Economic evolution and demographic change : formal models in social sciences

I Formal Models in Economics.- 1 A chaotic process with slow feed back: The case of business cycles.- 1.1 A first model.- 1.1.1 Investments.- 1.1.2 Consumption.- 1.2 The cubic iterative map.- 1.2.1 Fixed points, cycles and chaos.- 1.2.2 Formal analysis of chaotic dynamics.- 1.2.3 Symbolic dynamics.- 1.3 "Brownian random walk".- 1.4 Digression on order and disorder.- 1.5 The general model.- 1.5.1 Relaxation cycles.- 1.5.2 Other cycles.- 1.5.3 The Slow Feed Back.- 1.6 Conclusion.- 2 Nonlinear Interactions in the Economy.- 2.1 Introduction.- 2.2 The Long Wave Model.- 2.3 Mode-Locking and Chaos.- 2.4 Conclusion.- 3 Fast and Slow Processes of Economic Evolution.- 3.1 Introduction and Background.- 3.2 The Problems of Economic Development Theory.- 3.3 Synergetic Development Economics - Some Basic Concepts.- 3.4 The Arena.- 3.5 Rules of the Game.- 3.6 Networks.- 3.7 Knowledge As Networks and Knowledge On Networks.- 3.8 Communication and Creativity - some Historical Evidence.- 3.9 Creativity and Communications - Econometric Results.- 3.10 The Inverted Explanation.- 3.11 The Destruction of the Industrial Society.- 3.12 The New Economic Structure.- 4 A stochastic model of technological evolution.- 4.1 Introduction.- 4.2 A Substitution Model.- 4.3 Application of a general evolutionary model to technological change.- 4.4 Discussion.- 5 Evolution of Production Processes.- 5.1 Introduction.- 5.2 Basic Assumptions.- 5.3 Formalization.- 5.4 Chernenko's Results.- 5.5 An alternative macro model.- 5.6 Simulation results.- 5.7 Modeling evolution on the individual level.- 5.7.1 Simulation run with total extinction.- 5.7.2 Simulation run without extinction.- 5.8 Conclusions.- 6 Innovation Diffusion through Schumpeterian Competition.- 6.1 Introduction: From "Homo Economicus" to "Homo Socialis": Innovation diffusion as a collective socio-ecological dynamic choice process.- 6.2 Analytical basis of Schumpeterian Competition: Collective choice and relative socio-spatial dynamics.- 6.3 Explicit analytical presentation of the innovation diffusion dynamics: Dynamic choice models.- 6.4 Intervention of an active environment: Generation of innovation adoption niches.- 6.5 Temporal innovation diffusion process.- 6.5.1 Qualitative analysis of the Schumpeter competition cycles for Clusters of competitive innovations.- 6.5.2 Variational principle of meso-level collective choice behaviour.- 6.6 Concluding Remark.- 7 Nonlinear Threshold Dynamics: Further Examples for Chaos in Social Sciences.- 7.1 Introduction.- 7.2 A Short Course into Chaos.- 7.3 How Addictive Behaviour and Threshold Adjustment May Imply Chaos.- 7.4 How Asymmetric Investment Behaviour of Two Competing Firms Generates Chaos.- 7.5 Concluding Remarks.- II Formal Models in Geography.- 8 Geography Physics and Synergetics.- 8.1 Introduction.- 8.2 Models of geographical interactions.- 8.2.1 Polarization and gravitation.- 8.2.2 Reformulations of the gravity model.- 8.2.3 The entropy maximizing approach.- 8.2.4 About men and particles.- 8.3 Models of geographical structures.- 8.3.1 The relativity of geographical space.- 8.3.2 Fractality of geographical space.- 8.3.3 Space-time convergence.- 8.3.4 The example of urban hierarchies.- 8.3.5 Processes and geographical forms.- 8.4 Conclusion.- 9 Chaotic Behaviour in Spatial Systems and Forecasting.- 9.1 Introduction.- 9.2 An Example for Chaotic Evolution: Migratory Systems.- 9.2.1 A Numerical Simulation.- 9.3 Estimation of Trend Parameters.- 9.4 The Estimation Procedure.