Il Fuzzy Cognitive Mapping come supporto alle decisioni nei processi di policymaking urbani: un caso applicativo

Titolo Rivista ARCHIVIO DI STUDI URBANI E REGIONALI
Autori/Curatori
Anno di pubblicazione 2019 Fascicolo 2019/124
Lingua Italiano Numero pagine 26 P. 122-147 Dimensione file 222 KB
DOI 10.3280/ASUR2019-124006
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FrancoAngeli è membro della Publishers International Linking Association, Inc (PILA)associazione indipendente e non profit per facilitare (attraverso i servizi tecnologici implementati da CrossRef.org) l’accesso degli studiosi ai contenuti digitali nelle pubblicazioni professionali e scientifiche

La consapevolezza circa la complessita ambientale comporta iniziative di pianificazioneintrinsecamente connesse ad una real-time knowledge. Partendo dallepotenzialita della fuzzy logic nella trattazione dell’incertezza, questo studio utilizzale fuzzy cognitive maps per esplorare tale complessita e supportare le decisionimulti-agente. L’analisi viene svolta nell’ambito del processo di costruzione di scenariper il nuovo piano di Taranto (Italia).;

Keywords:Fuzzy cognitive maps; Decision support systems; Politiche urbane;Policy-making; Sistemi ambientali complessi

