Social Networks and Relational Sociology
9.7 Micro and Macro
There is yet another dualism that relational soci- ology is required to tackle, however; namely, the micro-macro divide. As I understand it, the micro-macro debate focuses upon scale.
Sociology might focus upon the details of a few seconds of conversation between two people or upon matters of world history, begging the ques- tion of how such foci are linked and whether the principles governing one are relevant to the other.
This is a potentially very complex issue and I cannot do complete justice to it here. It is impor- tant, however, to say something about context and something about scale. I begin with the former.
The link between micro and macro is not always as diffi cult to envisage as it initially sounds. The events which turn the wheels of world history, affecting large numbers of people, are sometimes, in themselves, relatively ‘small’.
As I write, for example, the Greek Parliament has just agreed, very reluctantly, to pass a number of
‘austerity laws’ demanded by the European Union in return for a (third) fi nancial bailout, involving billions of Euros, in an effort to protect their country from economic disaster and possi- ble exit from the Eurozone. This is an event of global signifi cance with huge implications,
especially in Greece but across Europe and, to some extent, the world. With the exception of the huge crowds of protestors who gathered outside the Greek Parliament when the decision was being made, and who I return to below, however, most of the decisions shaping and steering this situation were made in interactions between a relatively small number of people over a rela- tively short period of time. Greek politicians sat face- to- face and debated. Similarly, the demands of ‘the European Union’ were decided by a small number of European politicians over a few days, face-to-face in various committee rooms, and relayed directly to the Greek Prime Minister.
Any analysis of these interactions would have to understand their context: the various pressures upon those involved, the stakes involved, and so on. However, this moment in global history was decided through face-to-face interaction which, whatever its particularities, assumed much the same form as any other human interaction. This is not atypical. As the individualists recognize, it is actors who do things and make things happen in the social world. All sociological phenomena can and should be tracked back.
The Greek government is a corporate actor, involving irreducible mechanisms of decision making and implementation. Likewise the European Union. The decisions made by and between these corporate actors often affect mil- lions of people. They are global in their reach;
macro-cosmic. But they are interactions between actors all the same. The ‘world system’ or ‘global social order’ is not a mysterious force affecting our lives from without but rather a network, albeit a hugely complex network, involving millions of actors, both human and corporate, and the vari- ous (often unequal) ties between them, and as such it can be analyzed by way of SNA (Smith and White 1992 ; Snyder and Kick 1979 ). The social macro-cosm may involve ‘bigger’ actors and/or more actors (see below) but it is no differ- ent in kind to its constituent micro-cosms.
In the Greek example social movements and their protests also played a role. They put pres- sure on the Greek government and sent a signal to other European politicians. Social movements do not fi t my defi nition of corporate actors because
they generally have no means of making deci- sions or enforcing their own resolutions (Offe 1985 ), even if some of the ‘social movement organizations’ within them do. Movements are relational phenomena, however, and innumerable studies have pointed both to their network char- acter and to the role of pre-existing networks in their formation and mobilization (Crossley 2007 ; Crossley and Krinsky 2015 ; Diani and McAdam 2003 ).
It isn’t always possible to pin the twists and turns of history down to particular interactions.
Certain trends and dynamics cannot be localized in this way. The relational approach is still the best way of making sense of such dynamics, however. Complexity theory in the natural sci- ences and the agent-based models employed therein provide a useful reference point for think- ing about these issues (Watts 1999 ; Barabási 2003 ; Newman et al. 2006 ). In complex systems, which are usually conceived of as networks involving interaction between millions of nodes, the multiplication of interactions and interven- tion of cascade, feedback and other such mecha- nisms generate fascinating organizational forms and dynamics akin to those sometimes observed by sociologists. These dynamics and forms are often extremely impressive; everything happens
‘as if’ by grand design. Unlike the sociological holists discussed above, however, complexity scientists are able to show by way of their models that such emergent forms are indeed emergent, that is, generated from the bottom up by way of interactions and their concatenations, and not inevitable outcomes of history’s grand plan.
