Digitally Assisted Collective Governance of Smart City Commons - ARTIO
Lead Research Organisation:
University of Leeds
Department Name: Sch of Computing
Abstract
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.
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.
Organisations
- University of Leeds (Fellow, Lead Research Organisation)
- ETH Zurich (Collaboration)
- Swiss National Science Foundation (Collaboration)
- University of York (Collaboration)
- Lappeenranta University of Technology (Collaboration)
- University of Fribourg (Collaboration)
- Karlsruhe Institute of Technology (Collaboration)
- OXFORDSHIRE COUNTY COUNCIL (Collaboration)
- University of Vienna (Collaboration)
- University of Sheffield (Collaboration)
- Stanford University (Collaboration)
- White Rose University Consortium (Collaboration)
- University of Bonn (Collaboration)
Publications
Guastella D
(2023)
Cooperative Multi-Agent Traffic Monitoring Can Reduce Camera Surveillance
in IEEE Access
Hausladen CI
(2023)
On the Legitimacy of Voting Methods
in SSRN
Helbing D
(2023)
Democracy by Design: Perspectives for Digitally Assisted, Participatory Upgrades of Society
in Journal of Computational Science
Narayanan A
(2023)
Collective Learning for Energy-centric Flexible Job Shop Scheduling
Pournaras E
(2024)
Collective privacy recovery: Data-sharing coordination via decentralized artificial intelligence.
in PNAS nexus
Qin C
(2023)
Coordination of drones at scale: Decentralized energy-aware swarm intelligence for spatio-temporal sensing
in Transportation Research Part C: Emerging Technologies
Description | The most significant achievements from the award so far have been the building of a novel collective privacy recovery method using decentralized artificial intelligence (AI). Here we introduce for first time a scalable and practical data-sharing coordination capability to collectively recover significant privacy for people, while letting data collectors provide online services of high quality with lower cost and risks. Such win-win coordination would not be possible to achieve at scale by humans alone due to the (combinatorial) complexity of the computational problem behind collective privacy recovery. Via data-sharing collective arrangements assisted by decentralized trustworthy AI, we finally make feasible to share data under the doctrine "as little as possible, as much as necessary", which has so far failed to find practicality and applicability. Our study provides a comprehensive understanding of the criteria that influence data-sharing decisions under different conditions: from privacy attitudes, to intrinsic, rewarded and coordinated data sharing. This is a striking advancement in the field that moves far beyond existing privacy protection techniques or mainstream privacy-intrusive artificial intelligence. With this key finding, we aspire to set a stepping stone for a long-awaited renaissance of privacy in the digital era. These key finding directly contribute to the overarching aim of this fellowship: to build socially responsible and sustainable governance systems of Smart City commons based on trustworthy digitally assisted collective decisions. Here, our key findings touch upon the huge amount of sensitive personal data generated by Smart City socio-technical infrastructures and how communities (data collectives) can manage their sharing with lower risks. This finding also contributes to the following objectives of this fellowship: O3: Build mechanisms of human-machine collective intelligence to coordinate citizens' decisions and solve complex governance problems of Smart City commons. O4: Empower trust on the studied decision-support systems without relying on single third parties and by aligning with citizens' collective interests. Moreover, the fellowship has generated key findings on the legitimacy of voting methods. In particular, we demonstrate evidence that the legitimacy of collective decision outcomes can be improved via multi-option preferential voting rules instead of majority voting. These findings contribute to the following objective: Determine the design of voting systems for outcomes that encompass social values such as fairness, legitimacy, consensus and participation in governing Smart City commons. |
Exploitation Route | The findings might be taken forward by industry (service providers, data centers) as a way to lower costs and risks of data storage. It may also support policy-makers and communities to align data sharing with the privacy values of citizens. These findings will be taken forward by policy-makers and designers of voting campaigns at a next stage of the project. |
Sectors | Communities and Social Services/Policy Digital/Communication/Information Technologies (including Software) Environment Government Democracy and Justice Security and Diplomacy Other |
URL | https://doi.org/10.48550/arXiv.2301.05995 |
