Active Arts, Culture & Design Engineering

Digitally Assisted Collective Governance of Smart City Commons - ARTIO

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AI plain-English summary

Cities could soon become places where residents vote on traffic jams and parking shortages using their phones, with decisions verified by blockchain and guided by artificial intelligence. This research tackles a fundamental flaw in how smart cities manage shared resources like transport, energy, and parking space. Current digital voting systems often disconnect people from the physical spaces they decide about, making choices vulnerable to misinformation and producing outcomes that lack legitimacy. Majority voting frequently fails to achieve consensus. The project combines Internet of Things devices, human-centred AI, and blockchain technology with social choice theory to create a new kind of collective governance. Urban points of interest—bus stops, car parks, community centres—would become digital voting centres where citizens’ locations are verified on the blockchain, enabling more informed, localised decision-making. If successful, this approach could transform how cities handle chronic problems: traffic congestion, overcrowded parking, and blackouts. Citizens might geolocate problems and vote on transport planning solutions, or allocate participatory budgeting funds on the spot. A smart parking system could balance demand across districts, while coordinated transport choices could optimise traffic control for an equitable shift to public and shared transport, preserving low-carbon zones. The work is applied and demonstration-focused, with four planned impact cases in collaboration with government and industry partners.

View original technical description
The aim of this fellowship programme is to design a socially responsible collective governance for Smart City commons: shared pool of urban resources (transport, parking space, energy) managed and regulated digitally. Smart City commons exhibit unprecedented complexity and uncertainties: transport systems integrate electric, shared and autonomous vehicles, while distributed energy resources highly penetrate energy systems. How can we manage Smart City commons in a sustainable and socially responsible way to tackle long-standing problems such as traffic jams, overcrowded parking spaces or blackouts? Failing to digitally coordinate collective decisions promptly and at large-scale has tremendous economic, social and environmental impact. Coordinated decisions require a digital (r)evolution, a new paradigm on where we decide, how we decide and what we decide. But which are limiting factors? 1.Online decision-making often disconnects citizens from the physical urban space for which decisions are made: choices are less informed and vulnerable to social media misinformation, while decision outcomes may show lower legitimation. What if collective choices could be made more locally as digital geolocated testimonies, creating opportunities for community interactions and deliberation? 2.Voting system design is another origin of poor collective decisions, with majority voting often failing to achieve consensus or fair and legitimate outcomes. What if we expanded the design space of voting systems with alternative voting methods, e.g. preferential, to encompass social values? While such methods have so far been costly and limited to low-cognitive exercises, negating their social value over majority voting, decision-support systems based on artificial intelligence (AI) emerge as game-changer. 3.With an immense computational and communication complexity, large-scale coordination of inter-dependent collective decisions remains a timely grand challenge. What if coordination could be digitally assisted and emerge as a result of smart aggregate information exchange, achieving privacy and efficiency? To address these challenges, I will combine Internet of Things, human-centred AI and blockchain technology with social choice theory and mechanism design. Using IoT devices, urban points of interest can be turned into digital voting centres within which conditions for a more informed decision-making will be verified in the blockchain, e.g. proving citizens' location. A novel ontology of voting features will provide the basis to predict voting methods that generate fair and legitimate outcomes. Using collective and active reinforcement learning techniques on the blockchain, human and machine collective intelligence will be combined to achieve a trustworthy coordination of collective decisions at large scale. In collaboration with high-profile partners from government/industry, I will demonstrate the applicability of these approaches via 4 innovative impact cases. 1.Using the developed solutions, citizens will geolocate problems and vote for transport planning solutions. 2.They will also vote on spot to implement participatory budgeting projects. 3.A smart parking system will be enhanced with load-balancing capabilities to alleviate crowded and polluted city centres. 4.Via citizens' coordination of transport modality, an urban traffic control system will be optimized for an equitable shift to public/sharing transport, while preserving low-carbon transport zones. These Smart City blueprints will open up new avenues for deeper understanding of digitally assisted collective governance. To master this inter-disciplinary research area and develop myself into a future leader, I will visit world-class leaders and, together with my team, enrol in novel training activities. Two esteemed mentors and an advisory board will further support me. I will engage with the broader community of citizens and policy-makers by organizing workshops and hackathons.

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Researchers

Evangelos Pournaras (Principal Investigator)

Related Research

Grants with similar aims, by meaning.

Bringing the Social City to the Smart City
MoSaIC: More-than-human sustainable and inclusive smart cities
Coping with Complexity and Urban Inequality: Dilemmas of Democratic Mega-city Governance
Participatory Policy Learning and New Municipalism
Rebooting Democracy: Democratic innovation for the information age

Original classification

Fellowship

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