Mapping the people and the organizations involved in DRR
Raffaele Giordano and Alessandro Pagano (CNR-IRSA)
Enhancing the coordination effectiveness in case of emergency among the different responders, when a fast and efficient response is required, is the main scope of several studies aimed at overcoming the main organizational factors hindering the cooperation. Up to now, much more research has been carried out with respect to what happens within the same organisation under stress, while knowledge on what happens when multiple organisations need to coordinate in unison to make the best of their capacity in a highly stressful environment is still limited. Most of the efforts carried out for enhancing coordination effectiveness were meant to innovate the information technology for internal and external communication, information production and sharing. Empirical evidences demonstrate the need to shift from innovating information production and management technologies towards enhancing the interaction processes among the different actors in emergency management. Interaction represents the mechanism allowing the different actors to interpret their environment, to achieve a satisfactory shared understanding of the situation, and to cope with the organizational and individual improvisation needed to deal with extreme events. Moreover, interactions allow to mitigate the conflicting interpretation of information about emergency due to differences in knowledge belief, customs and assumptions. Therefore, a better understanding of the complex network of interactions activated during the different DRR phases is of utmost importance.
Interaction network and DRR
Most of the efforts carried out in the field of organizational studies demonstrate that the network of interactions influencing the performance of a certain organization is more complex than the simple network involving the different agents. An organization can be modeled and characterized as a set of interlocked networks connecting four key entities operating within the organization, i.e. people, knowledge, resources and tasks. Therefore, in order to better comprehend this complex network of interaction, analytical methods are required capable to conceptualize not only the attributes of these entities, but also the set of relations and ties among them. The Meta-matrix conceptual framework could be implemented to this aim. This approach conceives the organization as composed by: social network, knowledge network, resources network, assignment network, information network, resources requirement and knowledge requirement.
How to map and analyse the map of interactions
The map and the analysis of the network requires collecting data about how the different agents interacts with each other, how they accessed the information and used it in order to perform some tasks. Two kinds of approaches can be implemented to this aim, i.e. the quantitative and the qualitative approach. The former requires numerical assessment about the different interaction. That is, how many times agent A interacts with agent B; how many times the information C is used by agent A, etc. Collecting this data is often difficult when the interaction is not leaving a tack – e.g. interaction via email, database access, etc. The qualitative approach is based on the elicitation and structuring of experts’ experiences and knowledge. It allows to overcome the limits due to the lack of data. The integration among social mapping and the critical event analysis facilitates the interaction with the experts and, thus, the knowledge elicitation phase.
How to map the network of interactions
Most of the methodologies aiming at mapping the network of interactions among people limit their analysis to the social network, that is, to map “who talks to, works with, and reports to whom”. According to the common formulation social network are developed in terms of ties among persons. The improvement of emergency management requires the adoption of an organizational perspective for what concerns the information sharing processes and the cooperative task allocation and performance. Specifically, temporary multi-organizations are created for improving the coordination efforts during the emergency management. This composite agent has to be considered as a network whose behaviour is a function of complex processes for combining and generating collective outcomes. In this perspective, networks are more ubiquitous than those simply social network, and entities besides agents can be networked together.
The adopted methodology for mapping the interactions during an emergency is based on the conceptualization of an organization as a set of interlocked networks connecting entities such as people, knowledge resources, tasks and groups. This meta-network representation effectively combines the knowledge level perspective, the social network perspective and the coordination management perspective.
The following table shows the meta-matrix approach.
|Agent||Social network: map of the interactions among the different institutional actors in the different DRR phase||Knowledge network: identifies the relationships among actors and information (Who does manage which information? Who does own which expertise?)||Assignment network: defines the role played by each actor in the DRR phases|
|Knowledge||Information network: map the connections among different pieces of knowledge||Knowledge requirements network: identifies the information used, or needed, to perform a certain task in the DRR|
|Tasks||Dependencies network: identifies the work flow. (Which tasks are related to which)|
Table 1: Meta-matrix framework showing the connections among the key entities of social network (adapted from Carley, 2005)
The details of the methodology for mapping the interaction among the main entities – i.e. agent, knowledge and tasks – is described in the following.
The first entity to be analysed is the Agent x Agent matrix, at the basis of the social network. Table 2 shoes an example of the social network.
