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A comprehensive textbook on the tools of mathematical sociology and their applicationsMathematical models and computer simulations of complex social systems have become everyday tools in sociology. Yet until now, students had no up-to-date textbook from which to learn these techniques. Introduction to Mathematical Sociology fills this gap, providing undergraduates with a comprehensive, self-contained primer on the mathematical tools and applications that sociologists use to understand social behavior.Phillip Bonacich and Philip Lu cover all the essential mathematics, including linear algebra, graph theory, set theory, game theory, and probability. They show how to apply these mathematical tools to demography; patterns of power, influence, and friendship in social networks; Markov chains; the evolution and stability of cooperation in human groups; chaotic and complex systems; and more.Introduction to Mathematical Sociology also features numerous exercises throughout, and is accompanied by easy-to-use Mathematica-based computer simulations that students can use to examine the effects of changing parameters on model behavior.Provides an up-to-date and self-contained introduction to mathematical sociologyExplains essential mathematical tools and their applicationsIncludes numerous exercises throughoutFeatures easy-to-use computer simulations to help students master concepts

This dissertation deals with the mathematical modelling of sovereign credit risk and its applications. In Chapter 1, motivated by the European sovereign debt crisis, we propose a hybrid sovereign risk model which takes into account both the movement of the sovereign solvency and the impact of critical political events besides the idiosyncratic credit risk. We are interested in the probability that the default occurs at critical political dates, for which we obtain closed-form formulae in a Markovian setting, where we deal with some unusual features, such as a treatment of the CEV model when the elasticity parameter β > 1. We compute explicitly the compensator process of default and show that the intensity process does not exist. In Chapter 2, by studying certain hybrid models in literature on credit risks, we consider a type of random times whose conditional probability distribution is not continuous and by which standard intensity and density hypotheses in the enlargement of filtrations are not satisfied. We propose a generalised density approach, where the hypothesis of Jacod is relaxed, in order to deal with such random times in the framework of progressive enlargement of filtrations We also study classic problems such as the computation of the compensator process of the random time, the decomposition of the Azéma supermartingale, as well as the martingale characterisation. The martingale and semimartingale decompositions in the enlarged filtration show that the H'-hypothesis holds in this generalised framework. In Chapter 3, we display several applications of the models proposed in the previous chapters. The most important application of the hybrid default model and the generalised density approach is the valuation of default claims. The results explain the significant negative jumps in the long-term Greek government bond yield during the sovereign debt crisis. The solvency of Greece tends to fall gradually through time and the bond yield has negative jumps when critical political events are held. In ...

This dissertation deals with the mathematical modelling of sovereign credit risk and its applications. In Chapter 1, motivated by the European sovereign debt crisis, we propose a hybrid sovereign risk model which takes into account both the movement of the sovereign solvency and the impact of critical political events besides the idiosyncratic credit risk. We are interested in the probability that the default occurs at critical political dates, for which we obtain closed-form formulae in a Markovian setting, where we deal with some unusual features, such as a treatment of the CEV model when the elasticity parameter β > 1. We compute explicitly the compensator process of default and show that the intensity process does not exist. In Chapter 2, by studying certain hybrid models in literature on credit risks, we consider a type of random times whose conditional probability distribution is not continuous and by which standard intensity and density hypotheses in the enlargement of filtrations are not satisfied. We propose a generalised density approach, where the hypothesis of Jacod is relaxed, in order to deal with such random times in the framework of progressive enlargement of filtrations We also study classic problems such as the computation of the compensator process of the random time, the decomposition of the Azéma supermartingale, as well as the martingale characterisation. The martingale and semimartingale decompositions in the enlarged filtration show that the H'-hypothesis holds in this generalised framework. In Chapter 3, we display several applications of the models proposed in the previous chapters. The most important application of the hybrid default model and the generalised density approach is the valuation of default claims. The results explain the significant negative jumps in the long-term Greek government bond yield during the sovereign debt crisis. The solvency of Greece tends to fall gradually through time and the bond yield has negative jumps when critical political events are held. In ...

