Technical, Organizational, Social and Economic (TOSE) through System Dynamics Modeling techniques (SDM)

Raffaele Giordano and Alessandro Pagano (CNR-IRSA)

Intro

Classical approaches to infrastructural reliability may be limited in describing the complexity of real systems. The resilience assessment of engineering systems, such as infrastructures requires a comprehensive approach moving beyond the merely structural dimension. The ‘culture’ (of both organizations and communities) is a key asset to describe resilience. It is thus required to broaden the perspective, with specific reference to more traditional approaches to resilience, going beyond the analysis of the structural (or ‘technical’) aspects.

Short description of the tool

An innovative approach to resilience assessment of infrastructural systems is based on the conceptualization of its four inter-related dimensions: Technical, Organizational, Social and Economic (TOSE approach). The Technical dimension of resilience refers to the ability of interconnected physical systems to perform to acceptable/desired levels. Organizational resilience is related to the organizations and institutions that manage the physical components of the systems, and is thus significantly affected by ‘culture’: it encompasses measures of capacity, planning, training, leadership, experience, and information management that may improve (or hamper) disaster-related organizational performances and problem solving. Among the influential parameters, the ability to incorporate lessons learned from past disasters, the training and the experience of personnel should be considered. The Social dimension includes population and community characteristics that render social groups either more vulnerable or more adaptable to hazards and disasters, and is strictly connected to ‘cultural’ issues as well. The Economic dimension of resilience is a key driver too, either accelerating or stopping the processes related to emergency management and recovery.

System Dynamics modeling techniques supported to identify and analyze the main elements fostering or hampering resilience according to the TOSE framework. After the definition of a conceptual model of infrastructural resilience, the model was built and tested to evaluate the impact of actions and strategies for resilience improvement on the dynamic evolution of the system. Specific reference was made to drinking water supply systems. Finally, it has been used to identify critical feedbacks, and to evaluate their influence on the implementation of policies aiming to enhance infrastructural resilience, assessing their evolution with time.

EDUCEN application of the tool

Resilience assessment was performed with specific reference to L’Aquila Case Study, implementing the TOSE approach on drinking water supply through System Dynamics Modeling techniques (SDM). L’Aquila case study was used to collect and structure the knowledge needed for model building and validation. The model was developed investigating the evolution of drinking water supply service in the aftermath of the earthquake of 2009. It was also used in order to perform a scenario analysis: this may support decision-makers to understand the impact of different strategies, conditions, assumptions on the response of the system. The key outcome of modeling is the analysis of the evolution of water deficit with time, which is a way of interpreting and representing the resilience. The comparative analysis of multiple scenarios helps describing the impact of different states of specific variables on the model outcomes.

The key advantage of the proposed model is connected to the possibility of quantitatively modeling resilience according to the TOSE approach. The joint impact of Technical, Organizational, Social and Economic aspects is taken into account and described.

Building, developing and using a SD model to assess the infrastructural resilience according to the TOSE framework requires some key phases.

  1. Knowledge elicitation: scientific knowledge available in literature should be integrated with expert knowledge, to be elicited through semi-structured interviews. Literature evidences help developing the conceptual model and identifying cause-effect relationships. Expert knowledge support in better identifying such relationships and determining the influences among the different dimensions of resilience.
  2. Conceptual modeling: the conceptual model definition should support representing the sum of cause-effect chains influencing the evolution of infrastructural resilience in case of a disaster. The experts should integrate the conceptual model, adding or deleting variables and modifying links. The modelling process ends when no new concepts and/or relationships emerge after a number of interviews. Specifically, the experts should identify the main variables and connect them according to the expected influences. They should also provide a quantitative interpretation of all the considered variables (even non-physical ones) and of their potential states. Finally, they should describe the dynamic evolution of the variables, the main causes of changes and the potential effects.
  3. SDM model building: the conceptual model is the basis for the stock-and-flow model. The key variables should be modeled as stocks, according to the SDM principles. Another subset of variables are instead defined as ‘conveyors’. A few of them are related to either external conditions or to potential actions/policies. The structure of the model should be then discussed and validated with experts and, in case data are available, using a quantitative calibration.

Example of application in other cases.

Kongar et al. (2017) recently proposed a comparison between the earthquake-induced physical and functional impacts to different infrastructures systems in L’Aquila and in Christchurch. The main aspects of the TOSE approach are considered. The SDM model was developed specifically for L’Aquila CS within EDUCEN activities, without being implemented in other CS. Nevertheless, the methodological approach is broad enough to be replicated with minor changes and adaptations in different cases, conditions, and even on various infrastructural systems

Referring to L’Aquila CS, both the structure of the model and the results provided, were discussed with a sub-group of experts, who reviewed the variables and their relationships. Additionally, a quantitative calibration was performed. A few key stocks were selected for calibration (e.g. ‘water deficit’), and their dynamic evolution was simulated. Experts were asked to identify for each variable a scatter plot to build an expected trend. The comparison between the expected and the predicted trend allowed the calibration of the key equations used in the model.

The model was built in order to deal separately with the four basic dimensions of resilience. Then the reciprocal influences among variables were identified. For instance, some capabilities reflecting organizational culture (e.g. the availability of human resources, the availability of a good knowledge concerning the infrastructure and the environment) which may have a direct influence on system resilience (i.e. they support a quick and effective response to technical issues) are explicitly included. The role of the social dimension was identified as well, focusing on the characterization of how behaviors, attitudes and awareness of the served population may either support or hamper resilience.

The results were discussed in details with the experts, and clearly underlined that the role of ‘non-structural’ measures on soft infrastructural system might be important as well as structural ones to increase resilience. Particularly, acting on several ‘cultural’ issues related to both individual (e.g. people awareness) and organizational features (e.g. cooperation, training level, knowledge) may have a benefit comparable with the one associated to the implementation of structural measures.

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