DecisionSupport 8Box 1: Key features of network mapp<strong>in</strong>g and social network analysisAdapt<strong>in</strong>g problem fram<strong>in</strong>gs. The <strong>in</strong>itial visualization of a stakeholder-knowledge network canprovide areas for further exploration and research, e.g. identify<strong>in</strong>g m<strong>all</strong>eable barriers (Moser &Ekstrom, 2010) or <strong>in</strong>formal networks and ‘shadow spaces’ (Pell<strong>in</strong>g et al. 2008), as well as ‘bridges’,‘boundary-spanners’ (Berkes and Folke, 1998) and different types of ‘flows’ of resources <strong>in</strong>clud<strong>in</strong>g‘<strong>in</strong>formal capital’. These can be highly significant <strong>in</strong> facilitat<strong>in</strong>g change and <strong>in</strong>fluenc<strong>in</strong>g policyprocesses, even if <strong>in</strong>tangible <strong>in</strong> nature. It is quite common to f<strong>in</strong>d ‘discourse coalitions’ with a sharedunderstand<strong>in</strong>g 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). Socialnetwork analysis can help understand how and why actors behave the way they do, through analysisof the structural pattern of relations (topology). It provides valuable <strong>in</strong>sights to problem fram<strong>in</strong>gs andhow uncerta<strong>in</strong>ty is dealt with. These characteristics help <strong>in</strong> climate adaptation ‘problem fram<strong>in</strong>g’ andunderstand<strong>in</strong>g different decision-mak<strong>in</strong>g regimes.Facilitat<strong>in</strong>g collaboration. Social processes express the structural pattern of relations <strong>in</strong> networksand show how outcome variables <strong>in</strong>fluence how networks change and evolve over time (Borgatti andFoster 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 oftemporal as well as spatial scales is significant, s<strong>in</strong>ce the time horizon for decision lifetimes amongstactors can act as a barrier (UK CCRA, 2012). Work<strong>in</strong>g cooperatively and collaboratively across anetwork appears to be an effective way of creat<strong>in</strong>g change. S<strong>in</strong>gle organizations can access (andbenefit from) the depth and breadth of resources but also the knowledge, understand<strong>in</strong>g, skills andexpertise needed to build adaptive capacity. Such work is ch<strong>all</strong>eng<strong>in</strong>g to coord<strong>in</strong>ate, requir<strong>in</strong>g skilland resources, which can be provided by a ‘L<strong>in</strong>k<strong>in</strong>g P<strong>in</strong>’ organization (Carley and Christie, 2000), i.e.for cross-organizational support. Network mapp<strong>in</strong>g can identify areas where these changes canoccur and the discussion and analysis of conflict<strong>in</strong>g or synergistic goals (barriers to cooperation andcollaboration). Identify<strong>in</strong>g these goals is also part of the participatory process when creat<strong>in</strong>g networkmaps. Not <strong>all</strong> flows are ‘positive’. Bod<strong>in</strong> and Crona (2009) cite examples of the correlation betweennetwork density and jo<strong>in</strong>t action. They also note that there may be a threshold above which networkdensity becomes counter-productive <strong>in</strong> facilitat<strong>in</strong>g collective action (e.g. Oh et al., 2004 <strong>in</strong> Bod<strong>in</strong> andCrona, 2009) due to the homogenization of <strong>in</strong>formation and a lack of ‘new’ knowledge lead<strong>in</strong>g to lessefficient resource use and/or reduced capacity to adapt to chang<strong>in</strong>g conditions.Agents of change. Network topologies can be analyzed at the network-level, but also at the nodelevelfocus<strong>in</strong>g on <strong>in</strong>stitutions or actors. Assess<strong>in</strong>g the position of the actor <strong>in</strong> the network and thenumber and strength of their relationships reveals their structural position to <strong>in</strong>fluence other actors.The centrality of an actor <strong>all</strong>ows analysis of the level of <strong>in</strong>fluence, but also the role they can play <strong>in</strong>the network as a bridge that connects others (Cash et al. 2002). An actor connect<strong>in</strong>g with manyothers has the ability to <strong>in</strong>fluence the flows between actors. Identify<strong>in</strong>g central actors is a useful wayto understand dom<strong>in</strong>ant decision fram<strong>in</strong>gs, how these are used and the effect on collective