- 9.5 Forecasting for Systems with Chaotic Evolution.- 9.5.1 Step I: Confidence Limits on Model Parameters by Monte Carlo Estimation.- 9.5.2 Step II: Monte Carlo Simulation of Systems Trajectories.- 10 Model Identification for Estimating Missing Values in Space-Time Data Series: Monthly Inflation in the US Urban System, 1977-1990.- 10.1 Introduction.- 10.2 Background.- 10.3 Update of individual urban area ARIMA models.- 10.4 Jackknife results for New York and Los Angeles.- 10.5 Transfer function interpolation.- 10.6 Implications.- 11 Explanation of Residential Segregation in one City. The Case of Cologne.- 11.1 Introduction.- 11.2 Measuring Segregation.- 11.3 Data.- 11.4 The Index of Inequality.- 11.5 Solutions.- 11.6 Statistical Explanation.- 11.7 Discussion.- 12 Determinants of Remigrant Behavior: An Application of the Grouped Cox Model.- 12.1 Introduction.- 12.2 Migrants in Germany.- 12.3 Foundations of the Survival Analysis.- 12.4 The Grouped Cox Model.- 12.5 Results.- 12.5.1 Estimations with the Total Sample.- 12.5.2 Estimations with a Subsample (20% CensoredData).- 12.6 Conclusion.- III Formal Models in Demography.- 13 Birth Control as a Social Dilemma.- 13.1 Introduction.- 13.1.1 Purpose.- 13.2 Method.- 13.3 Results.- 13.4 Discussion.- 14 Sex-Ratio, divorce, and labor force participation - An analysis of international aggregate data.- 14.1 Introduction.- 14.2 Data and measurement of variables.- 14.3 Results.- 14.4 Conclusion.- 15 Some Aspects of Competing Risks in Demography.- 15.1 Introduction.- 15.2 The Latent Failure Model.- 15.3 The Problem of Nonidentifiability.- 15.3.1 Inclusion of covariates (regressors).- 15.3.2 Bounds on net probabilities.- 15.3.3 Functional form assumptions about S.- 15.3.4 The postulate of independence.- 15.4 A Discrete-Time Model of Risk Elimination.- 15.5 Example.- 15.6 Conclusions.- 16 Dynamic Structural Equations in Discrete and Continuous Time.- 16.1 Introduction.- 16.2 Dynamic State Space Models.- 16.3 Maximum Likelihood Parameter Estimation with Continuous Measurements.- 16.4 Maximum Likelihood Parameter Estimation with Discrete Measurements.- 16.5 Conclusion.- 17 Recursive Probability Estimators for Count Data.- 17.1 Introduction.- 17.2 Katz Family.- 17.3 Separability and the A.L.D.P..- 17.4 Application.- 18 A Mathematical Model for Behavioral Changes by Pair Interactions.- 18.1 Introduction.- 18.2 The master equation.- 18.3 Most probable behavioral distribution.- 18.4 Kinds of pair interactions.- 18.4.1 Computer simulations.- 18.5 Game dynamical equations.- 18.5.1 Connection between Boltzmann-like and game dynamical equations.- 18.5.2 Stochastic version of the game dynamical equations.- 18.5.3 Selforganization of behavioral conventions by competition between equivalent strategies.- 18.6 Summary and Conclusions.- 19 Employment and Education as Non-Linear Network-Populations, Part I: Theory, Categorization and Methodology.- 19.1 Self-organization Models.- 19.2 Classification Stabilities.- 19.3 Methodolgy Considerations.- 19.3.1 Model Selection.- 19.3.2 Basic Assumptions.- 19.3.3 Micro-Macro-Relations.- 19.4 Systems Couplings.- 20 Employment and Education as Non-Linear Network Populations Part II: Model Structures, Estimations, and Scenarios.- 20.1 The Explanatory Framework.- 20.1.1 The Explanation Scheme for the Master Equation Framework.- 20.1.2 Five Different Designs for Factor Selections.- 20.2 Model Structures.- 20.2.1 The Employment Model.- 20.2.2 Equations of Motion.- 20.2.3 The Education Model.- 20.3 Estimation Results.- 20.4 Scenario Results.- 20.5 Scenario - Dimensions.- 20.6 Future Perspectives.