Description | This fellowship project has created the first key results with a direct impact on democracy and citizens' participation. In particular, findings on the legitimacy of voting methods (https://dx.doi.org/10.2139/ssrn.4372245) are used to inform the design of a participatory budgeting campaign in the city of Aarau in Switzerland. The campaign brings several innovations, for instance, testing a combination of cumulative voting and the method of equal shares with the aim for a more fair election of funded projects, higher inclusion of minorities, higher legitimacy and participation. Moreover, this effort also involves the development of new open source software features and the collection of new open data to be published. |
First Year Of Impact | 2023 |
Sector | Communities and Social Services/Policy,Government, Democracy and Justice |
Impact Types | Cultural Societal Policy & public services |
Description | Adoption of Fair Participatory Budgeting (Method of Equal Shares) |
Geographic Reach | Local/Municipal/Regional |
Policy Influence Type | Contribution to new or improved professional practice |
Impact | This method of equal shares is a completely different approach to elect winners in participatory budgeting campaigns. It promotes proportionality and prevents marginalization of communities, fairer allocation of budgets and inclusion of minorities. This method may create new opportunities for trust to direct democracy, improved economic impacts, changed of public attitudes towards direct participation and improved efficiency in delivery of public services. |
URL | https://www.stadtidee.aarau.ch/abstimmungsphase.html/1937 |
Description | New Edge-Cloud Infrastructure for Distributed Intelligent Computing |
Amount | £50,000 (GBP) |
Organisation | Alan Turing Institute |
Sector | Academic/University |
Country | United Kingdom |
Start | 06/2022 |
End | 03/2023 |
Title | Improvements on the collective learning method of EPOS |
Description | This is a multi-agent distributed combinatorial optimization method that is supporting our research and the study of new applications. |
Type Of Material | Improvements to research infrastructure |
Year Produced | 2023 |
Provided To Others? | Yes |
Impact | Expanded functionality: hard constraints satisfaction allowing us to tackle new optimization problems and applications. |
URL | http://epos-net.org |
Title | Stanford Participatory Budgeting |
Description | A voting platform for participatory budgeting campaigns |
Type Of Material | Improvements to research infrastructure |
Year Produced | 2023 |
Provided To Others? | Yes |
Impact | In collaboration with the development team in Stanford, we introduced new features such as cumulative voting, the support of the German language and the CHF currency. With these improvements, the platform will be used for voting in the city of Aarau in Switzerland. |
URL | https://github.com/StanfordCDT/pb |
Title | Agent-based Planning Portfolio |
Description | This dataset contains a portfolio of different datasets with generated plans for agents that represent multiple options to choose from. The following datasets are included so far: 1. Synthetic 2. Residential energy consumption 3. Bike sharing 4. Charging control of electric vehicles 5. Fog computing load-balancing 6. Home appliances peak-shaving 7. Data collectives These portfolio of agent plans has been used to study the collective learning algorithm of EPOS, the Economic Planning and Optimized Selections: http://epos-net.org |
Type Of Material | Database/Collection of data |
Year Produced | 2023 |
Provided To Others? | Yes |
Impact | Two new datasets were uploaded: - Data collectives - UAV swarm intelligence for distributed sensing These datasets were collected to allow the research outlined in the following papers respectively: Pournaras, E., Ballandies, M.C., Bennati, S. and Chen, C.F., 2023. Collective Privacy Recovery: Data-sharing Coordination via Decentralized Artificial Intelligence. arXiv preprint arXiv:2301.05995. Qin, C. and Pournaras, E., 2022. Coordination of Drones at Scale: Decentralized Energy-aware Swarm Intelligence for Spatio-temporal Sensing. arXiv preprint arXiv:2212.14116. |
URL | https://figshare.com/articles/dataset/Agent-based_Planning_Portfolio/7806548/5 |
Title | Data Collectives |
Description | Human experimentation data on data sharing decisio conducted at the DeSciL lab at ETH Zurich |
Type Of Material | Database/Collection of data |
Year Produced | 2023 |
Provided To Others? | Yes |
Impact | This dataset allowed us to conduct an ambitious research on privacy and build an AI solution to achieve collective privacy recovery. This work is featured in the following paper: Pournaras, E., Ballandies, M.C., Bennati, S. and Chen, C.F., 2023. Collective Privacy Recovery: Data-sharing Coordination via Decentralized Artificial Intelligence. arXiv preprint arXiv:2301.05995. |
URL | https://figshare.com/articles/dataset/Data_Collectives/21750158/1 |
Description | Distributed optimization and UAV swarm intelligence |
Organisation | Lappeenranta University of Technology |
Country | Finland |
Sector | Academic/University |
