key: cord-0058584-kd3i8fuu
authors: Bottero, Marta; Datola, Giulia; De Angelis, Elena
title: Exploring the Redundancy Capacity Through a System Dynamics Approach
date: 2020-08-26
journal: Computational Science and Its Applications - ICCSA 2020
DOI: 10.1007/978-3-030-58814-4_26
sha: 7c3491b285c644d8656e95de8b57311be77b76d1
doc_id: 58584
cord_uid: kd3i8fuu

Current urban agenda is interested in developing urban strategies to enhance urban resilience for cities. This paper examines the concept of urban resilience applied in the context of urban planning with specific attention to which are the capacities that make urban systems resilient. The knowledge and the understanding of these capacities are fundamental to set up suitable strategies. However, only few frameworks consider urban resilience and its capacity within the complexity of urban system. Through an in-depth literature review, this paper aims at providing a methodological framework both to recognize these capacities and analyze which are the main factors that mainly contribute to the achievement of these ability. The paper illustrates the first step of the application of a System Dynamics approach to analyze the redundancy capacity in order to define which are the key variables to enhance this capacity and also to determine which can be the impacts on the urban systems as a whole of the redundancy.

The concept of urban resilience is actually a fundamental objective of the new urban agenda [1] [2] [3] [4] [5] [6] . Urban resilience concept is defined as the capacity of the city to adapt, change and absorb in face to a disturbance, within all its dimensions [7] . Global community is interested in enhancing urban resilience for cities, to make them able to face with both natural and man-made hazards [8] . An increasing attention in applying resilience in urban planning is demonstrated by both the growing number of different campaigns [1] [2] [3] and by the development of new evaluation frameworks to measure urban resilience [9] [10] [11] [12] [13] [14] [15] [16] .

Different resilience approaches [17] [18] [19] [20] (e.g. engineering, ecological, economical) describe the characteristics, that a system should have to be resilient [21, 22] . These characteristics (i.e. capacities) are namely robustness, stability, flexibility, resourcefulness, coordination capacity, redundancy, diversity, foresight capacity, independence, connectivity and interdependence, collaboration capacity, agility, adaptability, selforganization, creativity and innovation, efficiency, and equity [22, 23] . However, only

As above mentioned, the main characteristics that make a city resilient have to be determined to properly define and evaluate strategies to enhance urban resilience. This section illustrates and describes those capacities that urban systems should have to maintain or enhance their resilience. This list is the result of a literature review about resilience characteristics, described both in different resilience approaches [22, 36] and in several urban resilience campaigns [13] . Figure 1 illustrates the capacities of resilient systems, recognised in different approaches, such as ecology, sustainability and climate change [22] .

In details, this dissertation is focused on those capacities referred to urban resilience, which can be summarised as follow:

• Reflexive. Reflective systems accept changings in continuous. To being reflexive, systems need mechanisms able to evolve continuously. The objective is be able to modify standards or norms in according to emerging evidences. As a result, people and institutions analyze and systematically learn from their past experiences, to base future decision-making [13] ; • Robust. Robust systems include well-conceived, constructed and managed physical assets. The target is being able to withstand the impacts of hazard events without significant damages. In details, robust design foresees potential failures in systems [13] ; • Redundant. Redundancy includes also diversity, or rather the presence of different ways to achieve needs. Redundancy can be explained with the repetition of the same elements, in order to guarantee the functionality of the systems in case of emergency events [13] ; • Flexible. Flexibility is the capacity to react to unexpected events [37] . The lack of flexibility characterizes often the disaster management systems. In fact, flexibility implies the ability of the systems to change, evolve and adapt in response to evolving situations [38] . Furthermore, it may favor decentralization and modular approaches for the management of infrastructure and ecosystem management [13] ; • Resourceful. This capacity includes the ability of the system to anticipate future conditions and in setting priorities. It also implicates that both citizens and institution should be able to achieve their goals or meet their needs during a shock or when under stress [13] ; • Inclusive. It is referred to the consultation and the engagement of communities, in order to reduce also the social vulnerability [13] ; • Integrated. Integration and alignment between city systems promote consistency in decision-making and ensure that all investments are mutually supportive to a common outcome. Integration is evident within and between resilient systems, and across different scales of their operation. Exchange of information between systems enables them to function collectively and respond rapidly through shorter feedback loops through the city [13] .

As mentioned in the first section, this paper analyses the capacity of redundancy. This capacity is related to the ability of the urban system to keep a satisfactory level of performance following an unexpected event. It is related to the presence of different elements with the same function, ensuring the continuity in case some elements fails [39, 40] and it is fundamental to ensure the continuity of urban performances. Table 1 lists a multi-dimensional set of urban resilience indicators, which are referred to redundancy. They are the result of an in-depth literature review about urban resilience indicators [16, 25, [29] [30] [31] .

