Bakhtizin | Global and National Demographic Forecasting Under Structural Uncertainty | Buch | 978-3-032-46973-1 | www.sack.de

Buch, Englisch, 111 Seiten, Format (B × H): 155 mm x 235 mm

Reihe: SpringerBriefs in Population Studies

Bakhtizin

Global and National Demographic Forecasting Under Structural Uncertainty

Demographic Memory and Agent-Based Models
Erscheinungsjahr 2027
ISBN: 978-3-032-46973-1
Verlag: Springer

Demographic Memory and Agent-Based Models

Buch, Englisch, 111 Seiten, Format (B × H): 155 mm x 235 mm

Reihe: SpringerBriefs in Population Studies

ISBN: 978-3-032-46973-1
Verlag: Springer


This book examines the challenges facing long-term demographic forecasts in an era of structural change. It explains how changes in demographic persistence, rising behavioral heterogeneity, and growing volatility can weaken the stability of fertility, mortality, and migration mechanisms, and thereby narrow the horizon over which projections can be trusted. Building on leading international projection frameworks, the book clarifies the difference between relatively robust short- and medium-term forecasts and long-run projections that are sensitive to structural breaks and nonlinear dynamics.

A distinctive contribution is the use of autocorrelation analysis as a diagnostic tool for examining demographic memory. By comparing autocorrelation structures of fertility, life expectancy, and net migration across countries, historical periods, and time lags, the book identifies varied patterns of statistical dependence and discusses their implications for the assumptions underlying extrapolation. Autocorrelation is used not to produce forecasts, but to assess linear dependence on past indicator values as one observable component of demographic memory.

The book also introduces agent-based models as a complementary framework for scenario analysis and stress-testing. ABM enables explicit representation of individual heterogeneity, adaptive behavior, and interaction effects that aggregate models often compress. Previously published modeling experiments provide retrospective illustrations of the proposed approach. Rather than proposing a single “best” long-term forecast, the book develops uncertainty-aware methods to interpret projections and explore alternative demographic futures.

Intended for demographers, population forecasters, economists, and computational social scientists, the volume provides a clear, methodologically grounded perspective on the limits of prediction and on practical tools for analysis under uncertainty.

Bakhtizin Global and National Demographic Forecasting Under Structural Uncertainty jetzt bestellen!

Zielgruppe


Research


Autoren/Hrsg.


Weitere Infos & Material


Chapter 1. Long-Term Demographic Forecasts and Their Methodological Foundations.- Chapter 2. Methodological Debates and Demographic Memory.- Chapter 3. Major Demographic Trends as Sources of Forecast Instability.- Chapter 4. Agent-Based Models and Demographic Simulation Frameworks.- Chapter 5. Scenario-Based Analysis and Demographic Futures under Uncertainty.


Albert Bakhtizin is a Member of the Russian Academy of Sciences, Director of the Central Economics and Mathematics Institute of the Russian Academy of Sciences, and a Professor at Lomonosov Moscow State University, Russia. His research focuses on demographic forecasting, structural uncertainty, and agent-based modeling. He has participated in international research projects, including World Bank–related initiatives, and has delivered invited academic lectures at leading universities. He also holds the Microsoft Certified Solution Developer (MCSD) credential.



Ihre Fragen, Wünsche oder Anmerkungen
Vorname*
Nachname*
Ihre E-Mail-Adresse*
Kundennr.
Ihre Nachricht*
Lediglich mit * gekennzeichnete Felder sind Pflichtfelder.
Wenn Sie die im Kontaktformular eingegebenen Daten durch Klick auf den nachfolgenden Button übersenden, erklären Sie sich damit einverstanden, dass wir Ihr Angaben für die Beantwortung Ihrer Anfrage verwenden. Selbstverständlich werden Ihre Daten vertraulich behandelt und nicht an Dritte weitergegeben. Sie können der Verwendung Ihrer Daten jederzeit widersprechen. Das Datenhandling bei Sack Fachmedien erklären wir Ihnen in unserer Datenschutzerklärung.