This dissertation deals with the mathematical modelling of sovereign credit risk and its applications. In Chapter 1, motivated by the European sovereign debt crisis, we propose a hybrid sovereign risk model which takes into account both the movement of the sovereign solvency and the impact of critical political events besides the idiosyncratic credit risk. We are interested in the probability that the default occurs at critical political dates, for which we obtain closed-form formulae in a Markovian setting, where we deal with some unusual features, such as a treatment of the CEV model when the elasticity parameter β > 1. We compute explicitly the compensator process of default and show that the intensity process does not exist. In Chapter 2, by studying certain hybrid models in literature on credit risks, we consider a type of random times whose conditional probability distribution is not continuous and by which standard intensity and density hypotheses in the enlargement of filtrations are not satisfied. We propose a generalised density approach, where the hypothesis of Jacod is relaxed, in order to deal with such random times in the framework of progressive enlargement of filtrations We also study classic problems such as the computation of the compensator process of the random time, the decomposition of the Azéma supermartingale, as well as the martingale characterisation. The martingale and semimartingale decompositions in the enlarged filtration show that the H'-hypothesis holds in this generalised framework. In Chapter 3, we display several applications of the models proposed in the previous chapters. The most important application of the hybrid default model and the generalised density approach is the valuation of default claims. The results explain the significant negative jumps in the long-term Greek government bond yield during the sovereign debt crisis. The solvency of Greece tends to fall gradually through time and the bond yield has negative jumps when critical political events are held. In ...

International audience ; A set of general formulas is proposed for the probability of failure on demand (PFD) assessment of MooN architecture (i.e. k-out-of-n) systems subject to partial and full tests. Partial tests (e.g. visual inspections, imperfect testing) may detect only some failures, whereas owing to a full test, the system is restored to an as good as new condition. Following the proposed approach and according to an example, performance estimations of the system and test policies are presented, by using the feedback from partial and full tests. An optimization of the partial test distribution is also proposed, which allows reducing the average probability of system failure on demand (PFDavg).

International audience ; A set of general formulas is proposed for the probability of failure on demand (PFD) assessment of MooN architecture (i.e. k-out-of-n) systems subject to partial and full tests. Partial tests (e.g. visual inspections, imperfect testing) may detect only some failures, whereas owing to a full test, the system is restored to an as good as new condition. Following the proposed approach and according to an example, performance estimations of the system and test policies are presented, by using the feedback from partial and full tests. An optimization of the partial test distribution is also proposed, which allows reducing the average probability of system failure on demand (PFDavg).

International audience ; A set of general formulas is proposed for the probability of failure on demand (PFD) assessment of MooN architecture (i.e. k-out-of-n) systems subject to partial and full tests. Partial tests (e.g. visual inspections, imperfect testing) may detect only some failures, whereas owing to a full test, the system is restored to an as good as new condition. Following the proposed approach and according to an example, performance estimations of the system and test policies are presented, by using the feedback from partial and full tests. An optimization of the partial test distribution is also proposed, which allows reducing the average probability of system failure on demand (PFDavg).

International audience ; A set of general formulas is proposed for the probability of failure on demand (PFD) assessment of MooN architecture (i.e. k-out-of-n) systems subject to partial and full tests. Partial tests (e.g. visual inspections, imperfect testing) may detect only some failures, whereas owing to a full test, the system is restored to an as good as new condition. Following the proposed approach and according to an example, performance estimations of the system and test policies are presented, by using the feedback from partial and full tests. An optimization of the partial test distribution is also proposed, which allows reducing the average probability of system failure on demand (PFDavg).

