Wilensky, 1999
From Eduwiki
summarized by David Kang
Introduction
In studying scientific models, students are often confused by "levels" of description that can be used to characterize a system with lots of interacting parts. An example is how traffic jams move backward while the individual cars move slowly forward. This notion of levels is useful for understanding a wide range of phenomena in the world. Wilensky argues for an expanded role for the concept of “levels” in the study of science. An understanding of levels is becoming even more important with the increased presence of the computer in our culture. Computers are used as tools for exploring the idea of levels, and computers themselves are best understood by thinking in terms of levels.
What are Levels Anyway?: Leveling about Levels
“Levels” can have many different meanings. Often, people think of levels in terms of hierarchies of control. A very different meaning of levels, which Wilensky calls the “container view,” is based on the idea of parts and wholes. In this paper, Wilensky focuses on yet another meaning of levels, which he calls the “emergent view” of levels. The focus is on levels that arise from interactions of objects at lower levels – like the traffic jam that emerged from the interactions among the cars. This notion of levels is central to understanding the emerging “sciences of complexity”– the investigation of how complex phenomena can arise from simple components and simple interactions. Research into complex systems touches on some of the deepest issues in science and philosophy–order vs. chaos, randomness vs. determinacy, analysis vs. synthesis. Indeed, the notion of levels is a powerful tool for understanding some of the most longstanding issues in science.
Leveling Stories
These stories are case studies that draw largely on experiences with StarLogo, a computer modeling environment designed explicitly for exploring systems with multiple interacting objects.There are two general ways for engaging students in using StarLogo. In some cases, students use StarLogo to build models “from scratch”–that is, they choose phenomena of interest to them (such as the formation of traffic jams) and write StarLogo programs to model (and explore the workings of) the phenomena. In other cases, researchers introduce a pre-built StarLogo model and engage students in discussing the workings of the model–and then invite them to modify or extend the model to deepen their understanding of it. The cases examine how students developed an understanding of emergent levels, and how this understanding helped them gain insight into the phenomena they were investigating. Each story focuses on a different scientific domain and each highlights a different theme.
Slime
Thinking about the life cycle of slime mold is an effective entry point for introducing students to the concept of levels. As long as food is plentiful, slime-mold cells exist independently as tiny amoebas. But when food becomes scarce, the slime-mold cells stop reproducing and move towards one another, forming a cluster with tens of thousands of cells. To engage students in exploring the behavior of slime mold–and, more broadly, exploring the nature of levels, the researchers wrote a StarLogo program that models the slime-mold aggregation process. The life cycle of slime mold touches on one of the most fundamental issues that arises when thinking about levels: What is an object? The very question of “objectness” becomes a question of “levels.” Objects that are viewed as singular at one level are best viewed as plural at another level.
Gas in a Box
It is a story about Harry, a science and mathematics teacher in the Boston public schools, who was very interested in the behavior of gases. He wanted to find out why the energies of the particles in a gas form a stable distribution called a Maxwell-Boltzman distribution. To answer this question, Harry decided to use StarLogo to build a model of gas particles in a box. By running various permutations of his programs, he found that this distribution was not the result of a specific set of initial conditions, but that any gas, no matter how the particles speeds were initialized, would attain this stable distribution. In a one-dimensional world, he concluded, average speed would stay constant; in a multi-dimensional world, particle distributions become non-uniform and this leads to an asymmetric distribution. Harry needed to understand there were two levels (and the interactions between them) in order to develop a deeper understanding of the Maxwell- Boltzman distribution. On one level, the particles reacted with other particles. On another level, the particles reacted to the entire system changing.
Predator-Prey
In this story, they described the use of StarLogo to make sense of the dynamics of predatory-prey interactions. Benjamin, a student at a Boston-area high school, set out to create a StarLogo program that would simulate the dynamics of an ecosystem. At the core of his simulation were turtles and food. His basic idea was simple: turtles that eat a lot of food reproduce, and turtles that don’t eat enough food die. When he ran his program with different variables, the turtle population oscillated, but out of phase with the food. Another student, Gabrielle, worked on a similar model using wolves and sheep rather than turtles and food. She wondered what would happen if she started the simulation with a very large number of sheep? She guessed that the sheep would then dominate the ecosystem. When Gabrielle ran the program, she was in for a surprise: all of the sheep died. Gabrielle assumed that each sheep had a particular chance of survival, and then added more sheep to increase the chances of a large group surviving. In this way of thinking, the chances just add up. But in fact, there is a feedback mechanism in the system, so that increased numbers result in reduced chances. The oscillating behavior in Benjamin’s and Gabrielle’s models is characteristic of all types of predator-prey systems.
Conclusion: Reaching for Another Level
This paper illustrates how computers can be used to introduce the concept of levels into science education. The concept of levels are: (1)critically important to the understanding of many scientific phenomena and many foundational philosophical questions; (2) greatly under-represented in today’s science-education curricula; and (3) much more easily explored and understood through the use of computational media than through any previous media.
