Choice processes appear in all spheres of society. Hitherto ruling paradigms in the modelling of choice problems have presumed a competitive general equi librium which, however, proves insufficient for dynamic processes. This contribution aims at providing a general coherent and closed frame work for the dynamic modelling of decision processes. It was one of my main interests to build a bridge between the pure model building concepts and their practical applications. Therefore all given examples are related to empirical work. Solution algorithms for the estimation of trend parameters as well as the numerical simulation in concrete applications therefore playa central role in this contribution. Friendly relations with a number of colleagues from many universities in Europe, and the U.S. have emerged during the different applications. I wish to thank all of them. The international cooperations were mainly initiated and supported by conferences and workshops organized and financed by the International Institute for Applied Systems Analysis (lIASA), the Istituto Ricerche Economico-Sociali Del Piemonte (I RES). the Institut National D 'Etudes De'mographiques (I NED), the Centre for Regional Science Research UmeJ. (CERUM) and the Projets de Cooperation et D'Echange avec France (Procop>' Special thanks go to the Volkswagen Stiftung for financial support of this work over the years. Thanks also go in particular to my friend and mentor Prof.W.Weidlich for his encouragement and for the many suggestions he made in fruitful discus sions and common work that have taken place over the years.
Show moreChoice processes appear in all spheres of society. Hitherto ruling paradigms in the modelling of choice problems have presumed a competitive general equi librium which, however, proves insufficient for dynamic processes. This contribution aims at providing a general coherent and closed frame work for the dynamic modelling of decision processes. It was one of my main interests to build a bridge between the pure model building concepts and their practical applications. Therefore all given examples are related to empirical work. Solution algorithms for the estimation of trend parameters as well as the numerical simulation in concrete applications therefore playa central role in this contribution. Friendly relations with a number of colleagues from many universities in Europe, and the U.S. have emerged during the different applications. I wish to thank all of them. The international cooperations were mainly initiated and supported by conferences and workshops organized and financed by the International Institute for Applied Systems Analysis (lIASA), the Istituto Ricerche Economico-Sociali Del Piemonte (I RES). the Institut National D 'Etudes De'mographiques (I NED), the Centre for Regional Science Research UmeJ. (CERUM) and the Projets de Cooperation et D'Echange avec France (Procop>' Special thanks go to the Volkswagen Stiftung for financial support of this work over the years. Thanks also go in particular to my friend and mentor Prof.W.Weidlich for his encouragement and for the many suggestions he made in fruitful discus sions and common work that have taken place over the years.
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1. Introduction.- 2. A Dynamic Theory of Decision Processes.- 2.1 The Panel Data-Based Discrete Choice Approach.- 2.2 The Master Equation View in Dynamic Choice Processes.- 2.3 The Decision Process.- 2.4 The Equations of Motion.- 2.5 Parameter Estimation.- 2.6 Selection Criteria for the Examples.- 3. Shocks in Urban Evolution.- 3.1 Introduction.- 3.2 A Stochastic Model on Shocks in Urban Evolution.- 4. Intra — Urban Migration.- 4.1 Introduction.- 4.2 A Stochastic Model on Intra-Urban Dynamics.- 5. Inter-Regional Migration.- 5.1 Introduction.- 5.2 The Stochastic Migration Model.- 5.3 Comparative Analysis of Inter-Regional Migration.- 6. Chaotic Evolution of Migratory Systems.- 6.1 Introduction.- 6.2 The Migratory Master Equation and Mean Value Equations for Interacting Populations.- 6.3 Chaotic Behaviour of Migratory Trajectories.- 6.4 Conclusion.- 7. Spatial Interaction Models and their Micro-Foundation.- 7.1 Introduction to Spatial Urban Theory.- 7.2 A Service System as the Basis of the Model.- 7.3 A Master Equation Approach.- 7.4 The Quasi-Deterministic Equations to the Dynamic Service Sector Model.- 7.5 The Stationary Solution of the Service Sector Model.- 7.6 Dynamic Simulations and their Interpretation.- 7.7 Concluding Comments.- 8. Further Applications and Extensions.- 8.1 Knowledge, Innovation, Productivity.- 8.2 Economic Cycles.- 8.3 Housing and Labour Market.- 8.4 Concluding Remarks.- 9. Appendix: The Master Equation.- 9.1 Deterministic and Probabilistic Description of Systems.- 9.2 Some General Concepts of Probability Theory.- 9.3 The Derivation of the Master Equation.- 9.4 The Stationary Solution of the Master Equation for Detailed Balance.- 9.5 The Stationary Solution of the Master Equation of Chapter 4.- 9.6 The Stationary Solution of the Master Equationof Chapter 5.- 9.7 The Embedding of Random Utility Theory.- 9.8 The Construction of Configurational Transition Rates via Panel Data.- References.
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