The cooperation and contamination between mathematicians, statisticians and econometricians working in actuarial sciences and finance is improving the research on these topics and producing numerous meaningful scientific results. This volume presents new ideas, in the form of four- to six-page papers, presented at the International Conference eMAF2020 - Mathematical and Statistical Methods for Actuarial Sciences and Finance. Due to the now sadly famous COVID-19 pandemic, the conference was held remotely through the Zoom platform offered by the Department of Economics of the Ca' Foscari University of Venice on September 18, 22 and 25, 2020. eMAF2020 is the ninth edition of an international biennial series of scientific meetings, started in 2004 at the initiative of the Department of Economics and Statistics of the University of Salerno. The effectiveness of this idea has been proven by wide participation in all editions, which have been held in Salerno (2004, 2006, 2010 and 2014), Venice (2008, 2012 and 2020), Paris (2016) and Madrid (2018). This book covers a wide variety of subjects: artificial intelligence and machine learning in finance and insurance, behavioral finance, credit risk methods and models, dynamic optimization in finance, financial data analytics, forecasting dynamics of actuarial and financial phenomena, foreign exchange markets, insurance models, interest rate models, longevity risk, models and methods for financial time series analysis, multivariate techniques for financial markets analysis, pension systems, portfolio selection and management, real-world finance, risk analysis and management, trading systems, and others. This volume is a valuable resource for academics, PhD students, practitioners, professionals and researchers. Moreover, it is also of interest to other readers with quantitative background knowledge.

The interaction between mathematicians and statisticians has been shown to be an effective approach for dealing with actuarial, insurance and financial problems, both from an academic perspective and from an operative one. The collection of original papers presented in this volume pursues precisely this purpose. It covers a wide variety of subjects in actuarial, insurance and finance fields, all treated in the light of the successful cooperation between the above two quantitative approaches. The papers published in this volume present theoretical and methodological contributions and their applications to real contexts. With respect to the theoretical and methodological contributions, some of the considered areas of investigation are: actuarial models; alternative testing approaches; behavioral finance; clustering techniques; coherent and non-coherent risk measures; credit scoring approaches; data envelopment analysis; dynamic stochastic programming; financial contagion models; financial ratios; intelligent financial trading systems; mixture normality approaches; Monte Carlo-based methods; multicriteria methods; nonlinear parameter estimation techniques; nonlinear threshold models; particle swarm optimization; performance measures; portfolio optimization; pricing methods for structured and non-structured derivatives; risk management; skewed distribution analysis; solvency analysis; stochastic actuarial valuation methods; variable selection models; time series analysis tools. As regards the applications, they are related to real problems associated, among the others, to: banks; collateralized fund obligations; credit portfolios; defined benefit pension plans; double-indexed pension annuities; efficient-market hypothesis; exchange markets; financial time series; firms; hedge funds; non-life insurance companies; returns distributions; socially responsible mutual funds; unit-linked contracts. This book is aimed at academics, Ph.D. students, practitioners, professionals and researchers. But it will also be of interest to readers with some quantitative background knowledge.

In the last two decades modal logic has undergone an explosive growth, to thepointthatacompletebibliographyofthisbranchoflogic, supposingthat someone were capable to compile it, would ?ll itself a ponderous volume. What is impressive in the growth of modal logic has not been so much the quick accumulation of results but the richness of its thematic dev- opments. In the 1960s, when Kripke semantics gave new credibility to the logic of modalities? which was already known and appreciated in the Ancient and Medieval times? no one could have foreseen that in a short time modal logic would become a lively source of ideas and methods for analytical philosophers, historians of philosophy, linguists, epistemologists and computer scientists. The aim which oriented the composition of this book was not to write a new manual of modal logic (there are a lot of excellent textbooks on the market, and the expert reader will realize how much we bene?ted from manyofthem)buttoo?ertoeveryreader, evenwithnospeci?cbackground in logic, a conceptually linear path in the labyrinth of the current panorama of modal logic. The notion which in our opinion looked suitable to work as a compass in this enterprise was the notion of multimodality, or, more speci?cally, the basic idea of grounding systems on languages admitting more than one primitive modal opera

