Epistemology & Learning Group
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There is a growing interest in role-playing activities, both in
school classrooms and in the culture at large. Despite
this growing interest, role-playing activities are rare in mathematics
and science classrooms. In social-studies activities, a major
goal is to help students adopt the perspective of another person.
But mathematics and science classes typically discourage this
type of perspective-taking; science is usually taught as a process
of detached observation and analysis of phenomena, not active
participation within phenomena. In this paper, we argue that role-playing
activities can play a powerful role in mathematics and science
education-particularly in the study of the new "sciences
of complexity." We present detailed descriptions and analyses
of two role-playing activities that we have organized. Each activity
is designed to help students explore (in a very participatory
way) the behaviors of complex systems, helping them develop better
intuitions on how complex phenomena can arise from simple interactions,
and predictable patterns from random events.
There is a growing interest in role-playing activities, both in
school classrooms and in the culture at large. In social-studies
classrooms, it has become increasingly common for students to
learn historical or economic ideas by playing out roles-for example,
acting as a member of the Communist Party in the Weimar Republic,
or acting as a candy manufacturer in a mock economy. Meanwhile,
fantasy role-playing games (in the spirit of Dungeons and Dragons)
have swept through the culture and are now becoming a major activity
on the Internet in the form of so-called MUDs (Curtis, 1993; Bruckman
& Resnick, 1995; Bruckman, 1997).
Despite this growing interest, role-playing activities are rare in mathematics and science classrooms. In social-studies activities, a major goal is to help students adopt the perspective of another person. But mathematics and science classes typically discourage this type of perspective-taking; science is usually taught as a process of detached observation and analysis of phenomena, not active participation within phenomena.
In this paper, we argue that role-playing activities can play a powerful role in mathematics and science education-particularly in the study of the new "sciences of complexity." We present detailed descriptions and analyses of two role-playing activities that we have organized. Each activity is designed to help students explore (in a very participatory way) the behaviors of complex systems, helping them develop better intuitions on how complex phenomena can arise from simple interactions, and predictable patterns from random events.
Through our analyses of these activities, we aim to:
Historically, developmental psychologists
have placed analytic, reflective modes of thinking on a pedestal,
viewing it as the most advanced form of thought. The classical
reading of Piaget (Ginsburg & Opper, 1969), for example, describes
cognitive development as a one-way progression from concrete to
formal or abstract ways of thinking. This view of cognitive development
fits neatly with traditional conceptions of scientific thinking
and science education. Science educators have traditionally emphasized
the importance of detached observation and analysis. And with
some good reason: a detached stance can help protect against students
projecting their own biases in their scientific observations and
During the past decade or two, there has been
a serious challenge to these views of cognition and of science.
A growing number of researchers (e.g., Gilligan, 1987; Lave &
Wenger, 1991) have argued that learning and knowing can not be
divorced from situated experience. Some researchers have called
for a "revaluation of the concrete" (Turkle & Papert,
1991; Wilensky, 1991). In this new view, the detached stance encouraged
by traditional science education tends to limit students' engagement
with scientific ideas, making it difficult for them to build on
their experiences and make strong personal connections to mathematics
and science. There is a new appreciation for active participation,
and not just distanced reflection, in the learning process. Ackermann
(1991) describes cognitive growth as "a dance between diving-in
Role-playing activities provide a very effective
way for students to "dive in." What might it mean to
bring role-playing to mathematics and science classrooms? The
activity of "playing turtle" in Logo provides an example
(Papert, 1980). In using the Logo programming language, children
give commands to a graphic turtle on the computer screen in order
to create and explore mathematical patterns. As part of this activity,
children are often encouraged to see themselves as the turtle,
to better imagine what the turtle should do. To figure out how
to make the screen turtle draw a circle, for instance, children
will often try to walk a circle themselves, noticing that a repeated
pattern of "step a little, turn a little" will produce
a roughly circular shape. In this way, students make use of their
knowledge of their own bodies to make a normally "abstract"
mathematical domain more "concrete." Papert describes
it as a process of "syntonic learning"-leveraging familiar
knowledge about your own body to gain a deeper understanding of
other knowledge domains.
We believe that these types of role-playing
activities can play a particularly powerful role in helping students
learn about complex systems. During
the past decade, there has been a ground swell of scientific interest
in the so-called "sciences of complexity"-the investigation
of how complex phenomena can arise from simple components and
simple interactions. New research projects on chaos, self-organization,
adaptive systems, nonlinear dynamics, and artificial life are
all part of this growing interest in complex systems. The interest
has spread from the scientific community to popular culture, with
the publication of general-interest books about research into
complex systems (e.g., Gleick, 1987; Waldrop, 1992; Gell-Mann,
1994; Kelly, 1994; Roetzheim, 1994; Holland,
1995; Kauffman, 1995).