Table 2: Agent x Agent matrix
In the previous matrix, Wij represents the importance of the interaction between the agent Ai and the agent Aj as perceived by the agent Aj. Similarly, the value of Wji refers to the strength of the interaction between the agent Ai and the agent Aj as perceived by the agent Aj. The weights can be assessed accounting for the experts opinion. In this work, we use the term “experts” to indicate policy-makers and official responders involved in the emergency management. The experts’ knowledge was elicited through a series of individual semi-structured interviews. A storyline approach (SA) was implemented. Referring to a specific episode of emergency management, participants were required to describe the sequence of actions implemented in order to achieve their goals in the emergency management, the information used and the other agents with whom they interacted.
The first issue to be addressed concerned the selection of the experts to be involved in this phase. In order to minimise the selection bias and the marginalization of stakeholders a top-down stakeholder identification practice, which is referred as ”snowballing” or ”referral sampling”, was implemented (Harrison & Qureshi, 2000; Prell et al., 2008). The selection process started with the actors mentioned in the official protocols of intervention. The preliminary interviews carried out with these agents allowed us to widening the set of stakeholders to be involved.
The results of the interviews were structured in individual Fuzzy Cognitive Maps (FCM) (figure 1). The structuring phase allowed us to translate the narratives into useful inputs for the SNA phase.
The interactions with the other agents can be activated through both the sharing of information and the cooperation to perform specific tasks. Each link in the FCM is characterized by a weight, which describes the stakeholders’ perception of the importance of that connection. The weight of the link agent-information describes the interviewee’s perception about how crucial is the agent to obtain the needed information. Similarly, the weight of the link information-task represents the role played by the information in facilitating the implementation of that specific task.
The individual FCMs were also used to define the other matrices. For instance, the individual i-th Agent x Knowledge matrix was obtained considering the weights assigned by the i-th actor to the different agent-information connections. The Agent x Knowledge matrix for the i-th agent is represented in Table 3.
Table 3: Knowledge network matrix for the i-th agent.
The overall Agent x Knowledge matrix was obtained as the sum of the individual matrices. Similar processes were implemented to develop the Agent x Tasks, Knowledge x Knowledge, Knowledge x Tasks and Tasks x Task matrices. In order to facilitate the elicitation of the participants’ opinions about the importance degree, fuzzy linguistic variables can be defined. This method requires the identification of the linguistic labels used by the interviewees to describe the importance of the connections. The weights in the matrixes are used to develop the network. They represents the strength of the ties between two entities. Figure 2 and 3 shows, respectively, the social network and the knowledge network developed for the Lorca CS. network.
The direction of the links indicate which agent mentioned the interaction. For instance, the link between L.EM2 and L.OP2 shows that L.EM2 perceived itself interacting with L.OP2, but not vice-versa. The thickness of the links represent the weights assigned by the different actors during the knowledge elicitation phase. Figure 3 shows the knowledge network for the Lorca CS.
The map demonstrates that there is no exclusivity in the agent-knowledge interactions, namely there is no actor exclusively owning pieces of knowledge. Therefore, cooperation among the different actors is crucial to overcome the fractured nature of the information system. The combination of the different networks allowed to map the complex interactions among the main elements activated during the flood emergency, i.e. agents, knowledge and tasks (figure 4).
Figure 4 shows the actual complexity of the interaction mechanisms supporting the emergency management. Failure in this network – i.e. lack of an information, missing cooperation for task implementation, etc. – could provoke uncontrollable cascading effects leading to the failure of the whole emergency management process. Therefore, it becomes crucial for the emergency managers to enhance their comprehension of this complexity, in order to implement actions aiming to increase the effectiveness of the emergency management network and to reduce its vulnerability.
The results of the analysis can support emergency managers in different ways. Firstly, the SNA allows to identify the actors that, because of their role in the network, could play a central role in speeding up the information sharing process. These actors should have easy access to the required information. Secondly, the SNA allows identifying the reasons of potential conflicts hampering the cooperative emergency management - i.e. information that should be shared between two different actors in order to facilitate the task implementation, but it is currently owned by one actor with limited capability/willingness to share. Thirdly, the SNA allows assessing the congruence between the information needed for performing certain tasks and the information actually accessible to the actors performing those tasks.
Concluding, the SNA results could provide useful information for improving the protocol of intervention in case of emergency.
How to analyse the map of interactions
Different kinds of analysis can be carried out through the implementation of graph network theory to the network of interactions. The results of the analysis can be used to enhance the effectiveness of the emergency management network, through the identification of key elements – i.e. key actors, key knowledge and key tasks – and main vulnerabilities, that is, the characteristics of the network that could lead to the failure of the emergency network.
Two different levels of analysis can be performed, i.e. node-level metric analysis and network-level metric analysis. The former allows for an analysis of the complexity of the network surrounding each node. This kind of analysis is used to identify the key elements in the network. The network-level analysis allows for better comprehension of the complexity of the network and makes it possible to identify key vulnerabilities. The results can be used to support the development of strategies aiming at improving emergency management through network performance. Two different groups of actions can be implemented to this aim. On the one hand, actions can be defined aiming at putting the key elements at the core of the emergency management protocols – e.g. enhancing the sharing of key information, emphasizing the role of key actors, etc. On the other hand, actions can be identified aiming at reducing the key vulnerability – e.g. increasing the speed of information by increasing the capabilities of the central agents to have access to crucial information.
The following tables describe the different measures, their meaning and how to use them to assess the performance of emergency management network.
|Network||Network measure||Assessment||Meaning in DRR|
|Agent x Agent||Total degree Centrality||Those who are ranked high on this metrics have more connections to others in the same network.||Individuals or organizations who are ‘in the know’ are those who are linked to many others and so, by virtue of their position have access to the ideas, thoughts, beliefs of many others.|
|Betweenness centrality||The betweenness centrality of node v in a network is defined as: across all node pairs that have a shortest path containing v, the percentage that pass through v.||Individuals or organizations that are potentially influential are positioned to broker connections between groups and to bring to bear the influence of one group on another or serve as a gatekeeper between groups.|
|Agent x Knowledge||Most knowledge||Assess the number of links between a certain agent and the different pieces of knowledge in the network.||An agent with a high value of most knowledge has access to a great variety of knowledge to be used in case of disaster.|
|Agent x Task||Most task||Assess the number of links between a certain agent and the different task that need to be carried out in case of emergency.||An agent with a high degree of most task plays a crucial role in the network due to her/his capability in performing different tasks.|
|Knowledge x Knowledge||Total degree of centrality||It calculates the importance of a certain piece of information according to the number of connected links.||The most central pieces of knowledge are those whose availability is crucial to make the other pieces of knowledge accessible.|
|Closeness centrality||Closeness is the inverse of the sum of distances in the network from a node to all other nodes.||The closeness centrality measure allows us to identify the information that could facilitate the process of information sharing.|
|Knowledge x Task||Most task||Assess the number of links between a certain piece of knowledge and the different task that need to be carried out in case of emergency.||The pieces of knowledge with a high value for this measure are fundamental for the effectiveness of the network, since without them a high number of tasks will be not carried out.|
|Task x Task||Total degree of centrality||It analyses the complexity of the connections within the task X task network.||Tasks with high degree of centrality are those that have to be carried out in order to allow the executions of the other tasks.|
Table 1: Graph Theory measures for key element detection
Similarly, different graph theory measures can be implemented in order to assess the network vulnerability. That is, those elements that could lead to failures of the network, lower performance, reduced adaptability, reduced information gathering, etc. Considering the complexity of the emergency network, in this work the vulnerability elements were identified though a combination of different measures, as described in the table 2.
|Network||Network measures||Meaning in emergency management|
|Agent x AgentAgent x Knowledge||Total centrality degreeMost knowledge||An actor with a high degree of centrality and a low most knowledge degree represents a vulnerability because, although she/he a central position in the network, she/he has a limited capability to enable information sharing.|
|Agent x AgentAgent x Knowledge||Betweennes centralityMost knowledge||An actor with a high degree of most knowledge and a low betweennes degree represents a vulnerability because she/he is not capable to share with the others the pieces of knowledge she/he has access to.|
|Agent x AgentAgent x Task||Total centrality degreeMost task||An actor with a high degree of most task and a low centrality degree represents a vulnerability because, although she/he is required to carry out important tasks, she/he is quite isolated and cannot be supported by the others during an emergency.|
|Agent x KnowledgeKnowledge x Task||Most knowledgeMost task||A piece of knowledge poorly shared within the network (low most knowledge) represents a vulnerability if its access is crucial to carry out important task (high most task).|
|Agent x KnowledgeKnowledge x Knowledge||Most knowledgeCloseness centrality||A piece of knowledge with a high degree of closeness but poorly shared (low degree of most knowledge) represents a vulnerability since it could hamper the process of information sharing.|
|Agent x TaskTask x Task||Most taskCentrality degree||A task with a high centrality degree and with low most task degree represents a vulnerability because, although its importance, there is no, or very limited cooperation to guarantee its effectiveness.|
|Agent x AgentAgent x KnowledgeAgent x Task||Cognitive load||This measure takes into account the number of other agents, knowledge and tasks an agent needs in order to perform its own task. High cognitive load represents a vulnerability.|
Table 2: Measures for the detection and analysis of key vulnerability in the emergency management network
Besides the node-level analysis, the map of interactions can be analyzed at network level. Table 3 describes the measures that can be implemented in order to assess the effectiveness of the network in emergency management.
