Winners' Circles (Network and Other Perspectives)
We are almost there. This section is the last of the preliminaries before we start digging into the data. Its purpose is to suggest what social network analysis brings to the table that those engaged in other forms of social science research might want to consider.
In the years after WWII, empirical social science, especially but not exclusively in North America, came to be dominated by what Andrew Abbott has labeled Standard Causal Analysis (SCA; Abbott, 2004). Social network analysis (SNA) was one of the intellectual movements, others including the interpretive turn in anthropology and other forms of postmodernism, that rejected its fundamental assumptions about the nature of human society.
Pragmatically, SCA aimed to identify the levers required by social engineering. Conceptually, it saw society as the outcome of forces operating in a way fundamentally similar to those envisioned by physicists in classical mechanics. These forces impinged on human objects (individuals or groups), and compelled them to move in particular directions. It was imagined, for example, that a particular combination of forces labeled “race,” “gender,” and “income” would necessarily determine the direction of a woman’s life. Those who objected to that direction could, then, alter it by changing one or more of the forces in question.
The problem was how to identify the forces and measure the weight with which they impinged on the human object. The favored method was a large-N statistical analysis of randomly sampled data whose output is epitomized by regression lines, i.e. vectors,a.k.a., directions, straight lines from point A to point B, constructed by optimizing the weightings of the forces (other vectors) supposed to impinge upon them. Since the subjects who answered the survey questions were usually individuals, this method was also highly compatible with envisioning an ideal outcome in which democracy (votes by individuals) and market forces (consumer choices by individuals) would aggregate to form the forces that would, via negative feedback, keep that society in stable equilibrium.
As critics never tire of pointing out, however, this approach not only ignores the agency, the human freedom to choose, of those whose behavior is shaped by the forces in question. Reality is messy and the results of research conducted along these lines tend to be neither clean nor pretty. The heart of the critique is captured in the quote with which Lin Freeman begins his history of social network analysis. Attributed to Columbia sociologist Alan Barton and written in 1968, it reads as follows.
For the last thirty years, empirical social research has been dominated by the sample survey. But as usually practiced, using random sampling of individuals, the survey is a sociological meat grinder, tearing the individual from his social context and guaranteeing that nobody in the study interacts with anyone else in it. It is a little like a biologist putting his experimental animals through a hamburger machine and looking at every hundredth cell through a microscope; anatomy and physiology get lost, structure and function disappear, and one is left with cell biology…If our aim is to understand people’s behavior rather than simply to record it, we want to know about primary groups, neighborhoods, organizations, social circles, and communities; about interaction, communication, role expectations, and social control. (Freeman, 2004:1)
Without the sociological turn to which Barton points in his conclusion, we are left with trying to understand what goes on inside the human cells of the social organism. When that is conceived in terms of subjective feeling, meaning and choice, we are left with either the speculations of consumer research or, in academia, the interpretive/postmodern alternatives previously mentioned above. With that sociological turn, however, the way remains open for scientific research that is not necessarily constrained by SCA assumptions. Social network analysis is one prime example; agent-based modeling may be another. Classical forms of ethnography and history, which balance interpretation from the subject’s point of view with material conditions, also have much to contribute.
What is it exactly, however, that social network analysis brings to the table? According to Freeman, social network analysis is a combination of
1. Structural intuition based on ties linking social actors
2. Systematic empirical data
3. Graphic imagery, and
4. Use of mathematical and computational models. (Freeman, 2004:3)
In this study, the structural intuitions that motivate the research are derived from a common sense representation of the Japanese advertising industry as a range of mountains, three very large and others smaller, fading into foothills. That the mountains form a range is important. The feet of the mountains are joined. How separate the peaks actually are or, alternatively, represent the joint outcome of inferred tectonic processes become interesting questions. It is ties formed by social actors, advertising creatives who work together in teams, that suggest an approach to finding some answers. The systematic empirical data are provided by the credits in the TCC annual; heavy use is made of graphic imagery, especially network diagrams; and the mathematical and computational models incorporated in Pajek not only generate the diagrams, they also suggest a variety of ways to parse and explore the data.
