Box 1: Key features of network mapping and social network analysis

Adapting problem framings. The initial visualization of a stakeholder-knowledge network can provide areas for further exploration and research, e.g. identifying malleable barriers (Moser & Ekstrom, 2010) or informal networks and ‘shadow spaces’ (Pelling et al. 2008), as well as ‘bridges’, ‘boundary-spanners’ (Berkes and Folke, 1998) and different types of ‘flows’ of resources including ‘informal capital’. These can be highly significant in facilitating change and influencing policy processes, even if intangible in nature. It is quite common to find ‘discourse coalitions’ with a shared understanding of the problem, but not necessarily the same ‘world-view’, or ‘advocacy networks’ where the ‘world-view’ may be the same but approaches differ (Turnpenny et al., 2005). Social network analysis can help understand how and why actors behave the way they do, through analysis of the structural pattern of relations (topology). It provides valuable insights to problem framings and how uncertainty is dealt with. These characteristics help in climate adaptation ‘problem framing’ and understanding different decision-making regimes.

Facilitating collaboration. Social processes express the structural pattern of relations in networks and show how outcome variables influence how networks change and evolve over time (Borgatti and Foster 2003). The existence of subgroups or clusters can affect the level of cohesion. For example, weak ties can have negative effects on the capacity of subgroups to collaborate. The issue of temporal as well as spatial scales is significant, since the time horizon for decision lifetimes amongst actors can act as a barrier (UK CCRA, 2012). Working cooperatively and collaboratively across a network appears to be an effective way of creating change. Single organizations can access (and benefit from) the depth and breadth of resources but also the knowledge, understanding, skills and expertise needed to build adaptive capacity. Such work is challenging to coordinate, requiring skill and resources, which can be provided by a ‘Linking Pin’ organization (Carley and Christie, 2000), i.e. for cross-organizational support. Network mapping can identify areas where these changes can occur and the discussion and analysis of conflicting or synergistic goals (barriers to cooperation and collaboration). Identifying these goals is also part of the participatory process when creating network maps. Not all flows are ‘positive’. Bodin and Crona (2009) cite examples of the correlation between network density and joint action. They also note that there may be a threshold above which network density becomes counter-productive in facilitating collective action (e.g. Oh et al., 2004 in Bodin and Crona, 2009) due to the homogenization of information and a lack of ‘new’ knowledge leading to less efficient resource use and/or reduced capacity to adapt to changing conditions.

Agents of change. Network topologies can be analyzed at the network-level, but also at the node-level focusing on institutions or actors. Assessing the position of the actor in the network and the number and strength of their relationships reveals their structural position to influence other actors. The centrality of an actor allows analysis of the level of influence, but also the role they can play in the network as a bridge that connects others (Cash et al. 2002). An actor connecting with many others has the ability to influence the flows between actors. Identifying central actors is a useful way to understand dominant decision framings, how these are used and the effect on collective action. In this regard, central actors located in strategic positions can be potential ‘agents of change’ in the network or ‘adaptation champions’.

Inter-agency coordination. Options identified by different parts of a governance system often relate to who has control over the decision process, jurisdiction, political interests, funding, etc. (Renn, 2008 in Moser and Ekstrom, 2010). If the breadth of the system of concern covers many jurisdictions, the issue requires cross-coordination to implement options (Moser & Ekstrom 2010). The beneficial aspect of clusters is that they may facilitate the development of specialized and tacit knowledge within their own sub-groups. This is valuable for the knowledge diversity of network as a whole, provided that there are also mechanisms for knowledge transfer and boundary-spanning (Berkes and Folke, 1998) to facilitate ‘joined-up thinking’ between specializations, to lead to new knowledge and action. This can enhance integrated management and cross-sectoral planning. Without knowledge transfer, the opposite effect can manifest itself – very low collaboration and cooperation or reconciliation of actors with differing goals and objectives.

Types of Networks. A simple illustration of types of network topologies is shown below (Figure 1). These examples outline different types of networks based on number of peer connections, density of relations, role of boundary nodes between isolated networks and degree of cohesiveness, inheritance of links as organizational structures, subgroups interconnectivity and degree of network centralization.

Type 1. Individual action predominates. While people are connected in various ways, most actions are at the organisation/individuals own level and independent of what others believe or are doing. In this type of network, the psychology of individual action dominates. At this level, there may be a diversity of approaches to uncertainty and there is little need for a consensus view. The construction of the problem is usually highly constrained and mostly short-term with rather limited information on long-term futures.

Type 2. Individuals and groups are connected in an egalitarian space. There are various links but the network tends to be ‘like-minded’ and the structure of the problem is similar across actors. Uncertainty may not be explicit—rather reduced to tacit assumptions common in peer networks and reflected in cultural and group norms rather than a science-policy dialogue as such.

Type 3. Many organizations have hierarchical decision making with a leader (and even an meta-level organisation e.g. a Board) defining policy that is translated into strategy and action. Uncertainty can be explicit, although it tends to be wrapped into how the organization is structured and procedures that are in place for other purposes. Co-management would be the opposite to this, where multiple actors are involved in the governance to varying degrees as opposed to top-down centralized management. Adaptive co-management emphasizes flexible joint management processes, which will allow the continuous application of new knowledge where relevant (Bodin and Crona, 2009).

Type 4. A hybrid of two or more kinds of networks, which is often the reality. Two egalitarian networks for instance might be linked, each with its own approach to uncertainty. In such cases, there is more than one decision framing in play and uncertainty may enter the decision in different ways.

Type 1 (left): Individualistic, few links between nodes.
Type 2 (centre): Egalitarian more connected.
Type 4 (right): Multiple networks in a hybridisation.