|Network measure||Graph theory||Meaning in emergency management|
|Communication congruence||Measure to what extents agents communicate when and only when it is needful to complete tasks. Higher congruence occurs when agents don’t communicate if the tasks don’t require it.||Communication overload could reduce the effectiveness of the emergency management.|
|Knowledge congruence||Measures the similarity between what knowledge is assigned to tasks via agents, and what knowledge is required to do tasks. Perfect congruence occurs when agents have knowledge when and only when it is needful to complete tasks.||Having access to unnecessary knowledge could create “noises” during the emergency management.|
|Density||The actual number of network edges versus the maximum possible edges for a network N.||A dense network support the sharing of knowledge and information, leading to the creation of a common understanding.|
|Hierarchy||The degree to which a square network N exhibits a pure hierarchical structure.||In a hierarchical network, diversity of point of views and ideas is highly improbable. This negatively affect the richness of the knowledge co-production process,|
|Negotiation Knowledge||The extent to which personnel need to negotiate with each other because they lack the knowledge to do the tasks to which they are assigned.||Long negotiation processes needed to get the required information could reduce the effectiveness of the emergency management.|
|Speed average||The average communication time between any two agents who can communicate via some path.||Emergency management requires fast communication among the different agents.|
When and where to use social network modelling
The experiences carried out in EDUCEN project demonstrate the usefulness of social network modelling in detecting the actual role of the different actors and information in DRR. The results of EDUCEN activities in L’Aquila and Lorca CS showed the inadequacy of the official protocol of interaction for describing the actual network of interaction. The official protocols fail to account for the informal interactions activate during an emergency, and for the role of lay people and local knowledge in DRR.
Guidelines for when and where to use social network modelling
Social network modelling (SNM) could facilitate a better understanding among emergency managers of the complex network of interactions taking place during an emergency. In doing so, SNM enhances the coordination mechanism among the different responders and, thus, the effectiveness of the emergency management procedure.
The SNM emphasizes the need to adopt a multi-agency approach for the analysis of the network of interactions. Therefore, the emergency management multi-organization can be conceptualized as composed by interconnected meta-networks linking the main entities in organizational management, i.e. the agents, the knowledge and the tasks.
Starting from these premises, this section provides emergency managers and practitioners with guidelines for choosing SNM as a tool be adopted in order to enhance the emergency management process.
The experiences carried out in EDUCEN case studies, L’Aquila and Lorca, demonstrate the usability of SNM for addressing three different issues related to the coordinated interactions in emergency management:
- Identify all the actors that need to be integrated in the emergency management procedures;
- Assess the level of accessibility of crucial information in case of emergency;
- Evaluate the congruence between the information sharing process and the cooperative tasks performance.
Identification of the needed actors
The official protocol of intervention describe only part of the complex network of interactions activated during an emergency. Other actors play a crucial role, although they are not officially integrated in emergency management procedures. Moreover, the results of the EDUCEN activities demonstrate that the importance of particular responders – either institutional or not institutional – is related not only to their official role in the protocol of intervention. Rather influenced by their capability to spread information within the network of interactions, and to share resources and tasks. The EDUCEN results show that the actors at the centre of the map of interaction are those that can enable the collaborative emergency management. They can increase the speed of communication, facilitating the transfer of pieces of information from one side of the network to the other. I.e. these actors could act as interface between the institutional systems of responders and the community. Due to their wide web of interaction and their access to knowledge and information, these actors represent an effective channel for information sharing. Specifically, EDUCEN results show that these actors can increase the accessibility to institutional information. Often this result is achieved through the activation of informal interaction channels. Therefore, the SNM, has to be based on the collection of narratives about how the different actors actually interacted during the emergency.