This volume is a collection of selected papers that were presented at the international conference Model-Based Reasoning in Science and Technology. Abduction, Logic, and Computational Discovery (MBR09 BRAZIL), held at the University of Campinas, Campinas, Brazil in December 2009. A previous volume, Model-Based Reasoning in Scienti?c Discovery, edited by L. Magnani, N.J. Nersessian, and P. Thagard (Kluwer Academic/Plenum Publishers, New York, 1999; Chinese edition, China Science and Techn- ogy Press, Beijing, 2000), was based on the papers presented at the ?rst "model-based reasoning"international conference, held at the University of Pavia, Pavia, Italy in December 1998. Other two volumes were based on the papers presented at the second "model-based reasoning" international c- ference, held at the same place in May 2001: Model-Based Reasoning. Sci- ti?c Discovery, Technological Innovation, Values, edited by L. Magnani and N.J. Nersessian(Kluwer Academic/PlenumPublishers, New York, 2002)and Logical and Computational Aspects of Model-Based Reasoning, editedbyL. Magnani, N.J. Nersessian, and C. Pizzi(KluwerAcademic, Dordrecht,2002). Another volume Model-Based Reasoning in Science and Engineering, edited byL.Magnani(CollegePublications, London,2006), wasbasedonthepapers presentedat the third"model-basedreasoning"internationalconference, held at the same place in December 2004. Finally, volume Model-Based Reasoning inScienceandMedicine, editedbyL.MagnaniandL.Ping(Springer, Heid- berg/Berlin 2006), was based on the papers presented at the fourth"mod- based"reasoning conference, held at Sun Yat-sen University, Guangzhou, P. R. China.

Enriching understanding of the current theoretical debate on project-based learning and R&D sourcing, " Project-based Knowledge in Organizing Open Innovation "draws on innovation literature and knowledge-based perspectives to solve open problems in the relationship between knowledge development at project level and how firms organize product innovation combining in-house R&D activities with inbound open innovation.

Through field research in different industrial settings (pharmaceutical, automotive and machine tools) and with complementary methodological approaches, this book provides empirical evidence on how project knowledge features affect sourcing decisions at firm level.

Due to the emerging interest in the management literature on project-based organizations and on the relevance of project forms of organizing in a knowledge-based economy, this volume will appeal to scholars and students in business and management, in particular those in innovation management, organization theory and strategic management.

Addressing the still open issue of how the firm level should be complemented by studies at the project level of analysis, this book provides theoretical and empirical arguments on the advantages of a more fine-grained level of analysis to understand how firms organize their innovation processes across boundaries."

This volume is based on the papers that were presented at the International Conference Model-Based Reasoning: Scientific Discovery, Technological Innovation, Values' (MBR'01), held at the Collegio Ghislieri, University of Pavia, Pavia, Italy, in May 2001. The previous volume Model-Based Reasoning in Scientific Discovery, edited by L. Magnani, N.J. Nersessian, and P. Thagard (Kluwer Academic/Plenum Publishers, New York, 1999; Chinese edition, China Science and Technology Press, Beijing, 2000), was based on the papers presented at the first model-based reasoning' international conference, held at the same venue in December 1998.

The presentations given at the Conference explore how scientific thinking uses models and exploratory reasoning to produce creative changes in theories and concepts. Some address the problem of model-based reasoning in ethics, especially pertaining to science and technology, and stress some aspects of model-based reasoning in technological innovation.

The study of diagnostic, visual, spatial, analogical, and temporal reasoning has demonstrated that there are many ways of performing intelligent and creative reasoning that cannot be described with the help only of traditional notions of reasoning such as classical logic. Understanding the contribution of modeling practices to discovery and conceptual change in science requires expanding scientific reasoning to include complex forms of creative reasoning that are not always successful and can lead to incorrect solutions. The study of these heuristic ways of reasoning is situated at the crossroads of philosophy, artificial intelligence, cognitive psychology, and logic; that is, at the heart of cognitivescience.

There are several key ingredients common to the various forms of model-based reasoning. The term model' comprises both internal and external representations. The models are intended as interpretations of target physical systems, processes, phenomena, or situations. The models are retrieved or constructed on the basis of potentially satisfying salient constraints of the target domain. Moreover, in the modeling process, various forms of abstraction are used. Evaluation and adaptation take place in light of structural, causal, and/or functional constraints. Model simulation can be used to produce new states and enable evaluation of behaviors and other factors.

The various contributions of the book are written by interdisciplinary researchers who are active in the area of creative reasoning in science and technology, and are logically and computationally oriented: the most recent results and achievements about the topics above are illustrated in detail in the papers.

