Chaos Theory

Over the last fifty years Chaos Theory has evolved as a new science which assumes that the natural order is irregular, discontinuous and erratic (Gleick, 1987). Newtonian physics implies that there is a rational order to everything, that we can predict the events of any system if we are able to plug in enough variables. The idea behind “chaos theory” is that we can predict what systems might do, but we cannot be sure. Everything exists as a series of possibilities.

Since the beginning of the twentieth century several other sciences have evolved that have sought to solve the problems in prior models. For example, the theory of relativity eliminates the illusion of absolute time and space. Quantum mechanics eliminates the Newtonian dream of controllable measurement processes, as well as the fantasy of deterministic predictability. Chaos theory, the third new science, embraces irregularity as a norm. Scientists from different fields have begun to observe the regular patterns within the irregularity of the natural world.

In the old views of nature, notes Gleick (1987), it was held that simple systems behave in simple ways while complex systems imply complex causes. In the new view, it is believed that simple systems give rise to complex behaviors and complex systems give rise to simple behaviors (Snyder, 1995).

The new science of chaos centers around two points. The first is the exploration of the hidden order that exists within the chaotic systems. The second is the study of how self-organization emerges from chaos (Hayles, 1990).

The three principal conditions for a chaotic system are: (1) that it operates in a non-linear way; (2) that it is iterative (the output of one cycle becomes the input of the next); and (3) that small variations in initial conditions lead to large differences in outcomes. Many systems within educational organizations appear to meet these conditions (Cunningham, 2000).

The concepts of chaos theory can explain the way schools work. For example, teachers do not exist as separate entities, but are affected by the relationships that exist within schools. It may also shed some light on how we can deal with and understand how things in our classrooms, schools, and entire communities are interrelated and all reflect in some manner upon each other.

There are several aspects of chaos theory such as nonlinearity, complexity, butterfly effect, fractals and feedback mechanisms that may have significance for educational settings.

Nonlinearity

In a linear system there is a simple cause and effect relationship; A causes B which causes C, and so on. However, a chaotic system is nonlinear. A may not necessarily cause B at all times. Lots of variables come into play and interact with each other. School systems look like nonlinear chaotic systems, too. In school district A, the purchase of new computers might have a positive impact on student achievement, while in school district B, this might bring little or no gain in student achievement.

It is widely believed that experienced teachers have better classroom control. If you have a veteran teacher in a classroom, you will have an orderly environment and the administrators, thinking in a linear way, might believe that the more veteran teachers in a building, the more orderly the environment will be. That might not be the case in every school district, especially in urban schools; there are instances where young and inexperienced teachers contribute positively to the school environment much more than veteran teachers.

Complexity

Chaotic systems take complex forms, making their precise measurement difficult if not impossible (Glickman, 2001). Different measurement instruments have been put in place to evaluate and compare the performance of a school. However, due to the complex nature of schools, none of these assessment methods seem to measure precisely the school performance and have very limited validity for the following reasons (Cunningham, 2000):

• The prior achievement of pupils is not taken into account and this is a major factor in pupil achievement at a later stage.

• Schools are differentially effective in different subjects and with pupils of different ability, which is not reflected in a single figure.

• Schools change over time; however, the achievement data used reflects only one group and is essentially historical data.

• Student mobility between schools is not reflected in the assessment.

• Social factors, sex of students, ethnic origin and social background are not taken into account. These factors are out of the school’s control.

Therefore, assessing school performance and comparing one to another have become increasingly difficult given the complex nature of today’s schools.

The Butterfly Effect

The butterfly effect means that a small and seemingly unrelated event in one part of a system can have enormous effects on the other parts of the system. Theoretical meteorologist Edward Lorenz made the term ‘butterfly effect’ famous when he argued that a butterfly stirring its wings in Bejing today could unleash powerful storms in New York city next month. One implication of sensitive dependence on initial conditions is the impossibility of predicting not only next year’s weather, but the long term future of any chaotic system (Glickman, 2001).

