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Understanding Smart Cities as Social
Machines
Dirk Ahlers, Patrick Driscoll, Erica Löfström, John
Krogstie, Annemie Wyckmans
NTNU – Norwegian University of Science and
Technology
SOCM2016 Workshop @ WWW2016
Cities and Smart Cities
• Cities are interesting and complex
• Mixing place for people and technology
• Increased adaptation pressure
• Reinvention, sustainability
• Data-driven operation
• Smart City concepts as a driver
• Integration of ICT into services,
operation, and planning
Our definition of Smart Cities
• Smart Sustainable Participatory Liveable Cities
• Energy-Efficient, Climate-Resilient, Health-
Promoting, Inclusive & Attractive by default
Megamachines
• [Mumford 67] City as a
singular convergence of
technics, politics,
civilization
• Complex social process
• City-as-megamachine
• [May 2000] Information
Society as mega-machine
Urban Machinery
• Urban Machinery [Hård & Misa
2008]
• Cities as knowledge hubs
• Driving dissemination, research,
innovation
Citizen involvement
• [Arnstein 69] Ladder of
participation
[https://commons.wikimedia.org/wiki/File:Ladder_of_citizen_participation,_Sheey_Arnstein.tif
User:DuLithgow]
Criteria for Social Machines
• Social processes, merged with computation,
happening on the Web
• “social participation with machine-based
computation” [Smart et al. 2014]
• “Web-based socio-technical systems in which
the human and technological elements play
the role of participant machinery” [Smart,
Shadbolt 2014]
(1) Social Processes
• Cities as socio-technical organisms
• Society arises from social processes
• Cities are crystallisation points of societal issues
and transformation processes.
• Modern city planning approaches value such
influences
• Cities run complementary to human social
processes and within human social and societal
environments
• Citizen involvement
(2) Machine-based computation
• City operation as a background process
• IoT as enabler
• Integration of separate systems that make up
the city
• Official, inofficial, and global systems
– Cf. e-services, neighbor meetups, OSM
(3) Web-based
• Smart Cities are partially Web-based
• Features and aspects arise on the Web
• Smart Cities can be managed through IoT
• Not all of the Social Machine that is a Smart City
is very obvious on the Web
• Even factors that operate rather invisible under
the surface are used to make an impact
• Enables participatory and collaborative aspects
Related SM
• Lots of single-site social machines
• Towards higher complexity
– (e)Government [Tiropanis et al. 2014]
– Ecosystems
– The Web
Complexity
• Smart City as a system of systems
• Smart City as the Social Machine of an
ecosystem of Social Machines
• System surfaces are manifestations of the city
• Multiple abstraction levels
• Combination of official/
inofficial and local/global
social machines
[https://commons.wikimedia.org/wiki/File:Social_Network_Analysis_Visualization.png User:SlvrKy]
Synthesis
• Smart Cities as a complex ecosystem at different
levels of components, systems, and system- of-
systems
• Social Machines evolved in interaction between
system providers, users and machines
• Smart City as a loosely integrated set of Social
Machines in a digital ecosystem
• A semi-controlled infrastructure with a number of
data sources, application services, digital
infrastructures needed to bring data and services
to the users, and the users and citizens
Applications
• Transformation to Smart Cities adds
complexity
• Bridge computer science and urban
planning/architecture approaches
• Interdisciplinary understanding
• Drive observatory and Web Science/City
science approaches
• Urban computation and city analytics
Conclusion
• Smart City as Social Machine
– Thinking about complex urban issues
– Inclusion of citizens
– Improved social and societal view of the city
– Bridging gaps within interdisciplinary teams
[http://trondheim2030.no/2015/11/09/ny-tredimensjonal-modell-av-trondheim-gjor-det-
enklere-a-planlegge-framtidas-by/]
Contact
search://Dirk Ahlers
geo: 63°25‘10“N 10°24‘9“E
@dirkahlers
dirk.ahlers@idi.ntnu.no
http://smartsustainablecities.org/
Let’s talk!

