encourages continuous learning with chapter-specific online resources—video tutorials, R-scripts, blog posts, and an online community. Supports instructors through a companion website that includes customizable materials such as slides and syllabi to fit their specific course needs.
Author : Jingzheng Ren
Publisher : Springer Nature
Page : 225 pages
File Size : 40,43 MB
Release : 2021-06-01
Category : Technology & Engineering
ISBN : 3030675297
This book presents various methods for sustainability assessment of energy systems, under various different conditions and scenarios. It answers the questions of how to measure the sustainability of energy systems by adopting appropriate metrics and methods. This book provides readers with a comprehensive view of the frontiers of sustainability assessment methods for energy system analysis. It presents various methodologies, allowing readers to understand: the complete metrics for sustainability assessment; life cycle thinking for sustainability assessment of energy systems; and the advanced sustainability assessment methods for energy systems. This book is of interest to researchers, engineers, decision makers, and postgraduate students within the field of energy systems, sustainability, and decision analysis.
Author : Andreas Holzinger
Publisher : Springer Nature
Page : 397 pages
File Size : 31,52 MB
Release : 2022
Category : Artificial intelligence
ISBN : 303104083X
This is an open access book. Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and models that are both predictive and interpretable and understandable for humans. Explainable AI is receiving huge interest in the machine learning and AI research communities, across academia, industry, and government, and there is now an excellent opportunity to push towards successful explainable AI applications. This volume will help the research community to accelerate this process, to promote a more systematic use of explainable AI to improve models in diverse applications, and ultimately to better understand how current explainable AI methods need to be improved and what kind of theory of explainable AI is needed. After overviews of current methods and challenges, the editors include chapters that describe new developments in explainable AI. The contributions are from leading researchers in the field, drawn from both academia and industry, and many of the chapters take a clear interdisciplinary approach to problem-solving. The concepts discussed include explainability, causability, and AI interfaces with humans, and the applications include image processing, natural language, law, fairness, and climate science.