The cooperation and contamination among mathematicians, statisticians and econometricians working in actuarial sciences and finance are improving the research on these topics and producing numerous meaningful scientific results. This volume presents new ideas in the form of four- to six-page papers presented at the International Conference MAF2022 - Mathematical and Statistical Methods for Actuarial Sciences and Finance. Due to the COVID-19 pandemic, the conference, to which this book is related, was organized in a hybrid form by the Department of Economics and Statistics of the University of Salerno, with the partnership of the Department of Economics of Ca Foscari University of Venice, and was held from 20 to 22 April 2022 in Salerno (Italy) MAF2022 is the tenth edition of an international biennial series of scientific meetings, started in 2004 on the initiative of the Department of Economics and Statistics of the University of Salerno. It has established itself internationally with gradual and continuous growth and scientific enrichment. The effectiveness of this idea has been proven by the wide participation in all the editions, which have been held in Salerno (2004, 2006, 2010, 2014, 2022), Venice (2008, 2012 and 2020 online), Paris (2016) and Madrid (2018). This book covers a wide variety of subjects: artificial intelligence and machine learning in finance and insurance, behavioural finance, credit risk methods and models, dynamic optimization in finance, financial data analytics, forecasting dynamics of actuarial and financial phenomena, foreign exchange markets, insurance models, interest rate models, longevity risk, models and methods for financial time series analysis, multivariate techniques for financial markets analysis, pension systems, portfolio selection and management, real-world finance, risk analysis and management, trading systems, and others. This volume is a valuable resource for academics, PhD students, practitioners, professionals and researchers. Moreover, it is also of interest to other readers with quantitative background knowledge.

Enriching understanding of the current theoretical debate on project-based learning and R&D sourcing, ‘Project-based Knowledge in Organizing Open Innovation’ draws on innovation literature and knowledge-based perspectives to solve open problems in the relationship between knowledge development at project level and how firms organize product innovation combining in-house R&D activities with inbound open innovation. Through field research in different industrial settings (pharmaceutical, automotive and machine tools) and with complementary methodological approaches, this book provides empirical evidence on how project knowledge features affect sourcing decisions at firm level. Due to the emerging interest in the management literature on project-based organizations and on the relevance of project forms of organizing in a knowledge-based economy, this volume will appeal to scholars and students in business and management, in particular those in innovation management, organization theory and strategic management. Addressing the still open issue of how the firm level should be complemented by studies at the project level of analysis, this book provides theoretical and empirical arguments on the advantages of a more fine-grained level of analysis to understand how firms organize their innovation processes across boundaries.

The interaction between mathematicians and statisticians has been shown to be an effective approach for dealing with actuarial, insurance and financial problems, both from an academic perspective and from an operative one. The collection of original papers presented in this volume pursues precisely this purpose. It covers a wide variety of subjects in actuarial, insurance and finance fields, all treated in the light of the successful cooperation between the above two quantitative approaches. The papers published in this volume present theoretical and methodological contributions and their applications to real contexts. With respect to the theoretical and methodological contributions, some of the considered areas of investigation are: actuarial models; alternative testing approaches; behavioral finance; clustering techniques; coherent and non-coherent risk measures; credit scoring approaches; data envelopment analysis; dynamic stochastic programming; financial contagion models; financial ratios; intelligent financial trading systems; mixture normality approaches; Monte Carlo-based methods; multicriteria methods; nonlinear parameter estimation techniques; nonlinear threshold models; particle swarm optimization; performance measures; portfolio optimization; pricing methods for structured and non-structured derivatives; risk management; skewed distribution analysis; solvency analysis; stochastic actuarial valuation methods; variable selection models; time series analysis tools. As regards the applications, they are related to real problems associated, among the others, to: banks; collateralized fund obligations; credit portfolios; defined benefit pension plans; double-indexed pension annuities; efficient-market hypothesis; exchange markets; financial time series; firms; hedge funds; non-life insurance companies; returns distributions; socially responsible mutual funds; unit-linked contracts. This book is aimed at academics, Ph.D. students, practitioners, professionals and researchers. But it will also be of interest to readers with some quantitative background knowledge.