In terms of school improvement, what we understand from the butterfly effect is that it is impossible to predict the long-term effects of school improvement efforts. Planning in a chaotic system like a school should be medium range (one or two years) rather than long range (five to ten years). Formal planning in an unpredictable system needs to focus on process rather than product with the goal of producing “a stream of wise decisions designed to achieve the mission of the organization” (Patterson, Stewart and Purkey, 1986).

The butterfly effect ensures that no lesson will ever go completely as planned, or have the same effect on any two students. It indicates the need for teacher flexibility in teaching, as well as the need for individual attention to students, each of whom is experiencing a given lesson within his or her own personal context (Glickman, 2001).

Fractals

A fractal is a geometric shape that is similar to itself at different scales. Mid-sized branches of a tree are remarkably similar in shape to the larger branches from which they come. Smaller branches, in turn, are the same shape as the mid-sized branches from which they come, and so on.

Through work with fractal generations, it has become apparent to scientists that predictability does exist (known shapes re-appear), and randomness plays an important and unexpected role. What has been learned is that, within chaotic and seemingly unpredictable systems, structures of order exist through which the system recreates itself.

Complex social systems can also reveal self-similarity on different scales: at each level of the system, specific patterns of organization and culture reappear. Like fractals in nature, schools reveal self-similarity in different scales. For example, a school-wide staff development day, a department meeting, a classroom lesson, and a halfway interaction between a teacher and student might all reveal the same cultural characteristic. Thus, reflective inquiry at the school, team, classroom and individual level can help educators better understand their school culture, change needed, and pathways to improvement (Glickman, 2001).

By being reflective practitioners, teachers can develop their teaching skills, acquire more insightful experience in their fields and learn to look at problems from a different perspective. They also understand their weaknesses, areas of strengths and recognize the repeating patterns of their teaching styles.

Feedback Mechanisms

Chaotic systems contain feedback loops enabling outputs to feed back into the system as input. Feedback can bring stability or turbulence to a system. For example, a thermostat is a feedback mechanism that causes temperature stability. Conversely, when the sound from a loudspeaker feeds back through a microphone, it is rapidly magnified to create a disruptive shriek (Gleick, 1987). Feedback can also cause a system to move toward greater levels of complexity.

Feedback in schools can take the form of student performance data, survey results, quality circles, third party reviews, and so forth. The important thing is that meaningful data on the results of change efforts be made available to teachers, and that they be given opportunities to reflect on the data and redirect their change efforts accordingly.

With all the unpredictability present in classrooms, beneficial feedback is critical for both teachers and students. For teachers, student performance data, direct student feedback, and classroom observation data can all assist them to improve classroom instruction. For students, feedback on their cognitive and affective performance-from teachers, parents, and peers-is an essential part of the learning process. The fact that in chaotic systems like classrooms output becomes input means that the artificial distinctions we often draw between learning and assessment need to be removed: in reality, learning and assessment cannot be separated (Glickman, 2001).

Conclusion

The deterministic view of education that schools are simplistic, cause-effect systems which can be easily manipulated, quantized and controlled is not addressing the problems of today’s schools. From an alternative perspective, chaos theory gives us an understanding that the things we consider unimportant or trivial in our daily lives might have an equal weight in terms of affecting the results as the things we consider important. Just as it is characterized in the Qur’anic teaching that every minute thing or action is recorded in a Book regardless of its proportion. “Whatever your preoccupation (O Messenger), and whatever discourse from Him in this (Qur’an) you may be reciting, and whatever work you (O people) may be doing, We are certainly witness over you while you are engaged in it. Not an atom’s weight of whatever there is in the earth or in the heaven escapes your Lord, nor is there anything smaller than that, or greater, but it is (recorded) in a Manifest Book” (Yunus 10:61). It further suggests that everything we do has a significant impact on us, our communities and ultimately society as a whole.

In conclusion, chaos theory has the potential to offer deeper understanding of how today’s schools function in an ever changing world of our times.

Aydin Kara is a graduate student at University of Dayton. He studies educational leadership and administration. He can be reached at aydinkara33@hotmail.com.

