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Jaffe Andrew H.: The Random Universe

The Random Universe

How Models and Probability Help Us Make Sense of the Cosmos

Medien- und Gestaltungsästhetik

Die Gestaltung zukunftsfähiger Hochschullehre erfordert mehr als punktuelle Innovationen. Wie können Lernen und Lehren didaktisch gestaltet und Offenheitskompetenzen aufgebaut werden? Im Kontext gesellschaftlichen Wandels versammeln die Beitragenden interdisziplinäre Perspektiven, die ein differenziertes Bild der Lehre an Hochschulen zeichnen. Dabei diskutieren sie nicht nur theoretische Ansätze, sondern schauen auch auf die gemeinsame Praxisentwicklung - mit dem Ziel, Bildungsforscher*innen, Hochschuldidaktiker*innen und Lehrenden aller Disziplinen Inspirationen, Konzepte und Erfahrungen für eine lernförderliche, adaptive und resonante Lehre im digitalen Zeitalter zu liefern.

Die Gestaltung zukunftsfähiger Hochschullehre erfordert mehr als punktuelle Innovationen. Wie können Lernen und Lehren didaktisch gestaltet und Offenheitskompetenzen aufgebaut werden? Im Kontext gesellschaftlichen Wandels versammeln die Beitragenden interdisziplinäre Perspektiven, die ein differenziertes Bild der Lehre an Hochschulen zeichnen. Dabei diskutieren sie nicht nur theoretische Ansätze, sondern schauen auch auf die gemeinsame Praxisentwicklung – mit dem Ziel, Bildungsforscher*innen, Hochschuldidaktiker*innen und Lehrenden aller Disziplinen Inspirationen, Konzepte und Erfahrungen für eine lernförderliche, adaptive und resonante Lehre im digitalen Zeitalter zu liefern.

Theorie und Praxis zukunftsfähiger Lern- und Lehrprozesse

Hochschullehre im digitalen Zeitalter

Economic Impact of the Energy Transition: Energy-Environment-Economy Models explores and analyzes the complex nature of energy transition, the inter-relationships between energy, environment, and economy, and the use of 3E models to assess economic impact, including a new energy-environment-economy model introduced by the book's editors. The book begins by introducing energy transition and economic impacts, explaining classical macro-econometric models, input-output and CGE models, and macroeconomic models. The second part of the book focuses on energy-environment-economy (3E) models developed globally, with in-depth chapters examining GEC, E3ME, NEMS-MAM, and finally the new System of Three E-Models (STEM). The last section of the book provides case studies demonstrating the utilization of different 3E models in various parts of the world. A final chapter carefully examines the advantages and limitations of each model, its policy implications, and considerations for empirical analysis, discusses how these can be applied, addresses the remaining challenges in the field of energy transition and economic impact analysis, and suggests directions for future research. This is a valuable resource for researchers, advanced students, faculty, scientists, engineers, analysts, policy makers, consultants, and other industry professionals with an interest in energy transition, modeling, policy, and the interconnection between energy, environment, and economy.

Energy-Environment-Economy Models

Economic Impact of the Energy Transition

Das moderne Weltbild – und wem wir es verdanken | Lernen von den wichtigsten Denkerinnen und Denkern, um die Herausforderungen unserer Zeit zu meistern

Die Evolution des Denkens

Regensburger Beiträge zur Digitalisierung des Gesundheitswesens

Die fortschreitende Digitalisierung bringt die komplexen Wechselwirkungen zwischen Pflege, Kultur und Gesellschaft durcheinander. Die Beiträger*innen zeigen, dass in der Pflege ein Kulturwandel in vollem Gange ist, der die Entwicklung einer kultursensiblen Pflege erfordert. Neben den Auswirkungen&nbsp;der neuen Technologien beleuchten sie auch die Themen Migration und&nbsp;darauf aufbauende Machtstrukturen und bieten so eine umfassende, interdisziplinäre Einführung in die modernen Herausforderungen der Pflege, die nicht nur für Wissenschaftler*innen interessant ist, sondern auch praxisbezogene Fragen aufgreift.&nbsp;