A primary focus on relationships, ties between social actors, instead of actor attributes, is a feature cited by every introduction to social network analysis. Wasserman and Faust note four additional features frequently found in social network analyses.
• Actors and their actions are viewed as interdependent rather than independent, autonomous units.
• Relational ties (linkages) between actors are channels for transfer or ‘flow’ of resources (either material or nonmaterial).
• Network models focusing on individuals view the network structural environment as providing opportunities for or constraints on individual action.
• Network models conceptualize structure (social, economic, political, and so forth) as lasting patterns of relations among actors.(Wasserman and Faust, 2005 :4).
Thus, for example, in this study the actors, the advertising creatives, work together in teams. Team members bring different skill sets to the table, all of which are required to produce award-winning advertising. They are certainly interdependent. Working together in teams involves flows, minimally the exchange of ideas required to reach consensus on a proposal and produce the ad in question. Working in a team with other talented people presents opportunities; working with winning teams is a way to advance a career. Working in teams also involves constraints. Advertising creatives are notoriously independent thinkers, often prima donnas. But to work together successfully requires give and take and knowing how far to push without pushing too hard.
It is, however, an interesting question whether the actors who make up the teams form lasting relationships. Teams are formed for specific projects. Team members may go their separate ways once a project is over. Our data show us only that the members of a team worked together on one or more winning ads, and the networks constructed by contest judges decisions are only subsets of larger professional networks.
Mark Newman reminds us that, mathematically speaking, a network is simply a collection of points joined together in pairs by lines. (2010:1) In the case of social networks, the points represent actors, the lines the social ties between them. But precisely the same sort of representation can be used for all sorts of networks: the Internet and Worldwide Web, transport and power grids, and protein cascades in cell biology are frequently cited examples. Why, then, a reader might ask, turn to network analysis for help in understanding systems in which so much else are involved? Why, in particular, use network analysis to study people, whose feelings, thoughts and meanings are not represented in network diagrams? One answer is that all these can added to the framework that network analysis provides, as the overall plan for this research suggests. Newman provides another, and to me compelling, answer.
Scientists in a wide variety of fields have, over the years, developed an extensive set of tools?\mathematical, computational, and statistical?\for analyzing, modeling and understanding networks?c. Thus if there is a system you are interested in, and it can usefully be represented as a network, then there are hundreds of different tools out there, already developed and well understood, that you can immediately apply to the analysis of your system. Certainly not all of them will give useful results?\which measurements or calculations are useful for a particular system depends on what the system is and does and on what specific questions you are trying to answer about it. Still, if you have a well-posed question about a networked system there will, in many cases, already be a tool available that will help you address it. (2010: 2-3)
For this particular project, Pajek provides a handy toolkit in which many useful tools can be found. There is, however, one additional set of issues to consider about the way in which those tools are used.
Wasserman and Faust note that, “the methods of social network analysis can be used in two ways, as formal descriptions or in model and theory evaluation and testing.” (2005:5). Here I would like to suggest a third. The formal descriptions and network diagrams generated by Pajek and similar software can function, in effect, as the social scientist’s microscope. Like the biologist’s microscope, they reveal fine details of structure that are normally invisible or only dimly perceived. This simile gains added force from network visualization software’s use of color, shape and size to code network node attributes (as, for example, assigning a color that indicates the lead agency in charge of producing an ad or making actors with more direct connections to others visibly larger than others). These capabilities allow this network analyst to follow the biologist’s example and stain his slides to highlight structures of particular interest. Other tools make it possible to dissect complex structures and extract portions of them that are simpler to understand. The formal descriptions provide a richly detailed structural framework on which to hang other considerations, from personal perspectives to material conditions.
This point, however, marks the boundary of this study. Model and theory evaluation and testing require mathematical and computational expertise that others will have to supply.
Next entry: Winners' Circles (Establishing the Context)
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