References

• Cunningham, R. 2000. Chaos, Complexity and the study of Education Communities. Institute of Education.
• Gleick, J. 1987. Chaos: Making a new science. New York: Penguin Books.
• Glickman, Carl D. 2001. Supervision and Instructional Leadership: Allyn and Bacon
• Lortie, D. C. 1975. Schoolteacher. Chicago: University of Chicago Press.
• Patterson, J. L., Purkey, S. C., and Parker, J. V. 1986. Productive school systems for a nonrational world. Alexandria, VA: Association for Supervision and Curriculum Development.

As a relatively new and exciting science, chaos science grew very slowly during its infancy. Yet in the last decade, due to active research in many areas, it has became one of the hottest topics in academia as well as the popular science press. Many books have been published, and millions of Internet pages have been designed full of fractal pictures.1 Given that chaos is associated with disorder or confusion in a system or condition, why does it continue to attract so many people?

History of chaos

To understand chaos, one first has to understand the Newtonian worldview. Sir Isaac Newton’s (1642-1727) development of the calculus and laws of classical mechanics began a scientific revolution in seventeenth-century Europe that caused all subsequent scientists to view nature from a profoundly different perspective. Now that they finally could determine the dynamics of bodies by simple equations, they believed that they had found the ultimate eternal rules that shape the universe.

French physicist Pierre-Simon Laplace (1749-1827), who based his work upon Newton’s work, is credited with the following famous quotation (often referred to as Laplace’s Demon): ‘We may regard the present state of the universe as the effect of its past and the cause of its future. An intellect which at any given moment knew all of the forces that animate nature and the mutual positions of the beings that compose it, if this intellect were vast enough to submit the data to analysis, could condense into a single formula the movement of the greatest bodies of the universe and that of the lightest atom; for such an intellect nothing could be uncertain and the future just like the past would be present before its eyes.’2

Laplace’s Demon states the idea of determinism, that the past completely determines the future. One can clearly see why determinism was so attractive to scientists at that time. However, in Laplace’s word everything was predetermined: no chance, no choice, no uncertainty. A solid, inevitable destiny had frozen the events in every corner of the past and is continuously spreading out to the future to do same there. Determinism apparently invokes the idea that whole universe is like a clock. God set it in motion at the beginning of creation and then removed Himself, for everything had been planed before. The ideas that there was no place for free will and that God could not interfere killed the belief in a soul and, consequently, in spirituality. Philosophers and scientists have discussed this for many years. Determinism affected many philosophies and triggered the major ideological movements of during eighteenth century, especially in Europe.

Toward the end of the 1800s, mathematicians and scientists began encountering some very difficult equations, some of which we know today are unsolvable. The most troublesome are various nonlinear differential equations. Even though it looks like such a simple and totally deterministic system, the problem of three bodies attracting each other with purely gravitational forces (e.g., the sun, Earth, and moon triple) turns out to be missing an exact solution. At first, such problems were cast-off as special cases and largely ignored.

One reason for this also might come from the fascinating world of quantum mechanics, which dazzled even the great physicists, and the lack of fast computers at that time. When these equations finally were studied in detail, a fundamental change that would ultimately overthrow determinism began to occur in mathematics and science. An indication of the science that would be come to be known as ‘chaos’ began to appear.

Why does chaos interest people?

In contrast to its common usage, chaos does not actually mean disorder or confusion. By definition, it should occur in well-defined orderly systems. However, most natural physical systems often can exhibit an unpredictable or intractable behavior in the long run, even though the system is defined by clear-cut orderly mechanisms. In this sense, chaos can be defined as unpredictability rather than disorder.

For example, meteorologists use 12 sets of well-defined equations to forecast the weather. They relate such atmospheric parameters as pressure, temperature, flow speed, and time to each other. One can make a computer program that calculates the parameters’ final values by taking any initial conditions as the run’s starting point. In principle, therefore, if we know the initial temperature, pressure, and time values that describe today’s weather conditions, it is possible to derive tomorrow’s weather conditions by running a computer program, which is nothing more than a chain reaction of numerical iterations.

