Emergent Social Behaviors in Information Systems
The purpose of this essay is to provide a perspective on the specific social behaviors that may arise from the architecture and use of social software and information technology. My aim is to compile some of the latest and more lasting research on the subject into a common thread for the reader. The topic is somewhat disjointed without an established, traditional cannon of work, however there are many cutting edge researchers attempting to change that. I would also like to make the (somewhat safe) assertion that there are emergent and complex properties to social behaviors based in information systems, and that these properties’ respective theories are integral to understanding much of the data.
This paper utilizes original research I conducted on online blogs. It also contextualizes this original research into a systems perspective.
The end of Roger Lewin’s book, Complexity1 features a section on John Holland, a godfather in the world of emergence. He’s quoted as saying, “People make the difference.” Holland has an elegant way of explaining the phenomenon of emergence with use of a chess analogy. He explains, “If I look at the board and add up the value of the pieces on each side—nine for the queen, three for the bishop, and so on—I’m not going to understand how the game is going, because it is the position of the pieces that counts, their interaction. You have to look at the way the various pieces support each other to know which side is in the stronger situation. It’s the interaction of the pieces from which the strength emerges. It’s the same in all complex, adaptive systems. Interaction is the key2.” This explanation and analogy translates very well to the study of social interactions in information systems or social software systems. For example, in a chat room there are many different binary actions that users can engage in, such as choosing to “talk” or not to talk. Entering and leaving the chat is also an easily accountable action. However, these results relay very little information on the dynamics and politics of the conversations happening within the constraints of the software. Therefore, it seems appropriate and necessary to refocus systems theories so that they may include emergence and social complexity.
On the contrary, classical systems theory does not view participants in the system as agents, or having the ability to act in a manner inconsistent with the system3. In 1970 in his Primer on Simulation and Gaming, Richard F. Barton wrote, “Like behavior in real objects systems, behavior in simulations is too complex for complete explanation.4” This sentiment seems to be the predominant view of information-social systems up to and even including some recent research in the field. Later that decade, however, advancement was made in formalized mathematics of how to conceptualize participants in a hierarchal organization. One style of notation named participants as decision-making systems, or nested subsystems capable of agreeing or disagreeing, which thusly affects the flow of information from input to output5.
Returning to the book Complexity, Roger Lewin also writes (with the assistance of Birute Regine) about the nature of emergent properties and complexity theory within the world of business. He identifies a business as a complex adaptive system, but more relevant to my studies, he also compiled a short list of operational rules for complex systems6. They are:
○ The source emergence is the interaction among agents who mutually affect each other.
○ Small changes can lead to large effects.
○ Emergence is certain, but there is no certainty as to what it will be.
○ A greater diversity of agents in a system leads to richer emergent patterns.
These guidelines serve as another perspective on the foundation of complexity. However, creators of social software always have a unique problem in mapping the architecture of social interaction. Early examples of user-to-user interfacing, such as Usenet, provide a wealth of data for analysis, though this technology is now only one type of many. Marc A. Smith writes, “Each online communication system structures interaction in a particular way, in some cases with dramatic effect on the types of social organizations that emerge from people using them.7
Again, citing Smith’s Communities in Cyberspace on the subject of Usenet, an early online open forum of threaded discussion, “Almost anyone can read the contents of a Usenet newsgroup, create entirely new newsgroups, or contribute to one. This makes the Usenet a more interesting and challenging social space than systems that are ruled by central authorities. Whatever order exists in the Usenet is the product of a delicate balance between individual freedom and collective good. Many newsgroups are wild, unordered places, but what is startling is how many are organized and productive8.” The idea of a balanced freedom/authority binary is not a new one (see The New Golden Rule by Amitai Etzioni for a succinct analysis of the problem); though the underlying hope of an emergent balanced system has been strongly attached to the internet and its continually evolving form. There are disagreements to this, such as Theodore Roszak, author of The Making of a Counterculture: Reflections on the Technocratic Society and Its Youthful Opposition, states, “information technology has the obvious capacity to concentrate political power, to create new forms of social obfuscation and domination.9” To an extent this statement has merit, and should be considered by those who are creating the constraints or programs in which people communicate.
The Usenet was eventually made obsolete by the development of the World Wide Web and graphic interfaces. Then, in December of 1997, a Usenet participant named Jorn Barger dubbed his daily link listing, a “weblog,” and this marked an evolution in online social interaction10. The transition from forums and email lists to blogs and news aggregation sites marked a change in the “types” of people participating online as well as, naturally, their behaviors. I’m going to distinguish between types using the terms producers and consumers.