Stochastic games are have an element of chance: the state of the next round is determined probabilistically depending upon players' actions and the current state. Successful players need to balance the need for short-term payoffs while ensuring future opportunities remain high. The various techniques needed to analyze these often highly non-trivial games are a showcase of attractive mathematics, including methods from probability, differential equations, algebra, and combinatorics. This book presents a course on the theory of stochastic games going from the basics through to topics of modern research, focusing on conceptual clarity over complete generality. Each of its chapters introduces a new mathematical tool - including contracting mappings, semi-algebraic sets, infinite orbits, and Ramsey's theorem, among others - before discussing the game-theoretic results they can be used to obtain. The author assumes no more than a basic undergraduate curriculum and illustrates the theory with numerous examples and exercises, with solutions available online.

"The Medium of Contingency brings a completely new and original perspective to the relationship between the reality of the marketplace and its disassociation with the probabilistic and statistical tools used to model and support it. It utilises the author's extensive experience and research in derivatives pricing, the analysis of the theory of contingent claims and philosophy, and builds on the work of thinkers such as von Mises, Kolmogorov, Shafer and Vovk. This book argues a new way of thinking about the mathematical applications of finance: that market is "real" and relates to contingency, and yet has nothing to do with probability, when the latter is understood in its relation with statistics. Thus, a new appreciation of these relationships need to be developed to effectively apply them to the market. This book provides original insight into the relationship between financial mathematics and financial markets, and provides new perspectives on the theory of derivatives pricing in the quantitative finance industry."--

This book is intended as a textbook for a first course in applied statistics for students of economics, public administration and business administration. A limited knowledge of mathematics and - in one single chapter - some knowledge of elementary matrix algebra is required for understanding the text. Complicated mathematical proofs are avoided and the explanations are based on intuition and numerical examples. The aim of this book is to enable the student to understand the reasoning underlying a statistical analysis and to apply statistical methods to problems likely to be met within the fields of economics, public administration and business administration. The topics covered by the book are: - methods for exploratory data analysis - probability theory and standard statistical distributions - statistical inference theory - and three main areas of application: regression analysis, survey sampling and contingency tables. The treatment of exploratory data analysis, regression analysis and the analysis of contingency tables are based on the most recent theoretical developments in these areas. Most of the examples have never been presented before in English textbooks

This book is an exploration of the ubiquity of ambiguity in decision-making under uncertainty. It presents various essays on behavioral economics and behavioral finance that draw on the theory of Black Swans (Taleb 2010), which argues for a distinction between unprecedented events in our past and unpredictable events in our future. The defining property of Black Swan random events is that they are unpredictable, i.e., highly unlikely random events. In this text, Mandelbrot's (1972) operational definition of risky random unpredictable events is extended to Black Swan assets – assets for which the cumulative probability distribution or conditional probability distribution of random future asset returns is a power distribution. Ambiguous assets are assets for which the uncertainties of future returns are not risks. Consequently, there are two disjoint classes of Black Swan assets: Risky Black Swan assets and Ambiguous Black Swan assets, a new class of ambiguous assets with unpredictable random future outcomes. The text is divided into two parts, the first of which focuses on affective moods, introduces affective utility functions and discusses the ambiguity of Black Swans. The second part, which shifts the spotlight to affective equilibrium in asset markets, features chapters on affective portfolio analysis and Walrasian and Gorman Polar Form Equilibrium Inequalities. In order to gain the most from the book, readers should have completed the standard introductory graduate courses on microeconomics, behavioral finance, and convex optimization. The book is intended for advanced undergraduates, graduate students and post docs specializing in economic theory, experimental economics, finance, mathematics, computer science or data analysis.

Thirty years ago, biologists could get by with a rudimentary grasp of mathematics and modeling. Not so today. In seeking to answer fundamental questions about how biological systems function and change over time, the modern biologist is as likely to rely on sophisticated mathematical and computer-based models as traditional fieldwork. In this book, Sarah Otto and Troy Day provide biology students with the tools necessary to both interpret models and to build their own. The book starts at an elementary level of mathematical modeling, assuming that the reader has had high school mathematics and first-year calculus. Otto and Day then gradually build in depth and complexity, from classic models in ecology and evolution to more intricate class-structured and probabilistic models. The authors provide primers with instructive exercises to introduce readers to the more advanced subjects of linear algebra and probability theory. Through examples, they describe how models have been used to understand such topics as the spread of HIV, chaos, the age structure of a country, speciation, and extinction. Ecologists and evolutionary biologists today need enough mathematical training to be able to assess the power and limits of biological models and to develop theories and models themselves. This innovative book will be an indispensable guide to the world of mathematical models for the next generation of biologists. A how-to guide for developing new mathematical models in biology Provides step-by-step recipes for constructing and analyzing models Interesting biological applications Explores classical models in ecology and evolution Questions at the end of every chapter Primers cover important mathematical topics Exercises with answers Appendixes summarize useful rules Labs and advanced material available

