Simulating Consensus: What Hamburg’s AI Housing Experiment Can Teach Game Designers and Historians

Earlier this week, Harvard Business Review ran a piece by Mathis Bitton and Elizabeth Haas on Hamburg’s unlikely turn from zoning deadlock to large-scale refugee integration. The pivot point was not a political overhaul or a sudden influx of resources but the introduction of CityScope, an MIT Media Lab platform that combined vast data modelling with participatory design.

In Hamburg’s case, the problem was familiar to anyone who has tried to balance realism and playability in a game set in a real place: competing demands, finite space, entrenched rules. The city needed to accommodate tens of thousands of new residents without fracturing existing communities or overloading fragile infrastructure. CityScope’s answer was not a single master plan but a simulation environment where stakeholders could manipulate parameters, test “what if” scenarios, and see the consequences unfold in real time.

Workshops replaced town halls. LEGO-like models stood in for buildings and transit lines. Augmented reality layered performance metrics — environmental impact, energy efficiency, transit accessibility — over the map as participants shifted pieces around. The brilliance was not in showing people a finished plan, but in making them co-authors of the city’s future, using a design language they could intuitively grasp.

For game designers, the parallels are obvious. This is environmental storytelling in a live, iterative setting: mechanics and rules are transparent, player agency is real but bounded, and feedback loops are immediate. In historical game design — whether reconstructing 18th-century Edo or a speculative near future — these principles hold. Players (or citizens) understand the constraints not as arbitrary obstacles but as part of the world’s logic. They experiment, fail, adjust, and negotiate shared space.

For historians, CityScope’s process is a reminder that archives are not just about preservation but about reassembly. When we reconstruct the past, we work with incomplete, unevenly distributed evidence; we simulate possibilities, weigh trade-offs, and — whether we admit it or not — create models that persuade as much as they inform. Hamburg’s workshops resemble what happens when a research seminar takes the leap from interpretation to counterfactual modelling: “If we change this variable, what else must shift for the system to hold together?”

The point is that AI-assisted consensus-building is not simply a governance tool. It’s part of a broader design methodology — one that blends system modelling, historical context, and participatory engagement. Whether you’re planning housing in a real city, designing a historically plausible game space, or constructing an argument about the past, the same questions apply: What rules are negotiable? Who gets to move the pieces? And how do you visualise the consequences clearly enough that people can argue productively about what comes next?

Hamburg’s experience suggests that the future of decision-making — in both civic life and digital culture — may belong to those who can merge the historian’s archive with the designer’s toolkit, turning data into worlds people can explore, inhabit, and change together.

References

Bitton, Mathis, and Elizabeth Haas. “How AI Can Help Tackle Collective Decision-Making.” Harvard Business Review, August 12, 2025. https://hbr.org/2025/08/how-ai-can-help-tackle-collective-decision-making

MIT Media Lab. “CityScope: An Urban Modeling and Simulation Platform.” MIT Media Lab. Last modified May 13, 2022. https://www.media.mit.edu/publications/cityscope/

OECD Observatory of Public Sector Innovation. “CityScope FindingPlaces: HCI Platform for Public Participation in Refugees’ Accommodation Process.” OECD OPSI. https://oecd-opsi.org/innovations/cityscope-findingplaces-hci-platform-for-public-participation-in-refugees-accommodation-process/

MIT Media Lab. “FindingPlaces.” MIT Media Lab: City Science. https://www.media.mit.edu/projects/finding-places/overview/

MIT Media Lab. “Shifting Priorities, Finding Places.” MIT Media Lab. https://www.media.mit.edu/posts/shifting-priorities-finding-places-1/

MIT Media Lab. “City Science Lab @ Hamburg.” MIT Media Lab: City Science. https://www.media.mit.edu/projects/cityscope-hamburg/overview/

Noyman, Ariel, et al. “Finding Places: HCI Platform for Public Participation in Refugees’ Accommodation Process.” arXiv, November 26, 2018. https://arxiv.org/abs/1811.10123

Revel, Manon, and Théophile Pénigaud. “AI-Facilitated Collective Judgements.” arXiv, March 6, 2025. https://arxiv.org/abs/2503.05830

Zhang, Angie, et al. “Deliberating with AI: Improving Decision-Making in Graduate Admissions.” arXiv, February 22, 2023. https://arxiv.org/abs/2302.11623