I’m going to assign social producer two meanings—the first being more of a construction role, in which the producer builds a container for social interactions, thus having an authority (whether he realizes it or not) over how micro-societies will function within the space. An obvious example of this role is a social software designer creating something like a chat room. The second definition of social producer would be a person or software user who facilitates social action such as an active weak tie between multiple, disparate social groups who transmits ideas, “in-jokes”, and cultural trends. MySpace celebrities and popular bloggers are great examples. Then there are social “consumers”; this group is at the receiving end of online cultural construction. They surf YouTube and blogs but do not contribute content at nearly the level at which they consume it. The high level of interaction between these two or three groups is also unprecedented, as flexible, online content has only recently become available on a mass, consumer-based scale.
Social consumers have an important role as they create a widespread audience; they are the reason for information evolution partly because they can be marketed to. This in turn, then affects their responses, wants and needs and so on. Blogs, as understood in this context, offer a different understanding of community than userboards or chat rooms in that a single writer (blogger), or a single group of writers, directs conversation. This is similar to email lists, which Marc A. Smith describes as…”operat[ing] as benign dictatorships sustained by the monopoly power that the list owner wields over the boundaries and content of their group11.” A blog’s readers are also generally beholden to the topic of the post, or risk being ostracized by other readers in some way (moderation, snarky responses, etc.). This presents an interesting power dynamic in the Web 2.0 world—and is most likely the reason that blogging has been considerably studied, while research on blog commentary and the communities that surround blogs remains somewhat anecdotal.
The dynamics between social consumers and social producers may have been the primary cause in the rise of blog culture and the decline in forum-style software. As the amount of online software has increased, it seems the ratio of social “types” has shifted dramatically. More people fit the consumer type, and thus social and cultural consumption has become one of the primary goals in social production. For example, I pulled the following passage from an interview I conducted with personal bloggers who had very little previous experience:
“Nathan and Karen are two personal bloggers who receive fewer than 1000 hits a month. In conversation, they agreed that blogging felt different than personal journal writing as “you can get feedback on your ideas, and so you’re forced to structure them more.” Public blogging also created motivation to write, “as long as someone, somewhere, is somewhat engaged.” This is a meaningful insight into the structure of the blog format. For these personal bloggers, the primary function of readers and their comments serve as incentive for writing, with the secondary function of the comments being that of information and discussion. …”
My research continued to pull more anecdotal evidence from other bloggers confirming that the abstracted audience of social consumers is one of the main reasons for creating an interactive personal blog, as opposed to a personal diary. The content of the commentary or the surrounding blog community did not appear to be as meaningful to lower traffic sites.
Overall blog commentary is, I believe, a valuable subject of study as the practice funnels active human behavior and social interaction—complex qualitative forces—into traceable, data-rich sources of quantitative measure. Vocations such as trend forecasting, advertising & marketing, social theory research, and information architecture can benefit from its analysis.
But what is the next step in the research and analysis of social behavior online and in social software? I believe it to be cybernetics and artificial intelligence research, which yields some of the best contemporary tools in understanding emergent complex behaviors within systems. From Animals to Animats is an excellent explanatory text on describing the evolution of understanding complex systems. For example, one of the authors, Maja Mataric, notes the invaluable concept of AI emergent behavior coming from “top-down modularity12” He continues on to say:
“However, imposing such modularity minimizes precisely the type of interactions that seem to generate complexity in nature. The global behavior of complex systems, such as groups of social agents, is determined by the local interactions of their constituent parts. These interactions merit careful study in order to understand the global behavior of the society. In natural systems, such interactions resulted in the evolution of complex and stable behaviors that do not lend themselves to traditional, top-down style of analysis. In order to reach that level of complexity synthetically (emphasis added), such behaviors must be generated through a similar, interaction-driven, incrementally-refined process.13”
The article then continues on to describe that approach, a “combination of bottom-up experiments and theory with the purpose of designing, observing, and formalizing emergent behaviors.”
As a short aside, the article also features an interesting table, which lists the “basic interaction primitives.” They are: Collision Avoidance, Following, Dispersion, Aggregation, Homing, and Flocking. For a further expansion on the topic, I’d like to explore how these terms might help to better describe the origins and trajectory of a something like an online viral joke meme be passed along through social groups? Or what is the best manner in which to quantify these qualities in a social software setting?
