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Sargent T: Dynamic Programming

Dynamic Programming

Finite States

This textbook continues to fill the need for a systems engineering design textbook specifically written for students from a large variety of backgrounds-engineering, human factors, psychology, and physiology fields. These students need to learn about systems engineering, without it being too technical or at too much of a higher conceptual level for them. This new edition continues to give students exposure to the systems and systems engineering concepts they need and offers just enough technical expertise to use and apply what they have learned.Systems Engineering: Design Principles and Models, Second Edition, is completely updated and offers an overview of systems concepts with emphasis on the system life cycle. It provides a comprehensive overview of systems design processes and all activities involved, along with a review of systems analysis and modeling tools and techniques used. The updated textbook includes exercises and examples/case studies to illustrate real-world applications after each chapter. Emphasis is placed on the human element in systems design.This book is specifically written as a textbook for undergraduate, first-year graduate students, and those who have little or no engineering background. It enables them to learn about systems engineering and is a great book for those who will get involved in systems engineering and design and need to work with engineers in various fields, including project managers, human factors engineers, aerospace and aviation professionals, usability engineers, mechanical and electrical engineers, software engineers, and human factors professionals. This book can also be used as an introduction to systems engineering for professionals.PowerPoint slides, along with a solutions manual, are available for all qualified textbook adoptions.

Design Principles and Models, Second Edition

Systems Engineering

World Scientific Lecture Notes In Economics And Policy

This book attempts to understand the operation and properties of 'market economies'. We do this by carefully examining, refining and in some cases extending the major theorems that Microeconomics has established in response to the Walrasian conjecture. We critically evaluate theorems and results, that assert the existence, uniqueness, stability, optimality and empirical relevance of Walrasian equilibrium states. As a result of our work, we attempt to comment on the wisdom of recommending that socio-economic systems operate as a collection of deregulated competitive markets.

Lectures In General Equilibrium Microeconomics

Elements in Econophysics

Classical mechanics provided the conceptual and methodological foundations of neoclassical economics, which has its roots in economic individualism. Since the early twentieth century, statistical mechanics has underpinned a lesser-known approach to economics and finance, one that focuses on aggregates and the interactions between individuals. This has led to the emergence of a new field of research, known as econophysics, which brings to the fore concepts such as emergent properties, power laws, networks, entropy, and multifractality, thereby reshaping economic enquiry.

A Connected History of Two Disciplines

The Rise of Econophysics

In today's data-driven business world, organizations struggle to extract actionable insights from vast amounts of information. This textbook bridges theory and practice, offering structured approaches to leverage data for enhanced decision-making through machine learning, AI, and optimization modeling-transforming raw data into competitive business intelligence.Decision Making and Analytics: Data-Driven Business Intelligence and Optimization features advanced analytical methodologies paired with real-world implementation studies, creating a progressive learning path from customer behavior analysis through hybrid decision models. By integrating fuzzy logic, neural networks, text mining, and reinforcement learning, it provides a comprehensive toolkit applicable across business scenarios, turning abstract concepts into practical solutions.Serving multiple audiences, this resource bridges the gap between classroom theory and industry practice for students in industrial engineering, business analytics, and management information systems. Practitioners gain applicable methodologies to improve organizational decision-making. At the same time, researchers benefit from cutting-edge approaches across various domains, and policymakers can develop data-informed strategies-addressing the growing demand for professionals who translate data into strategic decisions.The textbook employs open-ended questions that develop analytical reasoning skills without predetermined solutions, better preparing students for real-world complexity. Supporting materials include high-quality figure slides, as well as PowerPoint slides, for qualified adopters, enabling engaging learning environments that effectively communicate complex concepts while building practical, data-driven decision-making skills.

Data-Driven Business Intelligence and Optimization

Decision-Making and Analytics

Routledge Frontiers of Political Economy

This book offers a comprehensive exploration of how empirical stock-flow consistent (SFC) modelling can be used as a tool for macroeconomic analysis. Based on a coherent framework rooted in national accounting principles, SFC models, which integrate both financial and real economy variables, are well-suited for capturing financial crises, macroeconomic imbalances and economic policy impacts.Focusing on France, the book presents the SFC FR model, an empirical SFC model based on French national data, and examines four decades of structural change of the French economy marked by financialisation, policy shifts, evolving trade and public deficits. Through detailed simulations, it assesses the effectiveness of a range of fiscal, monetary, and climate policies. Instead of supply side policy, which has been dominant, it provides insights into possible alternative economic strategies that prioritise equity and sustainability for the years 2025-2035.Bridging the gap between empirical realism and theoretical consistency, the book is a vital resource for economists, policymakers and researchers seeking a realistic and policy-relevant approach to macroeconomic analysis especially for economic policy design and evaluation.