PI Contribution | We provided expertise on collective learning and distributed optimization for two problems studied in LUT: - UAV last mile delivery - Job shop scheduling |
Collaborator Contribution | New application domains to test and study our optimization methods |
Impact | Two papers under consideration for publication. Two more papers are ongoing work. |
Start Year | 2022 |
Description | How is Artificial Intelligence Changing Science? Research in the Era of Learning Algorithms |
Organisation | Karlsruhe Institute of Technology |
Country | Germany |
Sector | Academic/University |
PI Contribution | I participate in this project as cooperation partner: https://howisaichangingscience.eu/cooperation-partners/ My role is to inform the project members and develop collaborations on how AI is changing inter-disciplinary research in the interface of computer and social science. |
Collaborator Contribution | Invitation to workshop and invitation to contribute to a forthcoming book. |
Impact | Participated in an organized workshop (https://howisaichangingscience.eu/conference-beyond-quantity/) and planning to contribute to a forthcoming book. |
Start Year | 2022 |
Description | How is Artificial Intelligence Changing Science? Research in the Era of Learning Algorithms |
Organisation | University of Bonn |
Country | Germany |
Sector | Academic/University |
PI Contribution | I participate in this project as cooperation partner: https://howisaichangingscience.eu/cooperation-partners/ My role is to inform the project members and develop collaborations on how AI is changing inter-disciplinary research in the interface of computer and social science. |
Collaborator Contribution | Invitation to workshop and invitation to contribute to a forthcoming book. |
Impact | Participated in an organized workshop (https://howisaichangingscience.eu/conference-beyond-quantity/) and planning to contribute to a forthcoming book. |
Start Year | 2022 |
Description | How is Artificial Intelligence Changing Science? Research in the Era of Learning Algorithms |
Organisation | University of Vienna |
Country | Austria |
Sector | Academic/University |
PI Contribution | I participate in this project as cooperation partner: https://howisaichangingscience.eu/cooperation-partners/ My role is to inform the project members and develop collaborations on how AI is changing inter-disciplinary research in the interface of computer and social science. |
Collaborator Contribution | Invitation to workshop and invitation to contribute to a forthcoming book. |
Impact | Participated in an organized workshop (https://howisaichangingscience.eu/conference-beyond-quantity/) and planning to contribute to a forthcoming book. |
Start Year | 2022 |
Description | Implementation of new voting methods for participatory budgeting |
Organisation | Stanford University |
Country | United States |
Sector | Academic/University |
PI Contribution | - Using the Stanford Participatory Budgeting platform (https://pbstanford.org) for first time in Europe - Proposing and designing new features for the platform - Knowledge exchange |
Collaborator Contribution | Implementation of new features on the Stanford Participatory Budgeting platform (https://pbstanford.org) |
Impact | - New supported features on the Stanford Participatory Budgeting platform (https://pbstanford.org). - New designed voting campaign to be tested with the platform - Further development and maintenance of open-source software |
Start Year | 2022 |
Description | Legitimacy in Participatory Politics |
Organisation | University of Fribourg |
Country | Switzerland |
Sector | Academic/University |
PI Contribution | Research visit of Dr. Thomas Wellings to build collaboration and work on research outputs from the participatory budgeting field test under the guidance of Prof. Regula Hänggli Fricker, University of Fribourg. |
Collaborator Contribution | Working in the School of Media and Communication, we aim to publish a high quality social science paper exploring legitimacy, with the results reflecting the Aarau study, as well as two additional studies in Switzerland. Aside from this, we will be working on further research exploring social network analysis of online political communication, which may have the scope for future collaboration. There are also talks planned and teaching, which will offer valuable experience. |
Impact | The following outcome has been part of this collaboration A survey on understanding determinants of legitimacy in participatory politics. - On the sources of legitimacy in participatory politics: A systematic literature review - Laurent Bernhard, Regula Hänggli, Thomas Wellings, Evangelos Pournaras - https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4699365 A software to test the expressiveness of voting methods - Fair and Inclusive Participatory Budgeting: Voter Experience with Cumulative and Quadratic Voting Interfaces - Thomas Welling, Fatemeh Banaie Heravan, Abhinav Sharma, Lodewijk Gelauff, Regula Haenggli, Evangelos Pournaras. https://arxiv.org/pdf/2308.04345.pdf |
Start Year | 2023 |
Description | Policy-making for Sustainable Mobility |