Research into complex systems touches on some of the deepest issues
in science and philosophy-order vs. chaos, randomness vs. determinacy,
analysis vs. synthesis. At the same time, this new field has introduced
new objects of study-objects (such as fractals) that were barely
conceivable before the study of complex systems and barely renderable
without computational media, yet strongly connected to many patterns
and phenomena in the everyday world.
In the minds of many, the study of complexity is not just a new
science, but a new way of thinking about all science, a fundamental
shift from the paradigms that have dominated scientific thinking
for the past 300 years. In particular, two dominant paradigms
of scientific thinking are being challenged. Whereas scientists
have long seen the world in terms of centralized control and centralized
causes, the new sciences of complexity tend to highlight decentralized
systems. And whereas scientists have long seen the world as a
deterministic clock-like mechanism, the new sciences offer probabilistic
explanations of phenomena.
The Deterministic, Centralized Mindset
The traditionally dominant paradigms have affected not only the
way scientists and mathematicians go about
their work, but also the way people in their everyday activities
think about the world. Most people have what we call "centralized
mindsets" (Resnick, 1994a, 1996) and "deterministic
mindsets" (Wilensky, 1993, 1995). These mindsets deeply
influence the ways in which people make sense of themselves and
their world. As a result, many people have
great difficulty understanding phenomena such as the population
variations within an ecosystem, the statistical patterns arising
from molecular collisions, the emergence of traffic jams on a
highway, or the fluctuations and swings of a national economy
Why have new ways of thinking (associated
with the new sciences of complexity) not become more widespread?
One major factor is the lack of tools and activities to help people
explore decentralized and probabilistic ways of viewing the world.
In an effort to remedy this deficiency, some educational
researchers (including ourselves) have recently developed computer
modeling environments to help students explore such phenomena.
For example, StarLogo2 is a programmable
modeling environment designed
explicitly to help people explore systems-oriented phenomena-and
develop new ways of thinking about such phenomena (Resnick, 1994a,
1996; Wilensky, 1995, 1996).
Here, we focus not on new tools but on a new category of activities-participatory
role-playing activities. Our belief is that role-playing activities
can support learners in moving beyond deterministic and centralized
ways of thinking. The goal is to help students build intuitions
about systems and complexity, just as playing turtle helps them
build intuitions about geometry.
Since 1993, we have organized role-playing
activities in a variety of contexts, such as conferences and university
classes (e.g., Resnick & Wilensky, 1993, 1995). The goal of
these role-playing sessions is to provoke participants to reflect
on their conceptions of complex phenomena and to help them develop
probabilistic, decentralized ways of thinking. Since these activities
grew out of our research with the StarLogo modeling environment,
we refer to them (humorously) as StarPeople activities.
Our first StarPeople activity was very simple.
We ask a group of people (we have tried it with anywhere from
20 to 1000 people) to start clapping-and to try to coordinate
their clapping into a single, unified rhythmic beat. There is
no leader or "conductor": the people must coordinate
their claps simply by listening to one another. The surprising
part of the experience is how quickly the group can synchronize
its clapping. Even with 1000 people, the clapping becomes synchronized
within a few seconds, after just a few cycles of clapping. The
activity is a simple demonstration of decentralized control, illustrating
how patterns can be coordinated without a coordinator, organized
without an organizer.
Overall, our role-playing activities fall
into two broad categories. In some activities (such as the clapping
example), we provide a goal for the overall group and each individual
must decide how to act to help the group achieve the goal. In
other activities, participants are given precise rules to follow,
then asked to reflect upon the group-wide patterns that
emerge. In all cases, the goal is to engage participants in thinking
about the relationships between individual and group behaviors,
helping them develop a better sense of how large-scale patterns
arise from simple, local interactions.
Making the Rules
This collection of activities is intended to be enacted in sequence.
In each activity, the goal is the same, but participants use different
forms of communication to achieve it. To start, each participant
is given a small Post-it note and a pen.
Activity 1. We tell each participant to pick a random
integer between 1 and 6 (inclusive), and write it on their Post-it
note. Then we tell them to organize themselves into like-numbered
groups: all of the 1's together, all of the 2's together, and
so on. We do not tell them how to get into groups. In all
cases that we have observed, some people "naturally"
emerge as leaders, shouting out things like "all 3's over
here." In a short time, six groups form.
Although this activity is intended primarily as a simple, introductory
activity, to serve as a foil for later activities, some interesting
probability issues can arise. Often, the groups are very uneven in size,
and some participants are surprised at the uneven distribution.
We have discussions about the reasons for the uneven distribution.
