Concord.org - @CONCORD: Volume 4, No. 1, Winter 2000

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Using StarLogoT

by Uri Wilensky

Here are two examples of the kinds of models students can build with StarLogoT. The first example is a model of a simple predator-prey ecosystem -- a popular model with high school students using StarLogoT. In a typical model, students model a predator (say a wolf) and a prey (say a sheep). They need to give rules to individual wolves and sheep so that they can move and interact. Many sets of rules are possible. A typical set of rules might assign an energy level to each wolf and sheep and decrease their energy when they move, increase their energy when they eat (wolves eating sheep). If their energy falls below 0, they would die. At every turn, they get a random number (roll an imaginary die) and if they are lucky they reproduce. (See Figure 1.)

A dynamic graph of the population levels of sheep and wolves can be viewed alongside the screen. If the rule sets are chosen appropriately, a typical result is that the population graphs look like out-of-phase sine waves -- sheep populations increase till the wolves have so much to eat that they increase, which reduces the population of sheep, which eventually, in turn, decreases the population of wolves, which results in an increase in the sheep population. (See Figure 2.)

This is a classical result, but seen here through the lens of emergent phenomena. The students control the behavior at the micro-level of the individuals and then observe the results at the macro-level of the populations. It is through experimenting with the dynamics of this connection that a powerful understanding of predator-prey dynamics can be achieved.

A second example is a model called Gas-in-a-Box, one of a suite of StarLogoT models in a package called GasLab. Gas-in-a-Box was originally created by a physics teacher, but the original model has been refined by dozens of students who have also created many variants and extensions of the original model.

The basic idea is a box containing thousands of gas molecules. Gas molecules are modeled as turtles that collide like elastic billiard balls, that is, they collide with the box and with other molecules without loss of energy. The user can set the mass and speed of any molecule. (See Figures 3 and 4.) The display color-codes the molecules, blue for slow, green for average speed, and red for fast.

In a typical first use, students initialize the molecules with equal masses and equal speeds but with random positions and headings. Thus all molecules start out green. When students run the model, they are usually surprised to see that the molecules turn color quite quickly and that many more of them turn blue than turn green. In other words, more of the particles slow down than speed up. Although this result is a direct consequence of a known law of gases, the Maxwell-Boltzmann distribution of molecular speeds, taught in high school physics, it is not recognized by students in this form. (In our experience, not just students, but even physicists are often surprised by this result.) Again, the key insight here is that the Gas-in-a-Box model allows students to see the gas from an emergent perspective. They come to see the connection between the micro-level of billiard ball collisions and the macro-level of the general characteristics of the gas as an ensemble. These two levels of description are typically taught separately in the high school curriculum. However, it is in understanding the connection between these two levels, how one emerges from the other, that leads to a powerful understanding of statistical thermal physics. The connection has been thought to be too hard for high school students, as it usually involves advanced mathematical machinery. But, through the use of multi-agent modeling languages such as StarLogoT, these ideas can be accessible to high school learners.

StarLogoT is in use by many students and teachers. In its years of use, we have assembled a large collection of "extensible" models (collectively entitled "Connected Models"). The sample models are drawn from a wide range of disciplines including physics, biology, mathematics, computer science, chemistry, materials science, ecology and economics. These sample models are created by students, teachers and researchers and go through a process of checkout and refinement before becoming a part of the distribution archive.

In the classroom, StarLogoT is typically used in roughly five phases:

A) In the first phase, the teacher typically leads the students in off-computer activities (known as participatory simulations or emergent activities) that provoke thinking about emergent phenomena. In these activities, students typically enact the role of individual elements of a system and then discuss amongst themselves what global patterns they detect and how those patterns could arise from their individual behaviors.
B) In the second phase, the teacher presents a "seed" model (a simple starting model) to the whole class, projected through an LCD panel so that everyone can view it. The teacher engages the class in discussion as to what is going on. Why are they observing that particular behavior? How would it be different if model parameters were changed? Is this a good model of the phenomenon it is meant to simulate?
C) In the third phase, students run the model (either singly or in small groups) on individual computers and explore the parameter space of the model.
D) In the fourth phase, each modeler (or group) proposes an extension to the model and implements that extension in the StarLogoT language. Modelers starting with GasLab, for example, might try to add to the model by building a pressure gauge, a piston, a gravity mechanism, or heating/cooling plates. The extended models are added to the project's library of extensible models and made available for others to work with as "seed" models.
E) In the final phase, students are asked to propose a phenomenon and build a model of it from scratch using the StarLogoT modeling primitives.

There are other simulation software packages that enable students to engage in phases B and C. However, because the students can't inspect or modify what happens inside these simulations, they can't engage in phases D and E and thus go more deeply into understanding the models. This is the problem with "black box" tools: they are easier to use at first, but provide fewer opportunities for learning. Other simulation packages, notably STELLA, are "glass box" like StarLogoT, but they ask students to model only at the level of populations. By enabling students to model at the level of individuals, Star-LogoT makes it easier for students to begin modeling because they start at the level of individual behavior. Hence they can base their models on their own experience -- both as individuals and of individual objects in the world.

We have worked with classrooms in all five of these phases. Generally, the depth of understanding of complex systems and emergent phenomena would be expected to increase as students start to more actively build, modify, and explore the models. The results that students can achieve with model extensions and designing their own models are often quite dramatic. Because of the great variations in available technology, learning time, and classroom organization, each phase has valuable applications.

Working in phase D, what we call the "extensible modeling" approach, allows learners to dive right into the model content. Learners typically start by exploring the model at the level of domain content. When they are puzzled by an outcome of the model, they design an extension to the basic model. This extension usually requires only a few language primitives to implement. This allows learners to follow a gently sloping path towards full StarLogoT language mastery -- skill with the general-purpose modeling language is acquired gradually as they seek to explain their experiments and extend the capabilities of the model.

Conclusion
The inclusion of a complex systems perspective in school curriculum has many benefits for learners:

We live in an increasingly interconnected world. Smokestacks in the Midwest cause acid rain in the East. Rainforest destruction in South America leads to greenhouse effects and weather pattern changes in Africa. Market collapses in the Far East can have great consequences on economies in the West. Traditional science which studies phenomena in isolation is not equipped to analyze and understand such systemic effects. Informed citizens in such a highly interacting world need tools that can help them cope with these complexities.
Though there is increased desire for interdisciplinary learning, students studying in a traditional curricular framework find it difficult to see the connections between different domains of knowledge. One strength of the complex systems theory perspective is that it enables us to see common patterns across traditionally separate fields: physical matter is the emergent result of molecular interactions; ecologies and biological niches are emergent results of interacting organisms; economies and markets are emergent results of the interactions of buyers and sellers.
Many everyday phenomena and experiences arise from the interactions of many different factors. Because these have been hard to study using traditional methods, they are excluded from the curriculum. Introducing complex systems allows students' personal experiences to be included in the curriculum - thus students see science as more personally relevant.
An understanding of patterns as emergent phenomena, rather than as results of equations, is both a more accurate picture of nature and easier for most people to understand. Science becomes more accessible as a result of this change in viewpoint.

By introducing a perspective of complexity and emergent phenomena, we make science more accurate, more inclusive and more accessible to the great majority of students.

Uri Wilensky is Director of the Center for Connected Learning and Computer-Based Modeling at Tufts University.
oas@ccl.tufts.edu

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