Complex systems are networks and their emer- gent organization can be analyzed as such. We might not be able to graph such networks very clearly, given their size, but we can analyze them using SNA and related methods. Interaction, ties and networks remain the bedrock of our under- standing of what is going on.
The focus on networks in complexity theory has also led to an interesting exchange with social science. Emergent effects in complex sys- tems are sometimes diffi cult to comprehend because it is diffi cult to imagine how order could emerge between such a large number of
nodes (millions). Surely, complexity theorists puzzled, geodesic distances would be too long to facilitate any signifi cant transfer of energy or information? In puzzling this question complex- ity theorists stumbled across work by social psychologist, Stanley Milgram ( 1967 ), which suggested that any two citizens picked at ran- dom from the US population, are, on average, separated by only six ties (‘six degrees of sepa- ration’). This so-called ‘small world’ phenom- ena was intriguing to the complexity physicists because it rendered the idea of mutual infl uence between nodes in a network of millions far more plausible. Geodesics need not be very long even in huge networks; in which case, infl uence and coordination across such networks is plausible.
This prompted complexity theorists both to con- duct a variety of studies looking for ‘small world’ examples in the natural world, which they found in abundance, and to solve the math- ematical problem posed by Milgram’s work:
namely, how can nodes in a network of millions be linked by such short geodesics? They came up with two possibilities, both of which work (mathematically), and have been demonstrated empirically and in simulations. More important for our purposes, however, is the support that it lends to my idea, introduced above, that the social world is a (multiplex, multi-modal, multi- leveled and dynamic) network. This idea some- times attracts resistance because sociologists are inclined to believe that the scale of national and international societies is so big that ‘some- thing else’, something other than interaction, ties and networks are at work. The work of the complexity theorists suggests that this need not be so and that a network model of society is plausible.
What the complexity theorists overlook in their use of Milgram, however, is his focus upon social division. Milgram conceived of social structure as a network. His research was focused, in some part, upon the basic properties of such networks, not least average geodesics. However, he was also interested in the impact of status dif- ferentials upon network structure. His work sug- gested that this could be considerable, particular in relation to race. His methodology involved
asking people to mail a package to others whom they knew, with the ultimate aim of delivering the package to a target individual who was not directly known to those involved at the start of the experiment. The study suggested that pack- ages often traversed geographical space with rel- ative ease and speed but that, where they were required to cross a racial divide, the process often stalled. Participants enjoyed good relations with others of their own race across the country, in other words, but few such relations with mem- bers of other racial groups even in their own town. Ties were shaped by status and more espe- cially ethnic homophily. I mention this here to demonstrate that and how relational sociology allows us to begin to think about and research such social divisions, on a macro-level. Social divisions, from a relational point of view, mani- fest in patterns of connection (and lack of con- nection) within a population and those patterns of connection are empirically analyzable using SNA (see also Blau 1974 , 1977 ).
Status homophily is an example of what I described early as ‘cohesive subgroups’. Actors who share a particular status tend to gravitate towards one another. Actors with different sta- tuses do not. They may even actively avoid one another. As noted earlier, however, this is not the only way in which nodes might cluster. Nodes can be clustered where they occupy ‘equivalent’
positions in a network, as defi ned in SNA and measured by a number of dedicated clustering algorithms. A good example of this ‘blockmodel- ing’ in action is Peter Bearman’s ( 1993 ) analysis of kinship networks and elite structures in Norfolk (England) prior to the (1642–1651) civil war. The details of the study are not strictly rele- vant here but it is important to note, fi rstly, that Bearman uses blockmodeling to render a very large network intelligible and to track both hier- archy and changes in hierarchy within English society; secondly, that he identifi es changes in this network which played an important role in the precipitation of the civil war. Again here SNA proves a useful tool for exploring ‘macro’ pro- cesses and events, and again the keys to under- standing those processes and events are shown to be networks, ties and interactions.