and other universities in the area present their work, focusing on their methods as well as the phenomena they are investigating. Typically, a half-hour presenta- tion is followed by an hour of lively discussion in these well-attended meetings. The second structure is an invi- tation to an internationally known scholar each semes- ter. The scholar gives a public talk and then a smaller symposium that concentrates on methodology. The third structure is a CIQR certificate program, where a certificate is offered to those graduate students who take specified method-oriented courses from the gen- eral curriculum at the university and then a special proseminar. The proseminar requires that the students engage in and jointly discuss research projects that they are undertaking. They then present their work to CIQR members at a meeting for that purpose. After only a year, this program had already granted certificates to 10 graduate students and 1 faculty member. The center also plans to engage in community action research.
The CIQR website includes a description of the center, the original proposals for the center and its cer- tificate program, a list of all the CIQR external speak- ers and their topics, a description of all the monthly presentations, announcements of coming events, a newsletter, and a list of the subcommittees along with their members and functions, and a sign-up procedure for those wishing to become CIQR members.
Fred Evans
Websites
Center for Interpretive and Qualitative Research:
http://www.ciqr.duq.edu
relationship, thexs are not prechosen (except for the beginning or “seed”x) but rather evolve from they(as a function ofx) being inserted back into the equation as a newx.Such recursive development can be deter- ministic as one looks back interpretively, but to predict with any accuracy from Situation 1 to Situation 4 is at best probabilistic––due to intervening Situations 2 and 3––with the attempt of probabilistic predictions for Situation 8 or 9 being near to impossible. This recur- sive patterning, often labeled “deterministic but not predictable,” accounts for weather predictions being given only in short-term probabilistic frames. In a metaphoric sense, to see Situation 2 emerging from Situation 1 and leading on, and indeed influencing (but not determining) Situation 3, brings forth the need for inquiry-oriented research to be interpretive, open- ended, probabilistic, historically situated, and cultur- ally contextual.
At the turn of the 20th century, Henri Poincaré, map- ping the results of measuring the gravitational effects of three moving bodies interacting with one another, saw a “monster” (the nonlinear) appearing and cried out
“prédiction devient impossible.” Only with supercom- puters are humans able to tame and train this monster.
Now there are strong advocates for high school classes to teach courses in nonlinear mathematics using the power of computers and graphing the results.
Research methodologies in the human and social sci- ences informed by chaos and complexity theories are not yet framed. It seems, however, that this type of inquiry, oriented to understanding change over time and involving complex situations, might use a combination of (nonlinear mathematical) pattern analysis and anthro- pological inquiry (a layering of interpretations). These procedures foreground relationships, with reality being nothing set “out there” for discovery but rather emerg- ing from dynamic relationships viewed over time.
Research of all sorts has always required interpre- tation––the questioning of the procedures used and the conclusions drawn by the researcher––but such interpretation has been against a (presumed) stable background. When interpretation is understood as a dynamic process, both reiterative and reflexive, influ- encing the process and direction of inquiry, it still has a historical background of past experiences and pat- terns to play against, but it also has the possibility of bringing forth the not-yet-seen or yet-to-be imagined.
Here in the realms of the not-yet-explored, human intention and creativity come into play as inquiry becomes richer and thicker through the layering of interpretations.
Chaos Theory
Those who study chaos theory accept that the world and universe in which we live are filled with turbulence, fractalness, and difference. Such dynamism is the very nature of our world/universe. Equilibrium, balance, simple harmony and conformity, norming, classifica- tion, and even equality and justice are human con- structs, abstractions placed by us for what William James called “the blooming, buzzing confusion” in which we find ourselves. Such “conceptual maps” are too often taken as physical realities that make predic- tion and control overly simple and overly orderly.
A dominant assumption in this rationalist way of thinking—patriarchal, modernist, analytic–referential—
is that, when all or enough facts are collected, accurate predictions can be made. In his 1812 treatise Analytic Theory of Probability, Pierre-Simon, marquis de Laplace posited a superior intelligence––often called Laplace’s “demon”––that, through its enormous intel- lectual powers, would “embrace in the same formula the movements of the greatest bodies and those of the light- est atoms; for it, nothing would be uncertain and the future, as the past, would be present before its eyes.”