action. Inthis regard, central actors located <strong>in</strong> strategic positions can be potential ‘agents of change’ <strong>in</strong> thenetwork or ‘adaptation champions’.Inter-agency coord<strong>in</strong>ation. Options identified by different parts of a governance system often relateto who has control over the decision process, jurisdiction, political <strong>in</strong>terests, fund<strong>in</strong>g, etc. (Renn,2008 <strong>in</strong> Moser and Ekstrom, 2010). If the breadth of the system of concern covers many jurisdictions,the issue requires cross-coord<strong>in</strong>ation to implement options (Moser & Ekstrom 2010). The beneficialaspect of clusters is that they may facilitate the development of specialized and tacit knowledgewith<strong>in</strong> 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-spann<strong>in</strong>g (Berkes andFolke, 1998) to facilitate ‘jo<strong>in</strong>ed-up th<strong>in</strong>k<strong>in</strong>g’ between specializations, to lead to new knowledge andaction. This can enhance <strong>in</strong>tegrated management and cross-sectoral plann<strong>in</strong>g. Without knowledge2
Social Network Analysistransfer, the opposite effect can manifest itself – very low collaboration and cooperation orreconciliation of actors with differ<strong>in</strong>g goals and objectives.Types of Networks. A simple illustration of types of network topologies is shown below (Figure 1).These examples outl<strong>in</strong>e different types of networks based on number of peer connections, density ofrelations, role of boundary nodes between isolated networks and degree of cohesiveness,<strong>in</strong>heritance of l<strong>in</strong>ks as organizational structures, subgroups <strong>in</strong>terconnectivity and degree of networkcentralization.Type 1. Individual action predom<strong>in</strong>ates. While people are connected <strong>in</strong> various ways, most actionsare at the organisation/<strong>in</strong>dividuals own level and <strong>in</strong>dependent of what others believe or are do<strong>in</strong>g. Inthis type of network, the psychology of <strong>in</strong>dividual action dom<strong>in</strong>ates. At this level, there may be adiversity of approaches to uncerta<strong>in</strong>ty and there is little need for a consensus view. The constructionof the problem is usu<strong>all</strong>y highly constra<strong>in</strong>ed and mostly short-term with rather limited <strong>in</strong>formation onlong-term futures.Type 2. Individuals and groups are connected <strong>in</strong> an egalitarian space. There are various l<strong>in</strong>ks but thenetwork tends to be ‘like-m<strong>in</strong>ded’ and the structure of the problem is similar across actors.Uncerta<strong>in</strong>ty may not be explicit—rather reduced to tacit assumptions common <strong>in</strong> peer networks andreflected <strong>in</strong> cultural and group norms rather than a science-policy dialogue as such.Type 3. Many organizations have hierarchical decision mak<strong>in</strong>g with a leader (and even an meta-levelorganisation e.g. a Board) def<strong>in</strong><strong>in</strong>g policy that is translated <strong>in</strong>to strategy and action. Uncerta<strong>in</strong>ty canbe explicit, although it tends to be wrapped <strong>in</strong>to how the organization is structured and proceduresthat are <strong>in</strong> place for other purposes. Co-management would be the opposite to this, where multipleactors are <strong>in</strong>volved <strong>in</strong> the governance to vary<strong>in</strong>g degrees as opposed to top-down centralizedmanagement. Adaptive co-management emphasizes flexible jo<strong>in</strong>t management processes, which will<strong>all</strong>ow the cont<strong>in</strong>uous application of new knowledge where relevant (Bod<strong>in</strong> and Crona, 2009).Type 4. A hybrid of two or more k<strong>in</strong>ds of networks, which is often the reality. Two egalitariannetworks for <strong>in</strong>stance might be l<strong>in</strong>ked, each with its own approach to uncerta<strong>in</strong>ty. In such cases,there is more than one decision fram<strong>in</strong>g <strong>in</strong> play and uncerta<strong>in</strong>ty may enter the decision <strong>in</strong> differentways.Figure 1. Illustrations of peer-oriented network types.Type 1 (left): Individualistic, few l<strong>in</strong>ks between nodes.Type 2 (centre): Egalitarian more connected.Type 4 (right): Multiple networks <strong>in</strong> a hybridisation.3