H.W. Gottinger – författare

7. 2 Cumulative Pollutants and the Rate of Economic Growth 217 7. 3 Economic Growth in an Unregulated Economy 223 7. 4 Economic Growth in a Degrading Economy 231 7. 5 Economic Growth in the Spaceship Economy 237 7. 6 Dynamic Equilibrium in the Spaceship Economy 248 7. 7 An Evaluation of the Model and some Conclusions 254 7. 8 Some Long-Term Implications of the Model 256 References 258 Chapter 8: Optimal Economic Growth when CO Constraints are Critical 2 8. 1 Introduction 259 8. 2 Formulation of the Problem 260 8. 3 Derivation of the Necessary Conditions 264 8. 4 Analysis of the Necessary Conditions 265 8. 5 Conclusion 277 References 279 Chapter 9: Uncertainty. Value of Information and Greenhouse Gas Emissions 9. 1 Introduction 280 9. 2 An Illustrative Example - Evaluating a Climate Research and Monitoring Programme 282 293 9. 3 The Value of Information in a Stochastic Dynamic Programme 301 9. 4 Optimal Policies in a Stochastic Dynamic Programme 307 9. 5 Resource Costs and Critical Probabilities 9. 6 Conclusions 314 317 References 319 Epilogue 325 Index viii Preface The movement of carbon from sources to final disposition is known as the carbon cycle. The largest reservoir of carbon is in carbonate sediments such as limestone and chalk. Other significant but less stable reservoirs include fossil fuels, living and dead plants and animals, carbonates and bicarbonates dissolved in the ocean.

7. 2 Cumulative Pollutants and the Rate of Economic Growth 217 7. 3 Economic Growth in an Unregulated Economy 223 7. 4 Economic Growth in a Degrading Economy 231 7. 5 Economic Growth in the Spaceship Economy 237 7. 6 Dynamic Equilibrium in the Spaceship Economy 248 7. 7 An Evaluation of the Model and some Conclusions 254 7. 8 Some Long-Term Implications of the Model 256 References 258 Chapter 8: Optimal Economic Growth when CO Constraints are Critical 2 8. 1 Introduction 259 8. 2 Formulation of the Problem 260 8. 3 Derivation of the Necessary Conditions 264 8. 4 Analysis of the Necessary Conditions 265 8. 5 Conclusion 277 References 279 Chapter 9: Uncertainty. Value of Information and Greenhouse Gas Emissions 9. 1 Introduction 280 9. 2 An Illustrative Example - Evaluating a Climate Research and Monitoring Programme 282 293 9. 3 The Value of Information in a Stochastic Dynamic Programme 301 9. 4 Optimal Policies in a Stochastic Dynamic Programme 307 9. 5 Resource Costs and Critical Probabilities 9. 6 Conclusions 314 317 References 319 Epilogue 325 Index viii Preface The movement of carbon from sources to final disposition is known as the carbon cycle. The largest reservoir of carbon is in carbonate sediments such as limestone and chalk. Other significant but less stable reservoirs include fossil fuels, living and dead plants and animals, carbonates and bicarbonates dissolved in the ocean.

Ethics, as one of the most respectable disciplines of philosophy, has undergone a drastic and revolutionary change in recent time. There are three main trends of this development. The first trend can be described as a tendency towards a rigorous formal and analytical language. This means simply that ethics has created beside its own formalized set­ theoretical language a variety of new formalized, logical and mathemati­ cal methods and concepts. Thus ethics has become a formalized meta­ or epidiscipline which is going to replace the traditional concepts, principles and ethical methods in the realm of social sciences. It is clear that a formalized form of ethics can be used more easily in social, economic and political theories if there are ethical conflicts to be solved. This first trend can be regarded as a conditio sine qua non for application in, and imposing ethical solutions on, social scientific theories. The second trend may be characterized as an association- or unification-tendency of a formalized and analytical ethics with decision theory. Decision theory as a new interdiscipline of social sciences is actually an assemblage of a variety of subtheories such as value-utility theory, game theory, collective decision theory, etc. Harsanyi has called this complex of subtheories a general theory of human behavior. Analytical or formal ethics is actually using this general theory of human behavior as a vehicle simply because this theory deals from the beginning with conflict solution, i. e.

In this book I develop a theory of complexity for economics and manage­ ment sciences. This book is addressed to the mathematically or analytically oriented economist, psychologist or management scientist. It could also be of interest to engineers, computer scientists, biologists, physicists and ecologists who have a constant desire to go beyond the bounds of their respective disciplines. The unifying theme is: we live in a complex world, but how can we cope with complexity? If the book has made the reader curious, and if he looks at modelling, problem recognition and problem solving within his field of competence in a more "complex" way, it will have achieved its goal. The starting point is the recognition that complexity is a well-defined concept in mathematics (e.g. in topological dynamics), computer science, information theory and artificial intelligence. But it is a rather diffuse concept in other fields, sometimes it has only descriptive value or even worse, it is only used in a colloquial sense. The systematic investigation of complexity phenomena has reached a mature status within computer science. Indices of computer size, capacity and performance root ultimately in John von Neumann''s paradigmatic model of a machine, though other 1 roots point to McCulloch and Pitts, not to forget Alan Turing. Offsprings of this development include: -complexity of formal systems and recursiveness; -cellular automata and the theory of self-reproducing machines; -theory of program or computational complexity; -theory of sequential machines; -problem solving, cognitive science, pattern recognition and decision processes.

In this book I develop a theory of complexity for economics and manage­ ment sciences. This book is addressed to the mathematically or analytically oriented economist, psychologist or management scientist. It could also be of interest to engineers, computer scientists, biologists, physicists and ecologists who have a constant desire to go beyond the bounds of their respective disciplines. The unifying theme is: we live in a complex world, but how can we cope with complexity? If the book has made the reader curious, and if he looks at modelling, problem recognition and problem solving within his field of competence in a more "complex" way, it will have achieved its goal. The starting point is the recognition that complexity is a well-defined concept in mathematics (e.g. in topological dynamics), computer science, information theory and artificial intelligence. But it is a rather diffuse concept in other fields, sometimes it has only descriptive value or even worse, it is only used in a colloquial sense. The systematic investigation of complexity phenomena has reached a mature status within computer science. Indices of computer size, capacity and performance root ultimately in John von Neumann's paradigmatic model of a machine, though other 1 roots point to McCulloch and Pitts, not to forget Alan Turing. Offsprings of this development include: -complexity of formal systems and recursiveness; -cellular automata and the theory of self-reproducing machines; -theory of program or computational complexity; -theory of sequential machines; -problem solving, cognitive science, pattern recognition and decision processes.