Activity 2. Each participant writes a new random
number on their Post-it. Again, we ask the participants to organize
themselves into groups-but this time they aren't allowed to talk.
The only way participants are allowed to communicate is by showing
their Post-its to one another. They aren't allowed to communicate
in any other way: no raising fingers, no stomping feet. Given
this restriction, groups form more slowly than in the previous
activity. Since it is difficult for individuals to become "leaders,"
participants need to develop new strategies.
When participants find others with the same group-number, they
tend to stay together. Some groups roam together, looking for
others with the same group-number. Other groups stay in one place,
expecting that "loners" will wander around and find
them. Sometimes, a group will send out an "emmisary"
to look for "lost" group members. But the restriction
on talking makes it difficult for group members to coordinate
this type of group strategy.
In some cases, this process does not converge on six groups, even
after an extended period of time. For example, sometimes two same-numbered
subgroups will each wait in place, expecting that "loners"
will find them. This "bug" is an example of getting
stuck on a local maximum. Some ways to fix this bug: a group can
send out an emmisary, or the whole group can continue to wander
as a unit.
The communication patterns are very different than in the first
activity. In the first activity, participants could have a global
effect by shouting out their numbers. They could also sense globally,
by looking around and listening to others. In the second activity,
with talking not allowed, each individual can affect only nearby
individuals (that is, they have only a "local" effect).
Participants continue to have some global "sensing"
capability, since they can look around and see other groups. But
their access to Post-it numbers is local: they can not "sense"
numbers at a distance.
In our discussions with participants, we help draw analogies to
the natural world. Many natural-world phenomena are based on local
interactions. For example, ants communicate not by "shouting"
to one another but by sniffing for chemical pheromones in their
By restricting talking, we force participants to adopt
strategies based on local interactions-in effect, to play the
role of individual ants. Although local strategies are common
in the natural world, people have weak intuitions about such strategies.
By reflecting on their experiences as "ants," and observing
the large-scale patterns that arise from their local interactions,
participants begin to make connections that help them develop
better intuitions about local
The process of "diving
in" to their new role enables them to develop insights that
would be difficult to attain by distanced analysis.
The activity has also stimulated discussion of how stable patterns can emerge from "random" processes. Many of the particular choices of individual participants seem random-such as whether to turn left or right at any particular time. Nevertheless, the overall patterns that form are quite predictable5.
Some participants have remarked that this second activity feels
much more "alive" than the first activity. Why does
it feel more alive? One person explictly pointed to the combination
of "some randomness and some order." Whereas the first
activity felt mechanical, with people moving directly to their
goals, the second activity felt more organic, with people continually
readjusting based on feedback from the surrounding "environment"
Activity 3. The goal is the same as in the previous
activities: to form like-numbered groups. But this time, we give
participants blindfolds, so they can not see one another. Participants
can communicate with whispers, but not loud talking. We tell people
that they do not have to participate if they feel uncomfortable
being blindfolded. Some people (without blindfolds) act as monitors,
making sure that participants do not wander into dangerous situations.
With these new restrictions, it takes much longer for groups to
form. When people find others with the same number, they are very
reluctant to ever split apart. They often hold hands or link arms,
to make sure not to lose connection with their "family."
Each proto-group tends to snake around the room, looking for other
There is often much laughter during this activity. The laughter
seems to reflect a combination of nervous anxiety and excitement.
Many people are uncomfortable having their sight cut off in this
way, and they aren't quite sure what strategies to follow. But
there is also an excitement of being confronted with a novel situation
and using different sensory modalities to interact with one another.
Sometimes, by random variation, only one person will have a particular
number. This activity proves to be particularly difficult for
such "singletons." They continue to wander around, not
being sure if they will ever find any other group members.
In this activity, interactions are even more "local"
than before. In Activity 2 ("look, but don't talk"),
participants can use their vision to get a global sense of how
and where people are clustering (even if direct communication
about group-numbers was local). In Activity 3, with blindfolds,
participants get no global information. All of their information
comes from people standing very close to them.
With communication restricted, people tend to stay closer to one
another. They use sound cues, and even warmth cues, to figure
out where other people are. The resulting pattern is more concentrated
than in the previous activities. It is not uncommon to see the
groups standing right next to one another, distinguished only
by the fact that group members are physically linked.
Often, the process does not converge to six groups. As in the
previous activity, two like-numbered groups might stop looking
for more members, and thus never find one another. But, even with
the blindfolds, most of the groups do manage to get together.
People tend to develop creative strategies for finding one another.
For example, the members of a group might stay linked together
but stretch out into a line, so that they can "sweep"
a larger area, hoping to find additional members-using strategies
reminiscent of those used by some micro-organisms searching for
food. