Urban Lifecycle Management: Modeling smart cities as complex systems
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- Smart cities can be modeled as complex systems with interacting functions that produce emergent properties greater than the sum of parts. - Modeling a smart city involves 3 steps: 1) strategic analysis of goals and stakeholders, 2) inventorying "building blocks" like functions, resources, capabilities, and 3) integrating blocks in a sustainable urban ecosystem. - Challenges include modeling soft human systems, ensuring equal access to the system, and balancing top-down and bottom-up innovation while improving social capital.
Urban Lifecycle Management: Modeling smart cities as complex systems
- 1. Claude Rochet Urban lifecycle management : system architecture applied to the conception and monitoring of smart cities: What Consequences for Public Management? Prof. Claude Rochet Claude.rochet@univ-amu.fr 1
- 2. What means “Smart”= presence of a learning feedback loop 2 Action Effect feedback from 0,0001sec. to a génération Sensors Data TreatmentInterpretation Usage Decision Technologies Social sciences ICT amplifying effect
- 3. When speaking of smart cities, what does it means? Efficient urbanization Inclusive urbanization Sustainable urbanization 3 Complex System Architecture: What are the key functions and their (un) desirable interactions? Complex System Architecture: What are the key functions and their (un) desirable interactions? System Integration: Granting people the same capacity to interact and have control over the urban system System Integration: Granting people the same capacity to interact and have control over the urban system Ecosystem modeling: Autopoiesis, resilience, scalability, innovation coordination Ecosystem modeling: Autopoiesis, resilience, scalability, innovation coordination
- 4. Claude Rochet Let’s set up some definitions: • Architecture, system architecture – The design of how basic functions interact to give birth to a whole that is more than the sum of the parts • Ecosystem: – A system with autopoeitic properties, that means being able to reproduce itself • Entropy, negentropy – Interactions within the system make it losing its energy and increasing disorder (entropy), life (human life in the case of a city) may import energy (negative entropy or negentropy) • Emergence: – Many properties of a system do not exist as a basic function or a physical state, but are the result of the interactions of these functions: eg. “ageing well”, “happy life” is the result of both physical and human systems. • Resilience: – The property of a system to withstand a shock and to recover with stronger ability • Green IT and IT for green – IT is both a solution to coordination problems that may help saving energy (eg. Smart grids) but fabrication of IT produce a lot of pollutants and its functioning produce a lot of heat and waste that need to be recycled. 11/09/2014 4
- 5. Claude Rochet Our basic assumptions • A smart city is not putting lipstick on a bulldog • A smart city is an ecosystem that includes the city and its periphery • A smart city is a city where one may live and work in: o Economic wealth creation o Social life o Common weal • A resilient architecture: o A living system based on cooperation between public authorities, private corp., citizens o A properly designed architecture made with off-the-shelf components o Systemic resilience is leveraged using IT • A sea change in firms business models and public administration. What is our shared vision?What is our shared vision? 11/09/2014 5
- 6. Is modeling a smart city possible? • A dead end: The temptation of the ideal city : XX century garden cities, techno- pushed approaches Masdar, Songdo… • A city is a living system 6
- 7. What modelling means? The Lego game: • The construction is based on standardised building blocks • No two figures are alike • Building is made using patterns: rules of integration using semantic + syntax • The final result in an integration of all the building blocks which is specific to needs and specifications 7
- 8. Claude Rochet A rationale for a smart city a system architect: A three steps approach • Strategic analysis • Inventorying the building blocks • Integrating the ecosystem 8
- 9. A rationale for a smart city a system architect: 1- Strategic analysis 9 Why building a city & what are the strategic goals? Who are the stakeholders? What are the generic functions to be performed by a smart city? With which organs? Technical devices, software… With which smart people? Conception, metamodel framework, steering Subsystems and processes People and tools Why designing this ecosystem? Who will live in the city? What are its activities? How the city will be fed? Where the city is located ? (context) What are the functions to be performed to reach the goals and how do they interact? With which organs and ressources? How people will interact with the artifacts? How civic life will organize?