This is the first in a set of10 books written for professionals in quantitative finance. These books fill the gap between informal mathematical developments found in introductory materials, and more advanced treatments that summarize without formally developing the important foundational results professionals need. Book I in the Foundations in Quantitative Finance Series develops topics in measure spaces and measurable functions and lays the foundation for subsequent volumes. Lebesgue and then Borel measure theory are developed on , motivating the general extension theory of measure spaces that follows. This general theory is applied to finite product measure spaces, Borel measures on n, and infinite dimensional product probability spaces. The overriding goal of these books is a complete and detailed development of the many mathematical theories and results one finds in popular resources in finance and quantitative finance. Each book is dedicated to a specific area of mathematics or probability theory, with applications to finance that are relevant to the needs of professionals. Practitioners, academic researchers, and students will find these books valuable to their career development. All ten volumes are extensively self-referenced. The reader can enter the collection at any point or topic of interest, and then work backward to identify and fill in needed details. This approach also works for a course or self-study on a given volume, with earlier books used for reference. Advanced quantitative finance books typically develop materials with an eye to comprehensiveness in the given subject matter, yet not with an eye toward efficiently curating and developing the theories needed for applications in quantitative finance. This book and series of volumes fill this need.

Cover -- Contents -- List of tables -- List of figures -- List of boxes -- List of key concepts -- Preface -- 1 Data, samples and statistics -- 1.1 Chapter overview -- 1.2 What are data? -- 1.3 Samples and populations -- 1.4 Central tendency -- 1.5 Dispersion within a sample -- 1.6 Description, inference and bias -- 1.7 R code for Chapter 1 -- 1.8 Notes on SPSS syntax for Chapter 1 -- 1.9 Bibliography and further reading -- 2 Probability distributions -- 2.1 Chapter overview -- 2.2 Why are probability distributions important in statistics? -- 2.3 Discrete distributions -- 2.4 Continuous distributions -- 2.5 R code for Chapter 2 -- 2.6 Notes on SPSS syntax for Chapter 2 -- 2.7 Bibliography and further reading -- 3 Confidence intervals -- 3.1 Chapter overview -- 3.2 From point estimates to interval estimates -- 3.3 Confidence intervals -- 3.4 Confidence intervals for a difference -- 3.5 Using Monte Carlo methods to estimate confidence intervals -- 3.6 Graphing confidence intervals -- 3.7 R code for Chapter 3 -- 3.8 Notes on SPSS syntax for Chapter 3 -- 3.9 Bibliography and further reading -- 4 Significance tests -- 4.1 Chapter overview -- 4.2 From confidence intervals to significance tests -- 4.3 Null hypothesis significance tests -- 4.4 t tests -- 4.5 Tests for discrete data -- 4.6 Inference about other parameters -- 4.7 Good practice in the application of significance testing -- 4.8 R code for Chapter 4 -- 4.9 Notes on SPSS syntax for Chapter 4 -- 4.10 Bibliography and further reading -- 5 Regression -- 5.1 Chapter overview -- 5.2 Regression models, prediction and explanation -- 5.3 Mathematics of the linear function -- 5.4 Simple linear regression -- 5.5 Statistical inference in regression -- 5.6 Fitting and interpreting regression models -- 5.7 Fitting curvilinear relationships with simple linear regression -- 5.8 R code for Chapter 5.