Digitale Technologien, Machtstrukturen und Migration im Gesundheitssektor

Pflegekultur im Wandel

A groundbreaking bridge between traditional financial theories and emerging interdisciplinary approaches, offering a unique perspective on finance in these uncertain times. From a seasoned expert in finance, behavioral finance, complexity science, and quantum finance. Adaptive Finance is a forward-looking exploration of the evolving world of finance, addressing the most pressing challenges and emerging trends facing financial markets today. It examines how traditional economic and financial models, long dominant in the field, are being redefined by advancements in behavioral science, mathematics, and technology. Financial professionals and academics must confront risks and dynamics that established theories often fail to capture in an era marked by complexity, technological disruption, and global uncertainty. This book provides the intellectual foundation needed to tackle these challenges. The chapters span a wide range of topics—from behavioral finance and causal models to quantum finance and innovations in financial systems—offering a comprehensive view of the forces reshaping finance. The book begins with well-established frameworks and progressively explores cutting-edge concepts, guiding readers through the transformation of financial theory and practice. Each chapter stands on its own, making the book suitable for both comprehensive study and selective reading. The authors present a timely perspective on how finance must adapt to a more complex and dynamic world.

A groundbreaking bridge between traditional financial theories and emerging interdisciplinary approaches, offering a unique perspective on finance in these uncertain times. From a seasoned expert in finance, behavioral finance, complexity science, and quantum finance. Adaptive Finance is a forward-looking exploration of the evolving world of finance, addressing the most pressing challenges and emerging trends facing financial markets today. It examines how traditional economic and financial models, long dominant in the field, are being redefined by advancements in behavioral science, mathematics, and technology. Financial professionals and academics must confront risks and dynamics that established theories often fail to capture in an era marked by complexity, technological disruption, and global uncertainty. This book provides the intellectual foundation needed to tackle these challenges. The chapters span a wide range of topics—from behavioral finance and causal models to quantum finance and innovations in financial systems—offering a comprehensive view of the forces reshaping finance. The book begins with well-established frameworks and progressively explores cutting-edge concepts, guiding readers through the transformation of financial theory and practice. Each chapter stands on its own, making the book suitable for both comprehensive study and selective reading. The authors present a timely perspective on how finance must adapt to a more complex and dynamic world.

Embracing Uncertainty and Complexity

Adaptive Finance

International Series in Operations Research & Management Science

This book serves as a concise and reader-friendly, yet rigorous and thought-provoking introduction to the field of statistical inference. As opposed to classical books on mathematical statistics, where there is a strong emphasis on proofs, this book focuses on developing statistical thinking, intuitive understandings of the subject, and specific applications of statistical inference in data science. As a corollary, though also covered, proofs will not be of paramount importance in the book. Their main role will be to provide the intuition and rationale behind the corresponding methods. The focus is on methods of statistical inference and their scope and limitations for real-world applications. On the other hand, statistical inference is not simply a toolbox that contains ready-made answers to all data-related questions. Almost always, as in solving engineering problems, statistical inference and analysis of new data require adjustment of existing tools or even developing completely new methods. To enable readers to modify existing methods and develop new ones, the book not only explains how the standard methods work, but also why, when, and under what assumptions. All chapters include end-of-chapter problems, with solutions provided at the end of the book.&nbsp;One of the goals of the book is to serve as an introductory text on statistical inference that can be used for teaching a semester-long course. The book is suitable for future and junior data scientists, data analysts, and industry researchers, as well as graduate and upper undergraduate students in computing and mathematical sciences, and master's and Ph.D. students in non-mathematical sciences and engineering. While familiarity with probability is assumed, readers need no prior knowledge of statistics.“Professor Konstantin Zuev has written an entertaining and highly readable book on real-world methods for statistical inference in data analysis. Loaded with real-world case studies and complete solutions to all exercises, this book is a valuable resource for both students and practitioners.”— Mor Harchol-Balter, Bruce J. Nelson Professor of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA

This book serves as a concise and reader-friendly, yet rigorous and thought-provoking introduction to the field of statistical inference. As opposed to classical books on mathematical statistics, where there is a strong emphasis on proofs, this book focuses on developing statistical thinking, intuitive understandings of the subject, and specific applications of statistical inference in data science. As a corollary, though also covered, proofs will not be of paramount importance in the book. Their main role will be to provide the intuition and rationale behind the corresponding methods. The focus is on methods of statistical inference and their scope and limitations for real-world applications. On the other hand, statistical inference is not simply a toolbox that contains ready-made answers to all data-related questions. Almost always, as in solving engineering problems, statistical inference and analysis of new data require adjustment of existing tools or even developing completely new methods. To enable readers to modify existing methods and develop new ones, the book not only explains how the standard methods work, but also why, when, and under what assumptions. All chapters include end-of-chapter problems, with solutions provided at the end of the book.&nbsp;One of the goals of the book is to serve as an introductory text on statistical inference that can be used for teaching a semester-long course. The book is suitable for future and junior data scientists, data analysts, and industry researchers, as well as graduate and upper undergraduate students in computing and mathematical sciences, and master's and Ph.D. students in non-mathematical sciences and engineering. While familiarity with probability is assumed, readers need no prior knowledge of statistics.

Foundations of Data Analysis

Fundamentals of Statistical Inference

Agricultural Statistics

Agricultural Statistics is published each year to meet the diverse need for a reliable reference book on agricultural production. It covers an extensive list of issues related to agriculture including livestock, farm values, crop production, and foreign trade. The estimates for crops, livestock, and poultry made by the U.S. Department of Agriculture are prepared mainly to give timely current state and national totals and averages. They are based on data obtained by sample surveys of farmers and of people who do business with farmers. The survey data are supplemented by information from the Census of Agriculture taken every five years. Being estimates, they are subject to revision as more data become available from commercial or government sources. Unless otherwise indicated, the totals for the United States shown in the various tables on area, production, numbers, price, value, supplies, and disposition are based on official estimates. They exclude states for which no official estimates are compiled. Professionals in farming, ranching, agricultural consulting, and animal science will find this most valuable.

Agricultural Statistics 2025

A novel framework for how entrepreneurs can use Bayesian reasoning to make decisions. This edited volume introduces and explores the concept of Bayesian entrepreneurship, a novel framework for understanding entrepreneurial decision-making under uncertainty. It brings together contributions from leading scholars to examine how entrepreneurs form beliefs about opportunities, learn through experimentation, and make strategic decisions. The Bayesian approach emphasizes the critical interaction between an entrepreneur’s prior beliefs and their capacity for learning and experimentation to update those beliefs. This dynamic process allows entrepreneurs to formulate and test theories about value creation and capture, attract resources and capabilities, and implement effective entrepreneurial strategies. The volume is organized around four key themes: <ul><li>the foundations and distinguishing features of Bayesian entrepreneurship </li><li>how entrepreneurs form and update prior beliefs in entrepreneurial decision-making</li><li>the role of experimentation and learning in entrepreneurial strategy</li><li>applications to entrepreneurship education and practice</li></ul> Bayesian Entrepreneurship offers both theoretical contributions advancing our understanding of entrepreneurial decision-making and practical insights for entrepreneurs and educators.