The use of specific approaches (e.g. storyline approach) increases the insight in the sequence of events during the emergency management. Particularly, it supports: i) a general description of the system being investigated (e.g. procedures/protocols and key actors involved); ii) definition of a scenario; iii) determination of the sequence of events during a storyline, focusing on actions and responses implemented by each actor, information used and interactions; iv) analysis of the impacts of the external pressure and the effects of actions of local authorities and community members.
Limiting the analysis to the institutionally defined interactions could be misleading. The following table describes the roles that can be played by the different actors in the network.
|Role of the actors||Meaning in the emergency management|
|Central actor||Individuals or organizations who are ‘in the know’ are those who are linked to many others and so, by virtue of their position have access to the ideas, thoughts, beliefs of many others. Individuals who are ‘in the know’ are identified by degree centrality in the relevant social network.|
|Information hub||Individuals or organizations that act as hubs are sending information to a wide range of others each of whom has many others reporting to them.|
|Authority||Individuals or organizations that act as authorities are receiving information from a wide range of others each of whom sends information to a large number of others|
|Gatekeeper (betweenness centrality)||Individuals or organizations that are potentially influential are positioned to broker connections between groups and to bring to bear the influence of one group on another or serve as a gatekeeper between groups. This agent occurs on many of the shortest paths between other agents.|
|Agent with most knowledge||Individuals or organizations that have more expertise or are associated with more types of knowledge than are others.|
|Agent with most tasks||Individuals or organizations that are assigned to more tasks or are associated with more types of tasks than are others|
The EDUCEN results demonstrate the usability of SNM to assess the actual importance of the information available within the network and its accessibility by the different actors. The basic assumption is that if crucial information is not accessible to central actors then cooperative tasks performance could be hampered. Therefore, it is of utmost importance, for an effective emergency management process, to enhance the comprehension of the actual use and accessibility of the different information. The EDUCEN case studies show that one of the most important barriers hampering the effective coordination of emergency management operations is the fractured nature of information in distributed system, where the state of the system itself can be perceived indirectly, through an effective information exchange by collaborative agents. In order to overcome this barrier, SNM allows emergency managers to have a clear idea about who has what information, where information is located in the network and how it is used. The following table shows the results of SNM analysis that can help to improve the sharing and accessibility of knowledge and information.
|Knowledge and SNM||Meaning in emergency management|
|Central information||The central information is crucial because it allows other pieces of information to be used in the emergency management.|
|Most used information||The SNM allows analysing the connections between information and tasks. Therefore, the most used pieces of information are those that are used in order to perform a high number of tasks.|
|Most shared information||The SNM allows analyzing the connection between information and actors. I.e. who knows what. The most shared information are those with a high level of accessibility by a high number of agents.|
The results of this analysis allows to enhance the information management in case of emergency: central information and/or most used information should be accessible to a high number of actors. This requires the timely availability, in a proper format, of the key information to agents having the trust of other agents and an important set of interconnections. The lack of this property represents a vulnerability in the network.
Congruence between information sharing and task cooperative performance
Finally, the SNM could be used to enhance the sharing of information, in order to improve the cooperation in task performance during emergency management. Specifically, SNM allows to identify the tasks that are shared among a high number of actors, and the tasks that require access to a high number of information.
|Tasks and SNM||Meaning in emergency management|
|Central task||The central task is crucial because it allows other tasks to be performed. These tasks should have the highest priority in the protocol of intervention.|
|Most shared tasks||The official protocols of interventions often ignore the fact that actors are carrying out some tasks informally. The SNM allows one to analyse the actual level of task sharing and to identify the tasks that require a high degree of coordination.|
|Highly demanding tasks||The analysis of the Task-knowledge network allows SNM to identify the tasks requiring a high volume of different information in order to be performed. If these tasks play a crucial role in the emergency management, assuring the accessibility to the needed information should be considered as crucial.|
Network analysis in L’Aquila CS
A series of interviews with the key responders were carried out in order to map the interactions activated among the different responders during the 2009 earthquake emergency. The analysis of the map allowed to detect the crucial role of the local emergency management team in facilitating the information sharing process.
Network analysis in Lorca CS
The map of the interaction network in Lorca CS refers to the San Venceslao flood episode occurred in 2012. Institutional and non-institutional responders were involved in the analysis. The map highlights the crucial role played by the leaders of the community. Suggestions were made in order to better integrate community in the information sharing process.