Lastly, at the 2008 symposium for the Association for the Advancement of Artificial Intelligence, research was presented on the topic of “Emotion, Personality, and Social Behavior.” I found this section to be particularly relevant for building finer models of social behavior that capture a subtlety not yet found in formalized mathematic systems. The seminar’s description stated, “Recent years have witnessed increased interest in modeling emotion and personality in cognitive, agent and robot architectures. Increasingly, the focus has been on exploring the role of affective factors in social behavior. These include emotions, moods, personality traits, and attitudes. Researchers and practitioners in areas such as social robotics, game development, affective HCI, and synthetic agents are increasingly recognizing the importance of these affective factors in developing believable, realistic and robust agents, and effective human-machine interfaces.”
Some of the questions explored during the seminar included:
○ How do we understand the interactions between emotion, personality, and social behavior?
○ What can they tell us about cognitive / cognitive-affective architecture?
○ How can we make systems that facilitate social interaction among humans or among humans and artificial characters?
○ How can considerations of affective factors contribute to more effective human-computer interaction in general?
○ How do intrapsychic cognition-emotion interactions manifest at the interpersonal level?
I believe addressing the issues of emotion in decision-making organization will be an important step in understanding the finer points of social interaction and emergent social behavior. The motivations and volitions of participants may prove to be just as important to analysis as their actions. Advertising theories seem to have a preternatural intuition into that realm, therefore it might serve complexity theory to examine some of the methods and concepts used in that vocation.
Overall, the study of emergence in social behavior seems to be progressing at an accelerating pace, which is seemingly made possible, though the conjunction of the knowledge of multiple disciplines as well as the methodologies of formalized mathematics, engineering, and the social sciences.
Notes and Citations
1 Lewin, Roger. Complexity:Life at the Edge of Chaos. Chicago: The University of Chicago Press, 1992.
2 Lewin, Roger. Complexity:Life at the Edge of Chaos. Chicago: The University of Chicago Press, 1992: Pp 220
3 M.D., Mesarovic, D. Macko, and Y. Takahara. "Theory of Hierarchal, Multilevel, Systems." Mathematics in Science and Engineering 68(1970): Pp 17
4 Barton, Richard F.. A Primer on Simulation and Gaming. Englewood Cliffs: Prentice-Hall, Inc., 1970: Pp 103
5 M.D., Mesarovic, D. Macko, and Y. Takahara. "Theory of Hierarchal, Multilevel, Systems." Mathematics in Science and Engineering 68(1970): Pp 109
6 Lewin, Roger. Complexity:Life at the Edge of Chaos. Chicago: The University of Chicago Press, 1992: Pp 202-203
7 Smith, Marc and Peter Kollock. Communities in Cyberspace: Perspectives on New Forms of Social Organization. London, Routledge Press, 1999; Pp 6-7
8 Smith, Marc and Peter Kollock. Communities in Cyberspace: Perspectives on New Forms of Social Organization. London, Routledge Press, 1999; Pp 6
9 Smith, Marc and Peter Kollock. Communities in Cyberspace: Perspectives on New Forms of Social Organization. London, Routledge Press, 1999; Pp 4
10 "Entry on Jorn Barger." Wikipedia.org. 31 March 2008. Wikipedia. 16 May 2008 <http://en.wikipedia.org/wiki/Jorn_Barger>.
11 Smith, Marc and Peter Kollock. Communities in Cyberspace: Perspectives on New Forms of Social Organization. London, Routledge Press, 1999; Pp 5
12 Maratic, Maja J. "Emergent Behaviors: From Single Interactions to Collective Intelligence."From Animals to Animats 2: Proceedings of the Second International Conference on Simulation of Adaptive Behavior. 1sr ed. 1993: Pp 432
13 Maratic, Maja J. "Emergent Behaviors: From Single Interactions to Collective Intelligence."From Animals to Animats 2: Proceedings of the Second International Conference on Simulation of Adaptive Behavior. 1sr ed. 1993: Pp 432
14 Maratic, Maja J. "Emergent Behaviors: From Single Interactions to Collective Intelligence."From Animals to Animats 2: Proceedings of the Second International Conference on Simulation of Adaptive Behavior. 1sr ed. 1993. Pp 435