However, in practice, initial conditions cannot be measured exactly and so contain a degree of uncertainty. But since the system’s governing laws are known, one may estimate the effect of errors on future results. Hence, instead of giving the exact results, one can provide an approximate range of possibilities. This range can still be very useful, provided that the deviations do not stray too far from the actual values. In addition, knowing how the error grows in the system might help us understand and control the systems. But if we apply these error estimates to weather forecast equations, we will encounter a large problem, for errors grow exponentially in such systems. Even a tiny deviation at the beginning can create huge deviations from the actual values. It also can provide unrelated or nonsensical results.

An example of chaotic systems

This numerical behavior was first observed by the meteorologist Edward Lorenz, a pioneer in modern chaos work. Fascinated by the results he obtained, in the early 1960s he gave an interesting metaphor to explain the situation of high sensitivity to initial conditions: A butterfly’s slight wing movement (i.e., a little deviation from the initial conditions) can change the future in a way that causes some chain reaction that ultimately result in a hurricane.

Such systems that exhibit a very high sensitivity to initial conditions are called chaotic systems. Chaos comes from the mathematical properties hidden in the equations defining the system, and such unpredictability cannot be removed. Even if the measurements’ quality could be improved by minimizing errors, chaos never disappears from a chaotic system.

One may suppose that chaos occurs in complicated systems, such as weather forecast systems having 12 sets of equations. But even much simpler systems, such as billiards, can exhibit a very high degree chaos. A usual billiard system consists of many balls and a rectangular shaped table. I challenge master billiard players by requesting them to play the game in a stadium-shaped table. I am sure that they will find it difficult to do so, because such billiard tables would be chaotic systems.

If a system is defined as chaotic, this does not necessarily mean that its behavior is totally undefined all the time. As in stadium billiards, a ball has to be inside the billiards, so it should be somewhere on the table, even though sometimes we cannot foretell its exact position because of chaos. Besides, if you send the ball with a velocity perpendicular to a straight side, it will bounce back and forth between the two sides forever. Therefore, depending on which initial conditions are taken, chaotic systems also can show characteristics of regular motion.

A 3-body problem (in general n-body problems) such as the sun, Earth, and moon system, is a chaotic system. But since we can predict the motions of these celestial objects with great precision for many years in the future, why do we call this system chaotic? This triple system possesses a very special set of conditions: distance between bodies, their masses, and their velocities. These parameters cause it to exhibit near-regular behavior. It is analogous to the example of stadium billiards given above, for this triple system bounces back and forth between the table’s sides.

Conditions for chaos

Chaos also can be caused by other factors than just uncertainties measured in the initial conditions. Scientists generally define hypothetical systems by isolating them from the outside world in order to simplify them as much as possible. However, as even objects in the real world that are far apart interact with each other, no system in the real world can be isolated. Given this, a closed (isolated) system might be defined as a fluctuating approximation to its real counterpart, which is changing in an unpredictable manner all the time. In short, even though we would know the exact initial conditions, the actual system could be chaotic due to changes in the approximate system. In this sense, many physical systems have an inclination toward being chaotic.

Due to its maximum complexity, the universe is the largest chaotic system. Observable regular patterns in special parts of that system repeat themselves in time. While the rest of the flows are unpredictable, they are not totally irregular, abrupt, or disordered. Just like whirls in a flowing river, they are in a kind of free motion searching for convenient conditions in which to give birth to organized structures.

The future of chaos

Chaos gives today’s scientist a new worldview, for Newton’s concrete, cold, and deterministic one has been shaken by the uncertainty principle of quantum mechanics. No one ever thought that the Newtonian worldview could be replaced. However, now scientists are more likely to be open to chance and choice than their predecessors. The question is whether chaos theory will cause large revolutions in how we understand the universe. However, the existing excitement, expanding research and growing number of articles, and its numerous applications from economy to biology, seem to indicate that a surprise improvement might not be so far off. ‘

Footnotes

1. Fractal: A geometric pattern that is repeated at ever smaller scales to produce irregular shapes and surfaces that cannot be represented by classical geometry. Fractals are used especially in computer modeling of irregular patterns and structures in nature.
2. ‘Chaos and Fractals: Laplace’s Demon.’ Online at: www.pha.jhu.edu/ ldb/seminar/laplace.html.