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Daniel Czamanski - Towards a New Urban Economics, Inbunden

Inbunden, Engelska, 2026

Towards a New Urban Economics

Traditional Models, Complexity, Big Data and AI

Av Daniel Czamanski

2639 kr

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Beskrivning
Daniel Czamanski analyzes the transition of urban economics from historical methods to contemporary inductive reasoning. Evaluating this change across the field’s early evolution to modern data-driven approaches, he builds on key concepts to explore complexity theory and artificial intelligence.Outlining central topics, including land-use patterns, housing markets, transportation systems and environmental impact, Czamanski provides inductive responses which rely on empirical evidence as the basis for explaining urban growth. Moving past traditional, deductive-centric approaches, chapters emphasize the need to leverage advanced modeling techniques and data analysis to address complex challenges, such as spatial inequality and climate change. The book highlights the importance of understanding cities as nuanced, interconnected systems where macro patterns emerge from micro-level decisions, advocating for integrated and adaptable approaches to urban development and policymaking.Towards a New Urban Economics is an essential resource for scholars and students of economics, finance and disruptive technologies. City planners and government regulators will similarly benefit from its actionable insights into the future of urban spaces.
Produktinformation
  • Utgivningsdatum: 2026-11-03
  • Mått: 156 x 234 x undefined mm
  • Format: Inbunden
  • Språk: Engelska
  • Förlag: Edward Elgar Publishing Ltd
  • ISBN: 9781035387137
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Mer information

Daniel Czamanski, The Philip & Ethel Klutznick Center for Urban and Regional Studies, Technion, Israel Institute of Technology, Israel

Recensioner i media

‘In this well-written and compellingly argued volume, Daniel Czamanski provides a timely critical survey of spatial economic theorizing and modeling, transporting the reader from a world of representative agents and deductive equilibrium solutions to one where understanding is better served by proceeding inductively with agent-based models, “big data”, and Machine learning.’

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