A novel framework for how entrepreneurs can use Bayesian reasoning to make decisions. This edited volume introduces and explores the concept of Bayesian entrepreneurship, a novel framework for understanding entrepreneurial decision-making under uncertainty. It brings together contributions from leading scholars to examine how entrepreneurs form beliefs about opportunities, learn through experimentation, and make strategic decisions. The Bayesian approach emphasizes the critical interaction between an entrepreneur’s prior beliefs and their capacity for learning and experimentation to update those beliefs. This dynamic process allows entrepreneurs to formulate and test theories about value creation and capture, attract resources and capabilities, and implement effective entrepreneurial strategies. The volume is organized around four key themes: <ul><li>the foundations and distinguishing features of Bayesian entrepreneurship </li><li>how entrepreneurs form and update prior beliefs in entrepreneurial decision-making</li><li>the role of experimentation and learning in entrepreneurial strategy</li><li>applications to entrepreneurship education and practice</li></ul> Bayesian Entrepreneurship offers both theoretical contributions advancing our understanding of entrepreneurial decision-making and practical insights for entrepreneurs and educators.

Bayesian Entrepreneurship

Klassiker auslegen

Thomas S. Kuhn: Die Struktur wissenschaftlicher Revolutionen

'Lucid, entertaining and precise... a brilliant warning about the gamification of everyday life' Tim Clare, Guardian Is this the game you want to be playing? Scoring systems are everywhere. Underpinning our daily lives - whether it's the fit bits on our wrists, likes on social media, and even school rankings - they have become pervasive and increasingly dangerous, warping our desires and outsourcing our values to external institutions. Instead of encouraging us to be more playful, to take pleasure in the journey of striving towards a goal, institutions, corporations and bureaucracies weaponize scoring systems to impose their own interests. No matter what, we always seem to be playing by someone else's rules. In The Score, philosopher C. Thi Nguyen shows us how this newly 'gamified' world has fundamentally captured our value systems, turning what might be moral or personal life choices into numerical data, and forcing us to prioritise what can be measured and monetized over what is truly meaningful to us. A life-long lover of online and board games himself, Nguyen argues that we should not stop playing games but rather take a step back and become more aware of their immersive and profound power, so that we might chart a way towards more creative and joyful lives. To start playing our own game.

How to Stop Playing Someone Else’s Game

The Score

Gesundheit, Kommunikation und Gesellschaft

Die Public Health Humanities verstehen Gesundheit und Krankheit als kulturell und sozial geprägte Phänomene. Sie ergänzen die empirisch orientierte Multidisziplin Public Health um geisteswissenschaftliche und künstlerische Perspektiven. Dabei fragen sie nach der Bedeutung des Künstlerischen für die Gesundheit, interessieren sich für moralische, soziale und kulturelle Dimensionen von Gesundheit und Krankheit in Kunst bzw. Medien und bieten theoretische Zugänge für gesundheitliche Analysen an. Bezugsdisziplinen sind u.a. Kunst-, Kultur- und Medienwissenschaften, Philosophie und Geschichte. Die Beiträger*innen leuchten das Spektrum unterschiedlicher Perspektiven und Zugänge der Public Health Humanities erstmals auf Deutsch aus.

Perspektiven der Public Health Humanities

Kunst – Kultur – Gesundheit

A rigorous, cutting-edge overview of the range of methods used to conduct causal inference in the social sciences. This textbook provides a lucid, rigorous, and cutting-edge overview of the methods used to conduct causal inference in the social sciences, covering all the core techniques and latest advances. Offering a detailed survey of the current state of microeconometric theory, Damian Clarke delves deeply into machine learning applications and presents developments in difference-in-difference methods, instrumental variables, multiple hypothesis testing, and other advanced topics. With a diverse range of examples and exercises offering hands-on experience, Applied Microeconometrics equips graduate students and researchers to apply state-of-the art scholarship to actionable problems. <ul><li>Integrates a rich array of machine learning methods into causal modeling frameworks</li><li>Covers recent advances in difference-in-differences and dynamic research designs, formal discussions of challenges related to inference and hypothesis testing, and heterogeneity analysis</li><li>Features a breadth of real-world examples from recent papers</li><li>Includes coding implementation in Python, R, and Stata</li></ul>

Applied Microeconometrics

The Random Universe

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