The aim of this research was therefore to use a collaborative learning process to build on the initial causal model for the integration of NBS to Copenhagen´s flood mitigation infrastructure. To that effect, the purpose of this paper is to present system dynamics approaches to policy modelling and simulation. In light of this, this research follows through the following objectives:

1. To identify the dynamic complexities and challenges that are often associated with complex dynamic systems;

2. To develop a taxonomic analysis of policy formulation problems so as to visualize their generic structures;

3. To make a causal loop analysis of the identified policy formulation problems based on their generic structures.

Thus, causal relationships between variables of interest within the socio-ecological system are examined, after which weaknesses in the socio-ecological processes in need of strengthening are identified, with particular reference to institutional capacity and the execution of governance. The process of gaining a deeper understanding of the complexity of the system, as well as identifying possible measures for strengthening socio-ecological processes through more effective governance, offers a pathway for decreasing vulnerability to natural hazards (MÁÑEZ et al. 2014, p.7). The findings of this study aim to provide emerging knowledge for local adaptation and could serve as a basis for similar socio-ecological systems elsewhere.

METHODS

Participatory SD modelling explicitly includes a wide range of stakeholders, and is often focused on public policy problems. It has been successfully used to improve decision making in a variety of relevant disciplines, including urban planning, transport policy, road safety and public health. The method has also been used to consider the outcomes of transport policies on air quality (Stave, 2002). As with many SD modelling efforts, these examples aimed to provide insights about future dynamic effects of policy alternatives by relating them to the system structure, as opposed to providing point predictions about outcomes at a future time. In the context of urban planning, participatory SD provides an opportunity to bring together disparate sources of evidence with the understanding of policy makers, practitioners, and advocacy groups. It can also potentially support policy makers who typically face major challenges in implementing change by enabling them to communicate more confidently about desired and expected outcomes across a range of domains of interest.  

Saeed (1992) describes a useful generalisable method for an SDM process that uses repeated cycles, starting with desired outcomes, then moving through the following stages: understanding of problem trends related to these outcomes; qualitative representation of the system structure; development of a dynamic simulation model; scenario experimentation; and policy design.

This research describes the first part of such a process, namely the development of an initial shared qualitative system understanding of urban planning. This form of modeling is however not undertaken to accurately predict the future, but to gain a deeper understanding of causal relationships within a system (FORD 2010, p.8). The method follows the idea of closed boundaries. Interactions are created between components of a system within a boundary (FORRESTER 1975, p.8), separating the “dynamically significant inner workings of the system from the dynamically insignificant external environment” (RICHARDSON 1991, p. 297, from Vennix 1996, p.45). System dynamics modeling however also allows for the consideration of nonlinear causal relationships and feedback loops, both important criteria when considering complex systems (BIRKMANN et al. 2013, p.201).

Group model building, a form of particpatory system dynamics, will be applied with the aim of maximizing involvement of stakeholders during data collection in phase two (HOVMAND 2014, p.18). The direct inclusion of stakeholder data in the model building phase further enhances accuracy and authenticity of the model itself (VENNIX 1996, p.5; ADGER et al. 2004, p.77). One of the distinct advantages offered by system dynamics, and more specifically group model building, is the ability of incorporating local knowledge and perceptions.

Causal loop' qualitative system dynamics enhances linear and 'laundry list' thinking by introducing circular causality and providing a medium by which people can externalise mental models and assumptions and enrich these by sharing them. Furthermore, it facilitates inference of modes of behaviour by assisting mental simulation of maps. By identifying policy links in maps, it allows focussed speculation of how to intervene to redesigned systems. By using archetypal structures, particularly total generic two loop structures, involving policies, boundaries and delays, it enables potential unintended consequences to be anticipated and hence increases the chances of plans being achieved. The methods bring much needed tools to the strategic areas of management and allow a wide range of managers to access the power of feedback thinking.