Participatory modelling (PM) techniques aim at involving stakeholders and local communities’ knowledge to support risks assessment management in social-ecological systems (SES). Understand SES complexity means consider unique socio-economic and cultural values, direct and indirect experiences, political characteristics that have influenced the formation of a community’s risk understanding and local knowledge. Comprehending the complexity of these interactions could help to exchange information and knowledge, leading to a better comprehension of the problem formulation through social learning processes and to facilitate conflict resolution. Evidences demonstrated that there is the need to require not only a deep understanding of the main physical phenomena to be addressed, but also a knowledge about stakeholders’ type, level of cooperation between different stakeholders and their risk perception. Once the problem is well understood, and stakeholders included in the process, it is possible to find more efficient adaptive management solutions to enhance the resilience of the system. For this reason, to improve risk management processes, it should be considered physical risk and social response in an integrated way. Starting from a literature review, the aim of this work is to demonstrate the role of PM techniques in adaptive risk management to collect knowledge and improve SES management. Specifically, coastal systems (CS) have been analysed. CS are particularly vulnerable to climate change negative impacts. In this sense, it is important to understand that vulnerability is socially constructed and not only determined by the occurrence of a physical event.

Integrating participatory modelling in risk management / Motta Zanin, Giulia; Santoro, Stefania. - ELETTRONICO. - 4:(2018), pp. 139-146. (Intervento presentato al convegno 10th International Conference INPUT 2018 tenutosi a Viterbo, Italy nel September 5-8, 2018) [10.6093/978-88-6887-048-5].

Integrating participatory modelling in risk management

Giulia Motta Zanin
;
Stefania Santoro
2018-01-01

Abstract

Participatory modelling (PM) techniques aim at involving stakeholders and local communities’ knowledge to support risks assessment management in social-ecological systems (SES). Understand SES complexity means consider unique socio-economic and cultural values, direct and indirect experiences, political characteristics that have influenced the formation of a community’s risk understanding and local knowledge. Comprehending the complexity of these interactions could help to exchange information and knowledge, leading to a better comprehension of the problem formulation through social learning processes and to facilitate conflict resolution. Evidences demonstrated that there is the need to require not only a deep understanding of the main physical phenomena to be addressed, but also a knowledge about stakeholders’ type, level of cooperation between different stakeholders and their risk perception. Once the problem is well understood, and stakeholders included in the process, it is possible to find more efficient adaptive management solutions to enhance the resilience of the system. For this reason, to improve risk management processes, it should be considered physical risk and social response in an integrated way. Starting from a literature review, the aim of this work is to demonstrate the role of PM techniques in adaptive risk management to collect knowledge and improve SES management. Specifically, coastal systems (CS) have been analysed. CS are particularly vulnerable to climate change negative impacts. In this sense, it is important to understand that vulnerability is socially constructed and not only determined by the occurrence of a physical event.
2018
10th International Conference INPUT 2018
978-88-6887-048-5
Integrating participatory modelling in risk management / Motta Zanin, Giulia; Santoro, Stefania. - ELETTRONICO. - 4:(2018), pp. 139-146. (Intervento presentato al convegno 10th International Conference INPUT 2018 tenutosi a Viterbo, Italy nel September 5-8, 2018) [10.6093/978-88-6887-048-5].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/190496
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