A century later, Poincaré became aware that Laplace’s vision was impossible. As Poincaré said in 1952, even if nature’s laws held no secret for us, we would still not be able to predict perfectly or even well, for in the interactions of phenomena (be they atoms or events), “small differences in the initial con- ditions produce very great ones in the final phenom- ena”; hence, Poincaré was aware that Newton’s calculus works on only two interacting phenomena, with the intrusion of a third yielding the mathematical monstrosity of nonlinearity. This monster (nonlinear equations) lay dormant for most of the next 75 years.
As is famously known by now, Edward Lorenz, work- ing on weather predictions during the 1960s, substi- tuted data carried to three decimals places for data he already had carried to six decimal places. His assump- tion, still current at that time, was that small differ- ences would have only small effects; cause and effect would be orderly and proportionate, not disorderly. To his surprise, over time the new prediction data on his printout began to deviate more and more from his past prediction data until the relation between the three- figure and six-figure sets of data—different initially by .001—were eventually incompatible. During the 1970s, with the advent of supercomputers and their tremendous powers of iteration (which pattern analy- sis of nonlinear equations requires), chaos theory was Chaos and Complexity Theories———75
born along with its famous metaphor, “A butterfly flapping its wings in Rio can cause a typhoon in Tokyo.” The point here is that accumulated develop- ment need not be linear; thus, it is difficult, indeed often impossible, to predict effects from causes.
Weather predictions today are given in short-term probabilities. Stock market moves appear to be ran- dom. Human development takes many pathways, with too many factors interacting to make developmental predictions (e.g., IQ) useful. Tsunamis, rogue waves, typhoons, tornados, earthquakes, and avalanches appear suddenly and with little (if any) warning.
Researchers in these areas, and in human development and actions, need to be aware not only that the map is not the territory but also that the territory is continu- ally shifting, as are the researchers. Future researchers may well need to operate from a nonlinear mathemat- ical frame, including the study of logistic frames.
Logistic frames deal with systems that are inversely interactive such as those of predator/prey, birth/death, and message/noise. For example, as predators increase, prey decrease, but at a certain point—with surviving prey becoming more skillful and faster than predators—an inverse relationship occurs; prey increase because they are caught less, and predators decrease because they are unable to catch the prey due to the development of lazy habits.
Equations to describe this relationship are usually written in the formF(x) =rx(1–x), where r is a con- stant (e.g., food, space, information) and 1–x is an inversion of the originalx,limiting (but by no means centering) the interactive relationship. The notion of a system being bounded but not centered––one that is dynamically changing––offers challenges and oppor- tunities to any researcher.
An interesting aspect of the logistic equation (above) is that as r increases from 1 to 2, doubling occurs (the output in the equation vacillates between two numbers––a boom/bust, 7 good years/7 bad years bifurcation). Another doubling occurs as r moves to 3, whereas at 3.57 (where doublings are fast and furi- ous) chaos sets in. From this third doubling arises the word chaos in a mathematical sense: Period 3 implies chaos. What is even more intriguing here is that in this chaotic realm (≥3.57 forr), spaces of reg- ular simple order appear. In a complexivist frame, then, order and chaos are not dichotomous (as mod- ernist and Newton frames posit) but rather entwined.
A good/bad, black/white, either/or, right/wrong frame gives way— not to a compromise frame between two
dichotomous poles but rather to a “third space” frame where entirely new possibilities can emerge. The chal- lenge for researchers, then, is to let go of an orienta- tion that looks for and finds cause/effect, either/or simple relationships and to develop research designs that explore the depth, richness, and thickness of the complex relationships that exist in any given situation.