This volume is a collection of selected papers that were presented at the international conference Model-Based Reasoning in Science and Technology. Abduction, Logic, and Computational Discovery (MBR09 BRAZIL), held at the University of Campinas, Campinas, Brazil in December 2009. A previous volume, Model-Based Reasoning in Scienti?c Discovery, edited by L. Magnani, N.J. Nersessian, and P. Thagard (Kluwer Academic/Plenum Publishers, New York, 1999; Chinese edition, China Science and Techn- ogy Press, Beijing, 2000), was based on the papers presented at the ?rst "model-based reasoning"international conference, held at the University of Pavia, Pavia, Italy in December 1998. Other two volumes were based on the papers presented at the second "model-based reasoning" international c- ference, held at the same place in May 2001: Model-Based Reasoning. Sci- ti?c Discovery, Technological Innovation, Values, edited by L. Magnani and N.J. Nersessian(Kluwer Academic/PlenumPublishers, New York, 2002)and Logical and Computational Aspects of Model-Based Reasoning, editedbyL. Magnani, N.J. Nersessian, and C. Pizzi(KluwerAcademic, Dordrecht,2002). Another volume Model-Based Reasoning in Science and Engineering, edited byL.Magnani(CollegePublications, London,2006), wasbasedonthepapers presentedat the third"model-basedreasoning"internationalconference, held at the same place in December 2004. Finally, volume Model-Based Reasoning inScienceandMedicine, editedbyL.MagnaniandL.Ping(Springer, Heid- berg/Berlin 2006), was based on the papers presented at the fourth"mod- based"reasoning conference, held at Sun Yat-sen University, Guangzhou, P. R. China.

In the last two decades modal logic has undergone an explosive growth, to thepointthatacompletebibliographyofthisbranchoflogic, supposingthat someone were capable to compile it, would ?ll itself a ponderous volume. What is impressive in the growth of modal logic has not been so much the quick accumulation of results but the richness of its thematic dev- opments. In the 1960s, when Kripke semantics gave new credibility to the logic of modalities? which was already known and appreciated in the Ancient and Medieval times? no one could have foreseen that in a short time modal logic would become a lively source of ideas and methods for analytical philosophers, historians of philosophy, linguists, epistemologists and computer scientists. The aim which oriented the composition of this book was not to write a new manual of modal logic (there are a lot of excellent textbooks on the market, and the expert reader will realize how much we bene?ted from manyofthem)buttoo?ertoeveryreader, evenwithnospeci?cbackground in logic, a conceptually linear path in the labyrinth of the current panorama of modal logic. The notion which in our opinion looked suitable to work as a compass in this enterprise was the notion of multimodality, or, more speci?cally, the basic idea of grounding systems on languages admitting more than one primitive modal opera

In the last two decades modal logic has undergone an explosive growth, to thepointthatacompletebibliographyofthisbranchoflogic, supposingthat someone were capable to compile it, would ?ll itself a ponderous volume. What is impressive in the growth of modal logic has not been so much the quick accumulation of results but the richness of its thematic dev- opments. In the 1960s, when Kripke semantics gave new credibility to the logic of modalities? which was already known and appreciated in the Ancient and Medieval times? no one could have foreseen that in a short time modal logic would become a lively source of ideas and methods for analytical philosophers, historians of philosophy, linguists, epistemologists and computer scientists. The aim which oriented the composition of this book was not to write a new manual of modal logic (there are a lot of excellent textbooks on the market, and the expert reader will realize how much we bene?ted from manyofthem)buttoo?ertoeveryreader, evenwithnospeci?cbackground in logic, a conceptually linear path in the labyrinth of the current panorama of modal logic. The notion which in our opinion looked suitable to work as a compass in this enterprise was the notion of multimodality, or, more speci?cally, the basic idea of grounding systems on languages admitting more than one primitive modal opera

This volume is based on the papers that were presented at the International Conference "Model-Based Reasoning: Scientific Discovery, Technological Innovation, Values' (MBR'01), held at the Collegio Ghislieri, University of Pavia, Pavia, Italy, in May 2001. The previous volume Model-Based Reasoning in Scientific Discovery, edited by L. Magnani, N.J. Nersessian, and P. Thagard was based on the papers presented at the first" model-based reasoning' international conference, held at the same venue in December 1998.

The presentations given at the Conference explore how scientific thinking uses models and exploratory reasoning to produce creative changes in theories and concepts. Some address the problem of model-based reasoning in ethics, especially pertaining to science and technology, and stress some aspects of model-based reasoning in technological innovation.

