• Need: They fill a gap!

• Physical Foundations: They foreground the physical components of cities (like transport networks and the built and natural environments)

• Efficiency: Yay efficiency!

• Scenarios: They open the door to richer what-if scenario testing.

• Uncertainty: They can help reinforce the importance of model uncertainty

• Appeal: It’s rare a modelling framework or paradigm attaches itself so firmly and so widely to such a variety of domains (public, private, academic) and areas (infrastructure, land use, transport). This is not to be discounted lightly.

Just kidding

No but really

• Lack of demographic, economic, and social process: They ‘rarely include any of the processes that determine how the city works in terms of its social and economic functions’ (Batty, 2018).

• Society at Scale: What does a social DT look like? How can individuals, communities, and policies be captured and presented in a DT?

• Mutability: When urban physical systems change, the sources of that change are typically straightforward to identify. Humans are more difficult.

• Scalability: Wicked societal problems are wicked because they are complex and big

• Time horizons: City DTs and other DTs work at a time scale consisting of minutes, days, and months, using real-time data to fine-tune immediate responses. This is not the time scale of wicked urban and social problems

• Technocracy: The greatest risk that DTs pose may be precisely what makes them so popular: the promise that they are a panacea for a range of challenges that other approaches – and policy and investment – have failed to resolve.

• Data, compute, and real time aren’t going anywhere

• Reckoning with data (access, bias, privacy) also isn’t going anywhere

• Rallying points are valuable (this is kind of a big deal)

• Digital Twins try to respond to a clearly identified need/gap (also a big deal)