An Empirical SFC Perspective

Neoliberal Economic Policy and Alternatives for France

Lecture Notes in Operations Research

This book presents the best papers from the 6th International Conference on Mathematical Research for Blockchain Economy (MARBLE) 2025, held in Athens, Greece. While most blockchain conferences and forums are dedicated to business applications, product development or Initial Coin Offering (ICO) launches, this conference focused on the mathematics behind blockchain to bridge the gap between practice and theory.
Blockchain technology has been considered as the most fundamental and revolutionizing invention since the Internet. Every year, thousands of blockchain projects are launched and circulated in the market, and there is a tremendous wealth of blockchain applications, from finance to healthcare, education, media, logistics, and more. However, due to theoretical and technical barriers, most of these applications are impractical for use in a real-world business context. The papers in this book reveal the challenges and limitations, such as scalability, latency, privacy, and security and showcase solutions and developments to overcome them.

This book presents the best papers from the 6th International Conference on Mathematical Research for Blockchain Economy (MARBLE) 2025, held in Athens, Greece. While most blockchain conferences and forums are dedicated to business applications, product development or Initial Coin Offering (ICO) launches, this conference focused on the mathematics behind blockchain to bridge the gap between practice and theory.Blockchain technology has been considered as the most fundamental and revolutionizing invention since the Internet. Every year, thousands of blockchain projects are launched and circulated in the market, and there is a tremendous wealth of blockchain applications, from finance to healthcare, education, media, logistics, and more. However, due to theoretical and technical barriers, most of these applications are impractical for use in a real-world business context. The papers in this book reveal the challenges and limitations, such as scalability, latency, privacy, and security and showcase solutions and developments to overcome them.

6th International Conference MARBLE 2025, Athens, Greece

Mathematical Research for Blockchain Economy

The book addresses the underlying question of mis-specification in models estimated with spatial data. When spatial econometrics focusses on model estimation techniques, it is most often assumed that mis-specification is restricted to spatial autocorrelation and occasionally heteroskedasticity.

The book addresses the underlying question of mis-specification in models estimated with spatial data. When spatial econometrics focusses on model estimation techniques, it is most often assumed that mis-specification is restricted to spatial autocorrelation and occasionally heteroskedasticity.

Introduction to Applied Spatial Econometrics

A paradigm-shifting textbook that teaches graduate students in economics how to use economic models to interpret data. Offering a new pedagogical paradigm, this textbook delivers a ready-made economics course covering the general concepts that unify different approaches to bringing economic models to data. Jesse Shapiro presents core ideas in quantitative economics as an abstract, cohesive whole and introduces canonical models that can be used across a wide range of applications. This course-in-a-book exposes students to stylized applications of these models that illuminate their foundations as well as recent research articles demonstrating the models’ broad applicability. <ul><li>Provides a coherent, generalized approach to making model-based inferences in empirical economics</li><li>Introduces central concepts and canonical models in quantitative economics</li><li>Features applications of modern research topics</li><li>Suits self-paced learning and classroom use alike</li></ul>

A paradigm-shifting textbook that teaches graduate students in economics how to use economic models to interpret data. Offering a new pedagogical paradigm, this textbook delivers a ready-made economics course covering the general concepts that unify different approaches to bringing economic models to data. Jesse Shapiro presents core ideas in quantitative economics as an abstract, cohesive whole and introduces canonical models that can be used across a wide range of applications. This course-in-a-book exposes students to stylized applications of these models that illuminate their foundations as well as recent research articles demonstrating the models’ broad applicability. <ul><li>Provides a coherent, generalized approach to making model-based inferences in empirical economics</li><li>Introduces central concepts and canonical models in quantitative economics</li><li>Features applications of modern research topics</li><li>Suits self-paced learning and classroom use alike</li></ul>

Introduction to Quantitative Economics

This open access book is a unique resource in computational mathematical optimization. It conveys the entire spectrum from the basic theory to the most advanced tricks in reformulations and decomposition approaches to solving mixed-integer linear programs. The book has useful and partly previously unpublished facts for students, teachers, and practitioners alike. The content covers algorithmic ideas like column generation, cutting planes, and branch-and-price, reformulation techniques like Dantzig-Wolfe decomposition and Lagrangian relaxation, the very important application domains of vehicle routing and crew scheduling, and has some practical advice when it comes to an implementation. Many notes and even more didactic illustrations and examples bring the theory to life, complemented by almost 140 exercises, including solutions. As a side effect, readers learn ways how to formulate integer programming models for a great variety of combinatorial optimization problems. The material draws from more than 400 references and the four authors' own decades of experience in the field. Several photos taken by the authors bring a personal touch to the writing, among other tiny anecdotal elements that the connoisseur will appreciate. The book is not, and cannot be encyclopedic, but it has the ambition to be the standard text and main reference in the field for the years to come.
Students, researchers, and practitioners aiming to solve large, complex discrete and combinatorial optimization problems—whether in industry, science, or technology—will find this book essential for exploring optimal and near-optimal solutions through decomposition methods and column generation.