The study of diagnostic, visual, spatial, analogical, and temporal reasoning has demonstrated that there are many ways of performing intelligent and creative reasoning that cannot be described with the help only of traditional notions of reasoning such as classical logic. Understanding the contribution of modeling practices to discovery and conceptual change in science requires expanding scientific reasoning to include complex forms of creative reasoning that are not always successful and can lead to incorrect solutions. The study of these heuristic ways of reasoning is situated at the crossroads of philosophy, artificial intelligence, cognitive psychology, and logic; that is, at the heart of cognitive science.

There are several key ingredients common to the various forms of model-based reasoning. The term model' comprises both internal and external representations. The models are intended as interpretations of target physical systems, processes, phenomena, or situations. The models are retrieved or constructed on the basis of potentially satisfying salient constraints of the target domain. Moreover, in the modeling process, various forms of abstraction are used. Evaluation and adaptation take place in light of structural, causal, and/or functional constraints. Model simulation can be used to produce new states and enable evaluation of behaviors and other factors.

The various contributions of the book are written by interdisciplinary researchers who are active in the area of creative reasoning in science and technology, and are logically and computationally oriented: the most recent results and achievements about the topics above are illustrated in detail in the papers.

The cooperation and contamination among mathematicians, statisticians and econometricians working in actuarial sciences and finance are improving the research on these topics and producing numerous meaningful scientific results. This volume presents new ideas in the form of four- to six-page papers presented at the International Conference MAF2022 – Mathematical and Statistical Methods for Actuarial Sciences and Finance. Due to the COVID-19 pandemic, the conference, to which this book is related, was organized in a hybrid form by the Department of Economics and Statistics of the University of Salerno, with the partnership of the Department of Economics of Cà Foscari University of Venice, and was held from 20 to 22 April 2022 in Salerno (Italy)  MAF2022 is the tenth edition of an international biennial series of scientific meetings, started in 2004 on the initiative of the Department of Economics and Statistics of the University of Salerno. It has established itself internationally with gradual and continuous growth and scientific enrichment. The effectiveness of this idea has been proven by the wide participation in all the editions, which have been held in Salerno (2004, 2006, 2010, 2014, 2022), Venice (2008, 2012 and 2020 online), Paris (2016) and Madrid (2018). This book covers a wide variety of subjects: artificial intelligence and machine learning in finance and insurance, behavioural finance, credit risk methods and models, dynamic optimization in finance, financial data analytics, forecasting dynamics of actuarial and financial phenomena, foreign exchange markets, insurance models, interest rate models, longevity risk, models and methods for financial time series analysis, multivariate techniques for financial markets analysis, pension systems, portfolio selection and management, real-world finance, risk analysis and management, trading systems, and others. This volume is a valuable resource for academics, PhD students, practitioners, professionals and researchers. Moreover, it is also of interest to other readers with quantitative background knowledge.

The cooperation and contamination between mathematicians, statisticians and econometricians working in actuarial sciences and finance is improving the research on these topics and producing numerous meaningful scientific results. This volume presents new ideas, in the form of four- to six-page papers, presented at the International Conference eMAF2020 - Mathematical and Statistical Methods for Actuarial Sciences and Finance. Due to the now sadly famous COVID-19 pandemic, the conference was held remotely through the Zoom platform offered by the Department of Economics of the Ca' Foscari University of Venice on September 18, 22 and 25, 2020. eMAF2020 is the ninth edition of an international biennial series of scientific meetings, started in 2004 at the initiative of the Department of Economics and Statistics of the University of Salerno. The effectiveness of this idea has been proven by wide participation in all editions, which have been held in Salerno (2004, 2006, 2010 and 2014), Venice (2008, 2012 and 2020), Paris (2016) and Madrid (2018). This book covers a wide variety of subjects: artificial intelligence and machine learning in finance and insurance, behavioral finance, credit risk methods and models, dynamic optimization in finance, financial data analytics, forecasting dynamics of actuarial and financial phenomena, foreign exchange markets, insurance models, interest rate models, longevity risk, models and methods for financial time series analysis, multivariate techniques for financial markets analysis, pension systems, portfolio selection and management, real-world finance, risk analysis and management, trading systems, and others. This volume is a valuable resource for academics, PhD students, practitioners, professionals and researchers. Moreover, it is also of interest to other readers with quantitative background knowledge.