In detail, the literature review has been carried out through the support of the Scopus platform, using the keywords "urban resilience indicators", "resilience capacities" and "redundancy capacity". Moreover, different resilience assessment approaches [13, 41, 42] and several case studies have been analyzed [43, 44] , to better understand how these indicators can be applied in the evaluation. For this reason, this paper provides a method to collect and describe these criteria considering: (1) dimension, (2) description, (3) unit, (4) capacity, (5) impact (Positive "+" or negative "−"), and (6) source, in order to combine and integrate the different information provided in literature [13, 16, 25, [29] [30] [31] 41] . The objective is underling the capacities to which they are referred to, putting in evidence which can be their possible influence.

System Dynamics Model are both computer-aided and theoretical approach applied to analyze dynamic and complex systems by the identification of the mutual interdependences between variables [46] . SDMs are based on the System Dynamics approach. The methodology of the System Dynamics (SD) has been introduced by Forrester at the end of '50s to analyze and improve the understanding of complex systems [33] [34] [35] . SD is based on feedback concepts to handle non-linearity and multi-loop that characterize complex systems. In fact, it is considered as a suitable tool to examine and illustrate the functioning of complex systems through the identification of the interdependent relationships between their variables [47] . Furthermore, it can be applied to model and simulate the behavior of complex dynamic systems to understand and analyze their dynamics and design development policies [35, [44] [45] [46] . System dynamics is a tool for understanding the change of a dynamic system over time. This approach can help in understanding the impacts of various factors on defined objectives in a system and provide useful information for decision makers [29, 35, 46, [48] [49] [50] [51] [52] .

In details, there are six steps through analyze and describe complex systems through the SD approach [35, 43] . These phases are:

1. Identify the problem; 2. Develop a dynamics hypothesis, explaining the cause of the problem; 3. Create a basic structure of a causal graph; 4. Argument the causal graph with more information; 5. Convert the augmented causal graph into a system dynamics flow graph; 6. Translate a system dynamics flow into SD models

Defined the variables of the system, the following step is developing the causal loop diagram, identifying the relationships and feedback loops between variables [42, 43] . The causal loop diagram illustrates the feedback structure of the system. It represents the reference mode that causes the dynamic behavior of the system [34] . It is also a tool to represent the relationships between variables. In detail, the relationships between variables can be either positive or negative. A positive relationship means that the variables will change in accordance to each other. Instead, the negative relationship implicates that the variables will change in opposite way.

The process to structure the causal loop diagram can be described within these phases:

• Define the problem and the objective;

• Identify the most important elements of the systems;

• Identify the secondary important elements of the systems;

• Identify the tertiary important elements of the systems;

• Define the cause-effect relationships.

As an example, Fig. 2 illustrates the causal loop referred to the population. Two different loops are identified, loop "R+" and "B−". Loop "R+" is a positive loop. It means that the population will grow with births, and the number of birth will increase with the population augment. It is a reinforcing loop, or rather the variable will change in the same direction over time [34] . Loop "B−" means that the number of deaths will grow with the population and the population will decrease as the death increasing. 

As mentioned in the first section, cities are complex, adaptive and dynamic systems [35] . It implies that their multi-dimensional components are influencing each other, determining the evolution of cities over time [47, 48] . It is fundamental to provide a tool able to consider how different elements can influence each other and how factors can impact on a defined objective, in accordance to urban resilience enhancement [10] . This paper proposes the employment of the causal loop diagram to determine both how urban resilience indicators can influence the redundancy, and how redundancy should affect these variables. This choice has been also made in accordance to the fact that resilience can be interpreted as the result of different capacities that are mutually interrelated [53] .

The challenge is providing a framework able to manage this complexity, to support decision makers in the identification of the key variables and their possible impacts on the city as whole, within urban resilience enhancement. Figure 3 illustrates the causal loop developed to analyze the redundancy. This causal loop has been developed following the phases mentioned in the previous section (Sect. 4.1). Firstly, the objective has been clarified, or rather "analyze how the variables can influence the redundancy and how the employment of the redundancy concept in urban development strategy could affect city as a whole". Secondly, the most important elements of the examined issue have been determined (Table 1) . For this phase, all indicators listed in Table 1 have been identified as the most important elements of the system. Thirdly, the causal loop between the most important variables have been designed. The mainly loops identified among redundancy and variables are: (1) Infrastructure for internet service "A"; (2) Arterial roads "B"; Emergency points "C", Number of hospital "D". Secondly, also some loops between variables themselves have been recognized: loop "E" among "infrastructure for internet services" and "quantity of services"; "F" among "renewable energy" and "diversity in renewable energy" and "G" between "diversification of economic business" and "economic development". Furthermore, through this causal loop diagram, also some secondary variables of the city have been identified, namely: (1) vulnerable people, (2) road demand, (3) road improvement rate, (4) urban area, (5) soil consumption, (6) renewable energy, (7) offer of public transport. These variables are not listed in Table 1 , because they do not influence redundancy in directly way. However, they are connected to the primary variable (Table 1 ) and they can influence the redundancy through both a reinforcing loop or by a negative influence. 