Complexity Theory
It is possible to say that chaoticians study the turbu- lent aspects of nature with an emphasis on both accel- erated development––the dramatic effects of small differences over time––and the intertwining of order/disorder in this development. Regarding com- plexity theory, it is possible to say that complexivists study nature’s ability to remain “stable” by accommo- dating and using small differences. Such dynamic sta- bility requires the system to use just the right amount of perturbation for its continued functioning. All liv- ing systems are dynamically stable. The human brain is one example; it functions as it is perturbed in “just the right amount.” It is impossible to predict the right amount of perturbation, somewhere between too much and too little disequilibrium. The human body is not so much a smoothly running machine as it is a complex adaptive system.
In complex adaptive systems, concepts such as net- works, self-organization (or self-regulation), feedback loops, self-similarity (or nestings), and disequilibrium all are important and have implications for human learning. Learning is a natural activity of the human species, and attention to the concepts inherent in chaos and complexity theories produce an epistemol- ogy quite different from the current one based on a Newtonian sense of stability and conformity. An idea from these theories, important to social science researchers, is that of dynamic networks and their feedback loops.
A dynamic network, “alive” due to its constantly adapting to change and new input, may or may not have major nodes. There is not, however, a central dominating node. Rather, there are interconnected pathways, as in a power or communications grid and in the human nervous or immune systems. In such systems, multiple pathways exist. Information flows in, around, and through these pathways. As informa- tion flows from one local node or situation to another local node or situation, it is changed as it intersects with other pathways (or experiences). The concept of 76———Chaos and Complexity Theories
there being one and only one major pathway, or royal road, to interpretation, learning, and teaching is quite nonsensical to a complexity theorist. Each local situa- tion has its own uniqueness, and as one local situation connects to another local situation—as happens in a network, even one dominated by major arteries—
interpretation/understanding becomes contextualized.
Metaphorically, if one wishes to say the phrase
“research says,” it is necessary to realize that research speaks with a dual emphasis; each situation is unique, not generalizable, but each situation also is connected to and interacts with other situations. A collection (or nexus) of situations can form a pattern or system made up of interconnected locals. Although each local requires its own contextualized interpretation, rela- tionships among situations, in forming patterns or systems, can show enough similarity to produce a meta-pattern. It is this issue of connecting local situa- tion to local situation, in a meta-pattern frame, that so bothered and consumed anthropological researcher Gregory Bateson. During his 20th-century lifetime, his search was for “the pattern that connects.”
The novel idea of feedback, introduced to systems thinking at the Macy Conferences during the 1950s, provided Bateson not only with insight into the pat- tern that connects but also with a new way of thinking about research. Drawing a network (visually), with its set of interconnected nodes, one quickly sees that it is nonlinear, with connections going in all sorts of direc- tions. Because of this nonlinearity, information (or a message) flows around various pathways, often cycling back to its origin. Such returning, recursing, and feeding back bring new information gathered along the way, and thus the original is “seen yet again for the first time.” Thus, interpretation of a situation is enriched by being layered with more interpretations.
Interpretive Inquiry
Chaos and complexity theories, the new sciences, are still in their early stages of development and, hence, do not yet have a well-formed research methodology.
Being framed by the concept of orderly disorder, how- ever, they potentially offer to researchers a different method of doing research—interpretive inquiry.
Inquiry, in its act, requires dialogue (or conversation), and dialogue requires interpretation. Interpretation is a reciprocal act, between text and reader, between situation and researcher. Each influences/directs the other. In this mutually interactive relationship, new
ideas, understandings, and insights emerge. Feedback (or recursive loops) becomes a powerful vehicle.
Using such a vehicle, it is possible to conceive of a researcher exploring a situation and then asking others to explore not only the same situation but also the researcher’s own explorations. Recursively, the researcher can then explore the interpretations of those critiquing her or his interpretations. As such a recursive method goes on, spaces open between the interpretations. These spaces, in between boundaries, are often called liminal spaces or third spaces. It is in these spaces that depth of meaning and the creation of new meaning reside. Such a (layering) method, yet to be fully developed, may well be what chaos and com- plexity theories have to offer researchers.
