The documentary Identical Strangers (2018) tells the deeply tragic story of a social science experiment in postwar New York, in which newborn triplets were separated and put up for adoption with poor or wealthy families. In the documentary, we see how triplets encountered each other "in the wild" - and from there unravel the web of the social experiment to which they were subjected. The brothers immediately become an instant media sensation, as they share many similarities despite their very different upbringings. They are strangers to each other, yet somehow identical. They are identical strangers.
A lot less macabre is the similar situation in the postwar housing stock. A substantial number of houses were built between circa 1965 and 1990. This was a time of enormous housing shortage, but also a time of a new industrial way of building. Components of the houses were made in the factory, and assembled at the construction site. This allowed for cost-efficient construction and reduced the need for skilled labor. Architectural firms worked closely with construction companies and contractors to build this way. As a result, many houses are alike, both on the inside and the outside. Yet who built where and when is largely unknown. Hypothetically, hundreds of thousands of houses could be ‘identical strangers’.
"The goal of this KIEM is to explore the probabilities and the potential for different professionals"
The potential of this undiscovered "alternative data" is exciting. It might provide opportunities to better weigh and scale up energy transition, facilitate circular reuse of building material, apply AI in spatial practice, and service upcoming decisions about cultural heritage.The goal of this KIEM is to explore the probabilities and the potential for different professionals, as reflected in the consortium with the fast-growing urban design firm iMoss and demolition company GPGroot - one of the leaders in circular demolition. From the Amsterdam University of Applied Sciences (HvA), the Chair of Spatial Urban Transformation teamed up with the Chair of Responsible IT, bringing in spatial research and AI research.
Our project builds on the AI techniques and methods we developed in the RAAKmkb project Sensing Streetscapes (www.sensingstreetscapes.com), where we converted urban design metrics into an AI search engine that helps trace reference locations in the design process. In this KIEM, we put AI to use to try to trace identical strangers. Yet, this exploration demands to work along more lines:
- Build an alternative dataset of identical strangers, by conducting an empirical investigation in urban neighborhoods built between 1965 and 1990.
- Investigate the building-culture and practice of this period in order to get a deeper understanding of the possible identical elements of the buildings.
- Design, test, and iterate diverse algorithmic strategies to understand which kind of approach helps to reveal identical strangers.
- Discuss possible use cases for sustainability, circularity, and cultural heritage in these neighborhoods.
"For instance, how many little differences exist between particular sets of the identical strangers – and what can be considered a tipping point?"
At the time of writing, we are in the middle of work. It is promising, challenging, and revealing. For instance, how many little differences exist between particular sets of the identical strangers – and what can be considered a tipping point? What are their building features that reveal their identical character? And which kind of AI strategy and mathematical translation of the buildings features proofs to work most accurately, efficiently, and can be scaled up?
Involving different kinds of disciplines (Urban Transformation research and AI research) and businesses (design and demolition) in one project is promising. Maybe it is this kind of mix of strangers that produces new practical perspectives and pathways for our complex societal issues, and the professionals working on totally different sides of them?