This open access book is a unique resource in computational mathematical optimization. It conveys the entire spectrum from the basic theory to the most advanced tricks in reformulations and decomposition approaches to solving mixed-integer linear programs. The book has useful and partly previously unpublished facts for students, teachers, and practitioners alike. The content covers algorithmic ideas like column generation, cutting planes, and branch-and-price, reformulation techniques like Dantzig-Wolfe decomposition and Lagrangian relaxation, the very important application domains of vehicle routing and crew scheduling, and has some practical advice when it comes to an implementation. Many notes and even more didactic illustrations and examples bring the theory to life, complemented by almost 140 exercises, including solutions. As a side effect, readers learn ways how to formulate integer programming models for a great variety of combinatorial optimization problems. The material draws from more than 400 references and the four authors' own decades of experience in the field. Several photos taken by the authors bring a personal touch to the writing, among other tiny anecdotal elements that the connoisseur will appreciate. The book is not, and cannot be encyclopedic, but it has the ambition to be the standard text and main reference in the field for the years to come.Students, researchers, and practitioners aiming to solve large, complex discrete and combinatorial optimization problems—whether in industry, science, or technology—will find this book essential for exploring optimal and near-optimal solutions through decomposition methods and column generation.

Branch-and-Price

Platform Supply Chains in the AI Era

Network Design and Planning

Platform Supply Chains 3

Chapman & Hall/CRC Texts in Statistical Science

Bayesian Statistical Methods: With Applications to Machine Learning provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. Compared to others, this book is more focused on Bayesian methods applied routinely in practice, including multiple linear regression, mixed effects models and generalized linear models. This second edition includes a new chapter on Bayesian machine learning methods to handle large and complex datasets and several new applications to illustrate the benefits of the Bayesian approach in terms of uncertainty quantification. Readers familiar with only introductory statistics will find this book accessible, as it includes many worked examples with complete R code, and comparisons are presented with analogous frequentist procedures. The book can be used as a one-semester course for advanced undergraduate and graduate students and can be used in courses comprising undergraduate statistics majors, as well as non-statistics graduate students from other disciplines such as engineering, ecology and psychology. In addition to thorough treatment of the basic concepts of Bayesian inferential methods, the book covers many general topics:<ul> <li>Advice on selecting prior distributions</li> <li>Computational methods including Markov chain Monte Carlo (MCMC) sampling</li> <li>Model-comparison and goodness-of-fit measures, including sensitivity to priors.</li> </ul>To illustrate the flexibility of the Bayesian approaches for complex data structures, the latter chapters provide case studies covering advanced topics:<ul> <li>Handling of missing and censored data</li> <li>Priors for high-dimensional regression models</li> <li>Machine learning models including Bayesian adaptive regression trees and deep learning</li> <li>Computational techniques for large datasets</li> <li>Frequentist properties of Bayesian methods.</li> </ul>The advanced topics are presented with sufficient conceptual depth that the reader will be able to carry out such analysis and argue the relative merits of Bayesian and classical methods. A repository of R code, motivating data sets and complete data analyses is made available on the book's website.

With Applications to Machine Learning

Bayesian Statistical Methods

Econometric Society Monographs

In the winter of 2021, the Swedish Nobel Foundation organized a Nobel symposium 'One Hundred Years of Game Theory' to commemorate the publication of famous mathematician Emile Borel's 'La théorie du jeu et les équations intégrales à noyau symétrique'. The symposium gathered roughly forty of the world's most prominent scholars ranging from mathematical foundations to applications in economics, political science, computer science, biology, sociology, and other fields. One Hundred Years of Game Theory brings together their writings to summarize and put in perspective the main achievements of game theory in the last one hundred years. They address past achievements, taking stock of what has been accomplished and contemplating potential future developments and challenges. Offering cross-disciplinary discussions between eminent researchers including five Nobel laureates, one Fields medalist and two Gödel prize winners, the contributors provide a fascinating landscape of game theory and its wide range of applications.

A Nobel Symposium

One Hundred Years of Game Theory

Dynamic Programming

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