# Cities as they could be: Artificial Life and Urban Systems

**Authors:** Juste Raimbault  
**arXiv:** [2002.12926](https://arxiv.org/abs/2002.12926)  
**Date:** 2020-02-28  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2002.12926v1/

## Key points

- Cities as organisms metaphor widely used in urban planning
- Urban modeling approaches linked to Artificial Life (ALife)
- Juste Raimbault's paper explores ALife and AI application to urban issues by constructing and exploring a citation network of around 225,000 papers
- Most literature focused on methodologies with strong modularity of approaches
- ALife concepts identified with significant potential for developing new urban theories
- ALife can be applied to various aspects of an urban system such as transportation, land use, energy consumption, and social dynamics
- Simulation models based on principles derived from biology and ecology can help understand complex interactions between different components of an urban system
- Importance of integrating AI techniques such as machine learning into ALife models to improve their predictive power
- Analyzing large datasets generated by sensors embedded in various parts of an urban system can identify patterns not visible through traditional methods
- Adopting a more holistic approach that considers cities as living organisms may help develop more effective solutions for creating sustainable and livable cities in the future.

## Summary

In the field of urban planning, the metaphor of cities as organisms has been widely used and some urban modeling approaches have explicitly been linked to Artificial Life (ALife). Juste Raimbault's recent paper titled "Cities as they could be: Artificial Life and Urban Systems" proposes to explore the extent of ALife and Artificial Intelligence (AI) application to urban issues by constructing and exploring a citation network of around 225,000 papers. The study reveals that most literature in this area is focused on methodologies with a strong modularity of approaches. However, it also identifies ALife concepts with significant potential for developing new urban theories. The paper highlights how ALife can be applied to various aspects of an urban system such as transportation, land use, energy consumption, and social dynamics. By using simulation models based on principles derived from biology and ecology, researchers can better understand complex interactions between different components of an urban system. For instance, one can simulate how changes in transportation infrastructure affect land use patterns or how social networks influence crime rates. The study also emphasizes the importance of integrating AI techniques such as machine learning into ALife models to improve their predictive power. By analyzing large datasets generated by sensors embedded in various parts of an urban system, researchers can identify patterns that are not visible through traditional methods. Overall, this paper provides valuable insights into how ALife and AI can be leveraged to address pressing challenges facing modern cities. Adopting a more holistic approach that considers cities as living organisms rather than mere collections of buildings and infrastructure may help develop more effective solutions for creating sustainable and livable cities in the future.