Research Implications
Research of the 20th century was heavily influenced by analytic quantitative methods. These methods assume a stable base, work from closed system assumptions, and are reductionist and linear in nature. Over a number of recent decades, qualitative methods have come to the fore in a number of disciplines or professions. To the degree that qualitative methods favor triangulation, they mimic quantitative methods and their closed sys- tems frame. To the degree that qualitative methods are narrative, personal, and cultural, they work from an open systems frame and yield the possibility of the newness emerging. The key distinction here is the dif- ference between proving (the essential nature of research) and probing (the essential nature of inquiry).
The former is closed in the sense that it is designed to come to a definite conclusion. The latter is open in the sense that it is designed to explore possibilities inherent in a situation. Each has its own methodologies or sets of operation. Research with a quantitative bent follows definite, preset, clearly stated procedures. The frame is one of either/or, as in one procedure/pill being better (more effective) than another, or the frame is one of finding a definite statement or fact, as in historical or legal research. In either case, there is a sense of certainty—of proving—hence the anthropomorphic phrase “research says.” In a proving, certainty-desired model, the logic used is that of domination.
Research with a qualitative—nontriangulated—
bent, often labeled as subjective for its emphasis on the personal and narrative, is more open-ended. Here experience, not validity, dominates. Human experience brings in intentionality, conscious reflection, hope, and Chaos and Complexity Theories———77
angst––all of which one would label under the term human condition. It is narrative that highlights this condition, it is narrative that probes this condition, and it is narrative with its interpretive methodology that brings to a situation its “truth.” Such truth is not prov- able but is felt. The situation is not objectified but is what Jerome Bruner called “subjunctified”––
“trafficking in human possibilities.” Research in this mode is far more akin to interpretive inquiry than to usual (and traditional) concepts of research.
Wm. E. Doll, Jr.
See alsoInterpretive Inquiry; Liminal Perspective
Further Readings
Bak, P. (1996).How nature works.New York: Springer- Verlag.
Bruner, J. (1986). Two modes of thought. InActual minds, possible worlds(pp. 11–43). Cambridge, MA: Harvard University Press.
Davis, B., & Sumara, D. (2006).Complexity and education.
Mahwah, NJ: Lawrence Erlbaum.
Doll, W., Fleener, M. J., Trueit, D., & St. Julien, J. (2005).Chaos, complexity, curriculum, and culture.New York: Peter Lang.
Gleick, J. (1987).Chaos: The making of a new science.New York: Penguin.
Hayles, K. (1991). Introduction: Complex dynamics in literature and science. InChaos and order(pp. 1–33).
Chicago: University of Chicago Press.
Iser, W. (2000).Range of interpretation.New York: Columbia University Press.
Johns, M. D., Chen, S. S., & Hall, G. J. (2004).Online social research: Methods, issues, and ethics.New York: Peter Lang.
Kauffman, S. (1995).At home in the universe.New York:
Oxford University Press.
Kauffman, S. (2000).Investigations.New York: Oxford University Press.
Lansing. J. S. (2003). Complex adaptive systems.Annual Review of Anthropology, 32,183–204.
Laplace, P. S. (1812).Théorie Analytique des Probabilité.
Paris: Courcier.
Lorenz, E. (1995).The essence of chaos.Seattle: University of Washington Press.
Mainzer, K. (2004).Thinking in complexity.Berlin:
Springer-Verlag.
Poincaré, H. (1952).Science and hypothesis.New York: Dover.
Prigogine, I. (1997).The end of certainty.New York: Free Press.
Prigogine, I., & Stengers, I. (1984).Order out of chaos.New York: Bantam Books.
Serres, M., with Latour, B. (1995).Conversations on science, culture, and time.Ann Arbor: University of Michigan Press.
Waldrop, M. (1992).Complexity.New York: Simon & Schuster.
Wolfram, S. (2002).A new kind of science.Winnipeg, Canada: Wolfram Media.
Websites
New England Complex Systems Institute (NECSI):
http://www.necsi.org
Santa Fe Institute: http://www.santafe.edu