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Understanding Smart Cities as Social Machines

  • 1. Understanding Smart Cities as Social Machines Dirk Ahlers, Patrick Driscoll, Erica Löfström, John Krogstie, Annemie Wyckmans NTNU – Norwegian University of Science and Technology SOCM2016 Workshop @ WWW2016
  • 2. Cities and Smart Cities • Cities are interesting and complex • Mixing place for people and technology • Increased adaptation pressure • Reinvention, sustainability • Data-driven operation • Smart City concepts as a driver • Integration of ICT into services, operation, and planning
  • 3.
  • 4. Our definition of Smart Cities • Smart Sustainable Participatory Liveable Cities • Energy-Efficient, Climate-Resilient, Health- Promoting, Inclusive & Attractive by default
  • 5. Megamachines • [Mumford 67] City as a singular convergence of technics, politics, civilization • Complex social process • City-as-megamachine • [May 2000] Information Society as mega-machine
  • 6. Urban Machinery • Urban Machinery [Hård & Misa 2008] • Cities as knowledge hubs • Driving dissemination, research, innovation
  • 7. Citizen involvement • [Arnstein 69] Ladder of participation [https://commons.wikimedia.org/wiki/File:Ladder_of_citizen_participation,_Sheey_Arnstein.tif User:DuLithgow]
  • 8. Criteria for Social Machines • Social processes, merged with computation, happening on the Web • “social participation with machine-based computation” [Smart et al. 2014] • “Web-based socio-technical systems in which the human and technological elements play the role of participant machinery” [Smart, Shadbolt 2014]
  • 9. (1) Social Processes • Cities as socio-technical organisms • Society arises from social processes • Cities are crystallisation points of societal issues and transformation processes. • Modern city planning approaches value such influences • Cities run complementary to human social processes and within human social and societal environments • Citizen involvement
  • 10. (2) Machine-based computation • City operation as a background process • IoT as enabler • Integration of separate systems that make up the city • Official, inofficial, and global systems – Cf. e-services, neighbor meetups, OSM
  • 11. (3) Web-based • Smart Cities are partially Web-based • Features and aspects arise on the Web • Smart Cities can be managed through IoT • Not all of the Social Machine that is a Smart City is very obvious on the Web • Even factors that operate rather invisible under the surface are used to make an impact • Enables participatory and collaborative aspects
  • 12. Related SM • Lots of single-site social machines • Towards higher complexity – (e)Government [Tiropanis et al. 2014] – Ecosystems – The Web
  • 13. Complexity • Smart City as a system of systems • Smart City as the Social Machine of an ecosystem of Social Machines • System surfaces are manifestations of the city • Multiple abstraction levels • Combination of official/ inofficial and local/global social machines [https://commons.wikimedia.org/wiki/File:Social_Network_Analysis_Visualization.png User:SlvrKy]
  • 14. Synthesis • Smart Cities as a complex ecosystem at different levels of components, systems, and system- of- systems • Social Machines evolved in interaction between system providers, users and machines • Smart City as a loosely integrated set of Social Machines in a digital ecosystem • A semi-controlled infrastructure with a number of data sources, application services, digital infrastructures needed to bring data and services to the users, and the users and citizens
  • 15. Applications • Transformation to Smart Cities adds complexity • Bridge computer science and urban planning/architecture approaches • Interdisciplinary understanding • Drive observatory and Web Science/City science approaches • Urban computation and city analytics
  • 16. Conclusion • Smart City as Social Machine – Thinking about complex urban issues – Inclusion of citizens – Improved social and societal view of the city – Bridging gaps within interdisciplinary teams [http://trondheim2030.no/2015/11/09/ny-tredimensjonal-modell-av-trondheim-gjor-det- enklere-a-planlegge-framtidas-by/]
  • 17. Contact search://Dirk Ahlers geo: 63°25‘10“N 10°24‘9“E @dirkahlers dirk.ahlers@idi.ntnu.no http://smartsustainablecities.org/ Let’s talk!