Organisation | Oxfordshire County Council |
Country | United Kingdom |
Sector | Public |
PI Contribution | The contributions are outlined as follows: - Knowledge exchange about our experience with the city of Aarau and the participatory budgeting campaign. - Contribution to an organized workshop in Oxfordshire - Ongoing preparation of a study on smart mobility |
Collaborator Contribution | The contributions are outlined as follows: - Organization of a workshop - Sharing insights and data about smart mobility in Oxfordshire |
Impact | An outline of the outputs includes the following: - An organized workshop - A first concept of a study on smart mobility to be performed in Oxfordshire |
Start Year | 2022 |
Description | Trust and legitimation in the digital democracy |
Organisation | ETH Zurich |
Country | Switzerland |
Sector | Academic/University |
PI Contribution | - Design of lab experiments and a field test on participatory budgeting - Data collection and software development - Research on voting methods |
Collaborator Contribution | The city of Aarau organizes and supports the participatory budgeting voting campaign. The University of Fribourg coordinates the project and ETH Zurich has provided access to the ETH Decisions Science lab for human lab experimentation |
Impact | The outputs of the project include: - Data for analysis - Software - New policies and decision-making practices for direct democracy - Publications This collaboration is highly multi-disciplinary in the interface of computer, social and political science. |
Start Year | 2020 |
Description | Trust and legitimation in the digital democracy |
Organisation | Swiss National Science Foundation |
Country | Switzerland |
Sector | Public |
PI Contribution | - Design of lab experiments and a field test on participatory budgeting - Data collection and software development - Research on voting methods |
Collaborator Contribution | The city of Aarau organizes and supports the participatory budgeting voting campaign. The University of Fribourg coordinates the project and ETH Zurich has provided access to the ETH Decisions Science lab for human lab experimentation |
Impact | The outputs of the project include: - Data for analysis - Software - New policies and decision-making practices for direct democracy - Publications This collaboration is highly multi-disciplinary in the interface of computer, social and political science. |
Start Year | 2020 |
Description | Trust and legitimation in the digital democracy |
Organisation | University of Fribourg |
Country | Switzerland |
Sector | Academic/University |
PI Contribution | - Design of lab experiments and a field test on participatory budgeting - Data collection and software development - Research on voting methods |
Collaborator Contribution | The city of Aarau organizes and supports the participatory budgeting voting campaign. The University of Fribourg coordinates the project and ETH Zurich has provided access to the ETH Decisions Science lab for human lab experimentation |
Impact | The outputs of the project include: - Data for analysis - Software - New policies and decision-making practices for direct democracy - Publications This collaboration is highly multi-disciplinary in the interface of computer, social and political science. |
Start Year | 2020 |
Description | White Rose Collaboration: Socially Responsible AI for Distributed Autonomous Systems |
Organisation | University of Sheffield |
Department | Automatic Control and Systems Engineering |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | This collaboration has been a White Rose Collaboration project that started on 28.1.2021 and completed on 31.12.2022. I have been the PI of this collaboration project and I ran research activities in synergy with University of Sheffield. |
Collaborator Contribution | The White Rose University Consortium funded the project with £11K, which funded lab equipment/supplies, traveling, conference and technical lab work. University of Sheffield contributed with making lab space available for one of my PhD students during two visits. |
Impact | An outlines of the outcomes include the following: - Profile Strengthening, Prestige and Mitigation of Exceptional Circumstances - PhD Students Support & Development - Workshops - Expansion of Lab Equipment and Research Activities - Research Visits & Exchanges - Ongoing and Future Research Activity - Proposals - Open data and Software - Publications More information is available upon request. This collaboration was multi-disciplinary covering the area of computer science and engineering (control systems). |
Start Year | 2021 |
Description | White Rose Collaboration: Socially Responsible AI for Distributed Autonomous Systems |
Organisation | University of York |
Department | Department of Computer Science |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | This collaboration has been a White Rose Collaboration project that started on 28.1.2021 and completed on 31.12.2022. I have been the PI of this collaboration project and I ran research activities in synergy with University of Sheffield. |