The causal loop processing (Fig. 3) has been useful to determine:

• The loops between redundancy and indicators; • The mutual relationships and loops among the considered indicators Through the development of the causal loop, it was possible to reason about how the considered indicators (Table 1) can influence redundancy and how redundancy can influence these variables, within the objective of enhancing urban resilience. As illustrated in Fig. 3 , different loops have been identified. Positive loops, namely "A", "B", "C" and "D" have been identified among redundancy and four indicators, or rather (1) infrastructure for internet services, (2) arterial roads, (3) emergency points and (4) number of hospitals. As an example, loop "A" means that the implementation of the redundancy in urban development strategy will increase the number of the infrastructure for internet services and the growing in the infrastructure for internet services will improve the redundancy capacity. Equally, for loop "B", "C" and "D", the redundancy implementation will increase the number of hospitals, number of arterial roads, and the number of emergency points and the growing of these elements will improve the redundancy capacity. Thus, these causal loops are reinforcing loops [34] , that means that the variables will change in the same direction over time.

As above mentioned, also the secondary variables have been identified during the causal loop drawing. Consequently, three loops among the primary and secondary variables have been recognized, or rather loop "E", "F" and "G".

As an example, the loop "G" can be discussed. This loop is between "economic development" and "diversification of economic business". It means that the economic development will increase with the encouragement of the diversification of the economic business and the diversification of the economic business will grow with the improvement of the economic development. Furthermore, also the loop "F" is very interesting within the redundancy analysis. It is a positive and reinforcing loop between "renewable energy production" and the "diversity in renewable energy". It means that the "renewable energy production" will increase with the improvement of the "diversity in renewable energy" and the "diversity in renewable energy" will grow through the increase of "renewable energy production". As the same time, also the loop "G" is a positive loop. It determines that the increasing of the "quantity of services" will implement the "infrastructure for internet services" and the growing in the "number of the infrastructure for internet services" will increase the "quantity of services".

Moreover, through the identification of the secondary variables, other important relationships have been identified. As an example, it has been recognised that the improvement of the (1) accessibility to public transport, (2) the increasing of the quantity of services and the (3) possible improvement in employment, related to the enhancement of the redundancy, can reduce the quantity of the vulnerable people [54] . At the same time, it is also possible to underline that the redundancy enhancement should have negative impacts on the environmental dimension. In fact, the growing of (1) main roads, (2) number of hospitals and (3) emergency points could increase the surface of the urban area and consequently the soil consumption rate. These consequences are also in accordance to the issues underlined by Walker related to the conflicts between resilience and sustainability [55] . In this sense, the causal loop diagram (Fig. 3) has been fundamental to underline how the capacity of the redundancy can have both positive and negative impacts on the urban system, as a whole.

The paper recognizes the capacities that make a city resilient (Sect. 2) [23] and provides a multi-dimensional set of urban resilience indicators referred to the redundancy capacity (Table 1) . Moreover, it illustrates a preliminary application of SD approach to analyze the relationships between the redundancy and its variables, through the development of the causal loop diagram (Fig. 3) . The illustrated application can be considered as preliminary step in the SD employment to analyze urban resilience within its capacities. It has been useful to underline its suitability in managing complexity and in identifying the probable impacts of different factors on the same objective [34] . Considering the recognized potentialities, the future development of this research should be organized with a sequential organization. Firstly, the causal loop (Fig. 3) has to be translated into the stock and flow diagram. It is necessary to evaluate the consistency of the redundancy impacts on the urban system over time [56] . Secondly, a System Dynamics Model that embrace all urban resilience capacities with their indicators will be processed. The challenge is providing a general evaluation framework, that is able to analyze urban resilience within its capacities [57] , managing cities as complex and adaptive systems [35] . The final objective is proposing an evaluation tool to assess urban development strategies [46, 54, 55] , considering their probable impacts on urban system over time. Thus, this research can support the definition of actions and policies for urban resilience enhancement, predicting their probable effects [62] . Furthermore, this general framework should be integrated with the multi-criteria analysis [58, 60, 61] , to consider the specific characteristic of context both in the evaluation and in setting developing strategies [57] [58] [59] .

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