  1. Aguilar J. (2003). A Dynamic FCM Approach Based on Random Neural Networks. International Journal of Computational Cognition, 1(4): 91-107.
  2. Aguilar J. (2005). A survey about FCM papers. International Journal of computational cognition, 3(2): 27-33.
  3. Axelrod R. (1976). Structure of decision: The cognitive maps of political elites. Princento: Princeton University Press.
  4. Borri D. and Camarda D. (2006). Visualizing space-based interactions among distributed agents: Environmental planning at the inner-city scale. Lecture Notes in Computer Science, 4101: 182-191.
  5. Borri D. and Camarda D. (2011). Planning for the environmental quality of urban microclimate: A multiagent-based approach. Lecture Notes in Computer Science, 6874:129-136.
  6. Borri D., Camarda D. and De Liddo A. (2004). Envisioning environmental futures: Multi-agent knowledge generation, frame problem, cognitive mapping. Lecture Notes in Computer Science, 3190: 230-237.
  7. Borri D., Camarda D. and De Liddo A. (2008). Multi-agent environmental planning: A forum-based case-study in Italy. Planning Practice and Research, 23(2): 211-228.
  8. Borri D., Camarda D. and Pluchinotta I. (2013). Planning urban microclimate through multiagent modelling: A cognitive mapping approach. In: Luo Y., ed., Cooperative Design, Visualization, and Engineering, CDVE2013, Lecture Notes in Computer Science, 8091: 169-176.
  9. Borri D., Camarda D. and Pluchinotta I. (2014). Planning for the microclimate of urban spaces: Notes from a multi-agent approach. In Luo Y., ed., Cooperative Design, Visualization, and Engineering, CDVE 2014, Lecture Notes in Computer Science, 8683: 179-182. Borri D., Camarda D. and Grassini L. (2002) (eds.). Sustainable Planning for Soil and Water: The Mediterranean. Paris: L’Harmattan.
  10. Bossomaier T.R.J. and Green D.G. (2000). Complex Systems: Cambridge University Press.
  11. Camarda D. (2010). Beyond citizen participation in planning: Multi-agent systems for complex decision making. In: Nunes Silva C., ed., Handbook of Research on E-planning: ICTs for Urban Development and Monitoring. Hershey PA: IGI Global, 195-217.
  12. Camarda D., Rotondo F. and Selicato F. (2014). Strategies for dealing with urban shrinkage: Issues and scenarios in Taranto. European Planning Studies, 23(1): 126-146.
  13. Dickerson J. and Kosko B. (1996). Virtual worlds as fuzzy dynamic systems. In: Technology for Multimedia, 1996. IEEE Press.
  14. Fischer F. (2000). Citizens, Experts, and the Environment: The Politics of Local Knowledge: Durham: Duke University Press.
  15. Friend J.K. and Hickling A. (1997). Planning Under Pressure: The Strategic Choice Approach. Amsterdam: Elsevier.
  16. Friedmann J. (1987). Planning in the Public Domain: From Knowledge to Action. Princeton: Princeton University Press.
  17. Giordano R. and Vurro M. (2010). Fuzzy cognitive map to support conflict analysis in drought management. In: Micheal G., ed., FCM Advances in Theory, Methodologies, Tools and Applications. Berlin-Heidelberg: Springer-Verlag.
  18. Giordano R., D’Agostino D., Apollonio C., Lamaddalena N. and Vurro M. (2013). Bayesian Belief Network to support conflict analysis for groundwater protection: The case of the Apulia region. Journal of Environmental Management, 115: 136-146.
  19. Giordano R., Pagano A., Pluchinotta I., Olivo del Amo R., Hernandez S.M. and Lafuente E.S. (2017). Modelling the complexity of the network of interactions in flood emergency management: The Lorca flash flood case. Environmental Modelling and Software, 95: 180-195.
  20. Groumpos P.P. (2010). Fuzzy cognitive maps: Basic theories and their application to complex systems. In: Glykas M., eds., Fuzzy Cognitive Maps. Studies in Fuzziness and Soft Computing, 247. Berlin-Heidelberg: Springer.
  21. Jungk R. and Mullert N. (1996). Future Workshop: How to Create Desirable Futures. London: Institute for Social Inventions.
  22. Khakee A., Barbanente A., Camarda, D. and Puglisi M. (2002a). With or without? Comparative study of preparing participatory scenarios using computer-aided and traditional brainstorming. Journal of Future Research, 6: 45-64.
  23. Khakee A., Barbanente A. and Puglisi M. (2002b). Scenario building for Metropolitan Tunis. Futures, 34: 583-596.
  24. King R. (1985). The Industrial Geography of Italy. London: Routledge.
  25. Kok K. (2009). The potential of FCM for semi-quantitative scenario development, with an example from Brazil. Global Environmental Change, 19: 122-133.
  26. Kosko B. (1986). FCM. International Journal of man-machine studies, 24(1): 65-75.
  27. Kosko B. (1988). Hidden patterns in combined and adaptive knowledge networks. International Journal of Approximate Reasoning, 2(4): 377-393. Licker P.S. (1987). Fundamentals of systems analysis with application design. New York: Boyd and Fraser.
  28. Lippe M. (2011). Building on qualitative datasets and participatory processes to simulate land use change in a mountain watershed of Northwest Vietnam.
  29. Enviromental Modelling and Software, 26 (12): 1454-1466.
  30. Minucci F. (1996) (ed.). Le regioni industrializzate tra declino e innovazione. Milano: FrancoAngeli.
  31. Ozesmi U. and Ozesmi S.L. (2004). Ecological models based on people’s knowledge: A multi-step FCM approach. Ecological Modelling, 176(1-2): 43-64.
  32. Papageorgiou E. and Kontogianni A. (2012). Using FCM in environmental decision making and management: a methodological primer and an application. INTECH Open Access Publisher.
  33. Rabino G.A. (2005). Processi decisionali e territorio nella simulazione multiagente. Milano, Esculapio.
  34. Robson B.T. (1988). Those Inner Cities: Reconciling the Economic and Social Aims of Urban Policy. London: Clarendon Press.
  35. Rodwin L. and Sazanami H. (1991). Industrial Change and Regional Economic Transformation: The Experience of Western Europe. London: Taylor and Francis.
  36. Sano M., Richards R. and Medina R. (2014). A participatory approach for system conceptualization and analysis applied to coastal management in Egypt. Eniviromental Modelling and Software, 54: 142-152.
  37. Sawyer R.K. (2005). Social Emergence: Societies as Complex Systems. Cambridge: Cambridge University Press.
  38. Schachter G. (1965). The Italian South. New York: Random House.
  39. Stach W., Kurgan L. and Pedrycz W. (2008). Numerical and linguistic prediction of time series with the use of fuzzy congitive maps. IEEE Trans. Fuzzy system, 16(1): 61-72.
  40. Stach W., Kurgan L. and Pedrycz W. (2010). Expert-based and computational methods from developing fuzzy cognitive maps. In: Michael G., ed., Fuzzy Cognitive Maps Advances in Theory, Methodologies, Tools and Applications. Berlin Heidelberg: Springer-Verlag, 23-24.
  41. Stylos C. and Groumpos P. (2004). Modelling clomex systems using fuzzy coignitive maps. IEEE transactions on system, man and cybernetic, 34(1): 155-162.
  42. Tolman E.C. (1948). Cognitive maps in rats and men. Psychological Review, 55(4): 189.
  43. Townshend T. (2006). From Inner City to Inner Suburb? Addressing Housing
  44. Aspirations in Low Demand Areas in Newcastle Gateshead, UK. Housing Studies, 21(4): 501-521.
  45. Wierzbicki A., Makowski M. and Wessels J. (2000). Model-based decision support methodology with environmental applications. Dordrecht: Kluwer Academic Publishers.
  46. Wratten S., Sandhu H., Cullen R. and Costanza R. (2013). Ecosystem Services in Agricultural and Urban Landscapes. London: Wiley.
  47. Zimmermann H.J. (2010). Fuzzy set theory. Wiley Interdisciplinary Rev.: Computational Statistics, 2(3): 317-332.

, Il Fuzzy Cognitive Mapping come supporto alle decisioni nei processi di policymaking urbani: un caso applicativo in "ARCHIVIO DI STUDI URBANI E REGIONALI" 124/2019, pp 122-147, DOI: 10.3280/ASUR2019-124006