Collaborator Contribution | The White Rose University Consortium funded the project with £11K, which funded lab equipment/supplies, traveling, conference and technical lab work. University of Sheffield contributed with making lab space available for one of my PhD students during two visits. |
Impact | An outlines of the outcomes include the following: - Profile Strengthening, Prestige and Mitigation of Exceptional Circumstances - PhD Students Support & Development - Workshops - Expansion of Lab Equipment and Research Activities - Research Visits & Exchanges - Ongoing and Future Research Activity - Proposals - Open data and Software - Publications More information is available upon request. This collaboration was multi-disciplinary covering the area of computer science and engineering (control systems). |
Start Year | 2021 |
Description | White Rose Collaboration: Socially Responsible AI for Distributed Autonomous Systems |
Organisation | White Rose University Consortium |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | This collaboration has been a White Rose Collaboration project that started on 28.1.2021 and completed on 31.12.2022. I have been the PI of this collaboration project and I ran research activities in synergy with University of Sheffield. |
Collaborator Contribution | The White Rose University Consortium funded the project with £11K, which funded lab equipment/supplies, traveling, conference and technical lab work. University of Sheffield contributed with making lab space available for one of my PhD students during two visits. |
Impact | An outlines of the outcomes include the following: - Profile Strengthening, Prestige and Mitigation of Exceptional Circumstances - PhD Students Support & Development - Workshops - Expansion of Lab Equipment and Research Activities - Research Visits & Exchanges - Ongoing and Future Research Activity - Proposals - Open data and Software - Publications More information is available upon request. This collaboration was multi-disciplinary covering the area of computer science and engineering (control systems). |
Start Year | 2021 |
Title | Data Collectives |
Description | AI system, documentation and analysis of coordinated data sharing of data collectives |
Type Of Technology | Software |
Year Produced | 2022 |
Open Source License? | Yes |
Impact | This software made possible to complete the following work: Pournaras, E., Ballandies, M.C., Bennati, S. and Chen, C.F., 2023. Collective Privacy Recovery: Data-sharing Coordination via Decentralized Artificial Intelligence. arXiv preprint arXiv:2301.05995. |
URL | https://zenodo.org/record/7457574 |
Title | Discrete-choice Multi-agent Optimization: Decentralized Hard Constraint Satisfaction for Smart Cities |
Description | This software is for implementing hard constraints at aggregate level in decentralized architectures |
Type Of Technology | Software |
Year Produced | 2023 |
Open Source License? | Yes |
Impact | This software is part of the paper - Discrete-choice Multi-agent Optimization: Decentralized Hard Constraint Satisfaction for Smart Cities - Srijoni Majumdar, Chuhao Qin, Evangelos Pournaras. In Multiagent Sequential Decision Making under Uncertainty (MSDM), 22nd International Conference on Autonomous Agents and Multiagent Systems, 2023. |
URL | https://github.com/epournaras/EPOS/tree/hard_constraints |
Title | Participatory Budgeting (PB) using Reinforcement Learning (MARL PB) |
Description | This repository describes the initial release of the code to simulate the Participatory Budgeting process and reach a consensus in real-world datasets from http://pabulib.org/ |
Type Of Technology | Software |
Year Produced | 2023 |
Open Source License? | Yes |
Impact | Publications, educational platform, supporting further research. This software is generated for the paper - "Consensus-Based Participatory Budgeting for Legitimacy: Decision Support via Multi-agent Reinforcement Learning." - Srijoni Majumdar and Evangelos Pournaras. In International Conference on Machine Learning, Optimization, and Data Science, pp. 1-14. Cham: Springer Nature Switzerland, 2023. |
URL | https://github.com/DISC-Systems-Lab/MARL-PB |
Title | SMOTEC: Smart Mobility On The Edge Computing |
Description | SMOTEC: Smart Mobility On The Edge Computing |
Type Of Technology | Software |
Year Produced | 2023 |
Open Source License? | Yes |
Impact | Publications, educational platform, supporting further research |
URL | https://zenodo.org/record/8167871 |
Title | Stanford Participatory Budgeting Platform |
Description | The software is used for conducting online/offline voting for participatory budgeting projects. The software includes support for various languages and voting methods. https://arxiv.org/abs/2308.04345 |
Type Of Technology | Software |
Year Produced | 2023 |
Open Source License? | Yes |
Impact | The software was used in conducting a participatory budgeting field test in the city of Aarau, Switzerland. |