- 10. Claude Rochet A rationale for a smart city a system architect: 2- Inventorying the “building blocks” 11/09/2014 10 Issues • Defining “smartness” and “sustainability” • Wealth creation • Finance and taxes • Controlling pollution • Equilibrium center – periphery • Migrations • Poverty • Education • Health • Crime • Segregation (social and spatial) • Leisure • Quality of life • How people interact with people and artifacts? • The New Business Models: • Public • Private • Project management • Institutional arrangements • The day to day decision making process in an evolutionary perspective • Empowerment • Direct democracy • Government • Governance • Project management • Social innovation • The state as a system engineer • Mastering ULM • The New Business Models: • Public • Private • Project management • Institutional arrangements • The day to day decision making process in an evolutionary perspective • Empowerment • Direct democracy • Government • Governance • Project management • Social innovation • The state as a system engineer • Mastering ULM Functions • Work • Budgeting • Transportatio n • Feeding • Caring • Protecting • Securing • Housing policy • Education • Leisure • Social benefits • Health care system • Migrations control Resources • Energy • Water • Data • Digital Systems • Traditions • Sociology • Technologies as enablers and enacters • Culture and traditions • Institutions and public organizations • Process modeling • Software • Tech providers • Open innovation Capabilities
- 11. Claude Rochet 11/09/2014 11 A rationale for a smart city a system architect: 3- Integration of the building blocks Soft domains Hard domains SMART city TransportationIndustry WorkHousing Sanitation EnergyWater Waste recycling Public services Health care Civic life Leisure Education Social integration GovernmentEconomy Institutional scaffolding Social life Periphery City Urban ecosystem Territory Commercial exchanges Food
- 12. Problems in smart cities ecosystem modeling Hard systems may be models thanks to the laws of physics (conservative systems) Soft systems can’t be modeled with the laws of physics (dissiptive systems) - Social siences - Big data - Multi-agents modeling The key of the success is here… … while business is there System integration, a key competency to be developed
- 13. Claude Rochet A tool to design and monitor the ecosystem: ULM (Urban Lifecycle Management© ) 13 Maturityofecosystemicproperties Development From history, social intelligence, idea, to framework Integrating off-the- shelves innovation Functional integration Technical integration Designing the engineering ecosystem Project management City 1.0 Gathering data and understanding ecosystem evolution Evaluating, correcting and upgrading Sustainable City 1.0 Integrating innovation City 2.0 Risk of collapse Unlike a product or a company, a city never dies, even if not sustainable (except in a case of collapse) Losing ecosystemic properties Permanent improvement Financial governance Socio political cycle Innovation cycle New City
- 14. Some critical points: Data 14 Legacy: How the city has evolved in the past •Hard data: statistics •Soft data: human memory => understanding the technological trajectory and social capital Present and future: Understanding how the city is evolving •Observatory for hard and soft data •Big data => Evaluating the scalability and resilience, improving social capital
- 15. Some critical points: Monitoring evolution and innovation 15 Innovation within building blocks has different speeds With smart networks innovation cycles are connected: (before, no): a permanent challenge The city dweller is the decider in last resort of the impact of any innovation on the city life: Good/Bad, useful/unusual, improve/kill
- 16. Power to technology or to citizens? Correlations => Induction Deduction =>Hypotheses Where is the brain? Existing knowledge
- 17. Some critical points: Improving social capital, bottom-up vs. top-down: The case of Christchurch (NZ) 17
- 18. Integration of disciplines 18 Levelsofcomplexity City Functions Citizens Complex systems engineering Extended P.A Political philosophy Complex system modeling Interaction and synergies Social networks and interactions Overlaps and interactions Common good as an emergence and structuring finality Ends and means of wealth creation Civic implication Polycentric Govce
- 19. The research and training program • Integrating and upgrading into smart cities issues the basics of complex systems architecture as a basic baggage for SC stake holders • Learning by doing: Applied research to the building of pilot projects • Convergence of disciplines: engineering, social sciences, urban sociology, system architecture, political philosophy, complex decision making 19
- 20. Merci! 20