URL | https://github.com/DISC-Systems-Lab/SPB-Stadtidee |
Title | Votelab |
Description | Digital democracy and new forms for direct digital participation in policy making gain unprecedented momentum. This is particularly the case for preferential voting methods and decision-support systems designed to promote fairer, more inclusive and legitimate collective decision-making processes in citizens' assemblies, participatory budgeting and elections. However, a systematic human experimentation with different voting methods is cumbersome and costly. This paper introduces VoteLab, an open-source and thoroughly-documented platform for modular and adaptive design of voting experiments. Voting designers visually and interactively build reusable campaigns with different voting methods, while voters easily respond to subscribed voting questions on the smartphone. A proof-of-concept with four voting methods and questions on COVID-19 involved in an online lab experiment assesses the consistency of voters' choices and demonstrates the capability of VoteLab to support rigorous experimentation of complex voting scenarios. |
Type Of Technology | Software |
Year Produced | 2023 |
Open Source License? | Yes |
Impact | Platform used for lab experiments and other publications |
Description | ASDA Lida Health & Sustainability Hackathon |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Industry/Business |
Results and Impact | This activity was a hackathon organized by ASDA and the Leeds Institute of Data Analytics (LIDA). The goal of this hackathon was to design and build solutions for healthy and sustainable consumption for the customers of ASDA. I participated in both the ideation phase as well as in the main hackathon event. My fellowship team also participated in the final hackathon event. Around 40-50 participants were split in 4 groups working on different aspects of sustainable consumption, for instance, carbon footprint, healthy recipes, etc. Together with my team, we provided expertise and shared lessons learnt from our award-winning Horizon EU project ASSET. The event was invaluable to build links to industry, know the datasets available and build a collaboration on decision-support systems for empowering sustainable consumption. |
Year(s) Of Engagement Activity | 2023 |
Description | Competition law and beyond: efficient digital markets through privacy, taxation, and consumer protection |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | This was a highly inter-disciplinary event to which I contributed as an invited speaker and panel expert on the topic of "Interdisciplinary Approaches to Digital Markets". The participants of the events were mainly lawyers, social scientists, economists and policy makers from university, public and non-governmental organizations. Participants were both online and physically present. The panel discussions provided new insights to professionals and practitioners about the design of digital markets and how they can be regulated for the protection of consumers' privacy and rights. |
Year(s) Of Engagement Activity | 2022 |
URL | https://www.leeds.ac.uk/main-index/events/event/212/jean-monnet-centre-of-excellence-on-digital-gove... |
Description | Field Study in Aarau |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Policymakers/politicians |
Results and Impact | input survey with citizens to understand public welfare amenities; scenario-based question on preference for the aggregation method; socio-demographic information; social preferences; political profiles. Actual voting for welfare projects in Aarau: 1703 voters; cumulative voting; 33 alternatives (projects); 17 winners using fairness and popularity based methods |
Year(s) Of Engagement Activity | 2023 |
URL | https://www.stadtidee.aarau.ch/abstimmungsphase.html/1937 |
Description | Invited Talk at the webinar of the Global Food and Environment Institute: A novel App to support the discovery and choices of more sustainable products |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | This was an invited talk to the September 2022 webinar of the Global Food and Environment Institute. I talked about my work on "How value-sensitive design can empower sustainable consumption", which received the GFEI Food and Agriculture Awards 2021. The event had 20-30 participants and other 70 views online in YouTube. My talk triggered interest and I had a follow up invitation to participate in the ASDA LIDA Healthy and Sustainable Consumption Hackathon event. |
Year(s) Of Engagement Activity | 2022 |
URL | https://www.youtube.com/watch?v=cwf_vtYLeyc |
Description | Invited talk at the Hackathon "Participatory Resilience" at ETH Zurich |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Postgraduate students |
Results and Impact | The goal of this talk was to give a hands on tutorial on collective learning and the EPOS optimization systems: Pournaras, E., Pilgerstorfer, P. and Asikis, T., 2018. Decentralized collective learning for self-managed sharing economies. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 13(2), pp.1-33. https://doi.org/10.1145/3277668 The students and practitioners participated in the hackathon used the EPOS toolkit to run Smart City optimization scenarios that improve the resilience of socio-technical infrastructures in a more participatory and decentralized way. The talk was received with enthusiasm and strengthened the ongoing collaboration with ETH Zurich. A follow up invited talk is made for the event "Co-Creating the Future: Participatory Cities and Digital Governance": https://www.participatorycities.net |
Year(s) Of Engagement Activity | 2022 |
URL | http://participatoryresilience.ch |
Description | Invited talk in the "10th International Conference on Automation, Robotics, and Applications (ICARA 2024)" |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | The talk covers the topic of "Coordination of Drones at Scale in Smart Cities: From Distributed Optimization to Multi-agent Reinforcement Learning". |
Year(s) Of Engagement Activity | 2024 |
Description | Invited talk in the "9th International Conference on Machine Learning, Optimization & Data Science" |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | The talk was presented on the topic "Collective Privacy Recovery: Data-sharing Coordination via Decentralized Artificial Intelligence", that highlighted a scalable privacy recovery mechanism. This led to conversations where participants were interested to understand the decentralized artificial intelligence architecture and how it can be used to collectively share data as little as possible and also achieve a minimum quality of service. |
Year(s) Of Engagement Activity | 2023 |
URL | https://academic.oup.com/pnasnexus/article/3/2/pgae029/7584946 |
Description | Invited talk to the "2023 Interact Workshop: Design for Equality and Justice" |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | The talk was presented on the topic "Fair and Inclusive Participatory Budgeting: Voter Experience with Cumulative and Quadratic Voting Interfaces" . |
Year(s) Of Engagement Activity | 2023 |
Description | Invited talk to the workshop on "Edge-Cloud Infrastructure for Distributed Intelligent Computing" |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | The workshop had speakers and participants from academia and industry working in the area of smart mobility and edge computing. The talk was presented on the topic "Smart Mobility & Edge Computing: Distributed Co-optimization at Scale", sparking discussion and further information about the topic. A total of 40 researchers attended the talk and shared their experiences in implementing smart systems for mobility and the optimizations aspects of their system. This talk also spurred effective conversations on how to implement hard constraints for further optimization and safety which is an important aspect in autonomous vehicle control. |
Year(s) Of Engagement Activity | 2023 |
Description | Invited talk to the workshop: "Beyond Quantity: Research with Subsymbolic AI" |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | This workshop consisted of participants and speakers from many different disciplines interested in AI and how AI is changing science. This included anthropologists, media artists, philosophers, sociology and computer science. Based on the outcomes of this workshop, the organizer is preparing a book with the contributions of the workshop. My talk covers the the topic of "Managing Smart City Commons with Human-machine Collective Intelligence" and I am planning a contribution to the forthcoming book. Moreover, the organizer is planning a visit at University of Leeds in Sept. 2023 to continue and strengthen the collaboration. |
Year(s) Of Engagement Activity | 2022 |
URL | https://howisaichangingscience.eu/conference-beyond-quantity/ |
Description | Invited talk to: "Leeds Institute for Data Analytics Workshop on Data Science Infrastructures" |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | The talk was attended by participants from various discipline. The talk was presented on the topic "Discrete-choice Multi-agent Optimization: Decentralized Hard Constraint Satisfaction for Smart Cities" spurred interest in understanding how hard constraints can help in improving the resilience and sustainability of technology used for developing smart cities. Interestingly, the participants were involved in understanding how hard constraints were implemented at aggregate level for decentralized multi-agent systems. |
Year(s) Of Engagement Activity | 2023 |
Description | Invited talk to: "Leeds Institute for Data Analytics Workshop on Data Science Infrastructures" |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | The workshop had the participants from many different disciplines having focus on data science and data science infrastructure. My talk focused on the topic of "Analysis and Prediction of Medical Protest Events using GDELT(Global Database of Events, Languages and Tone), the talk sparked a discussion about the novel idea of understanding social unrest events using data. |
Year(s) Of Engagement Activity | 2023 |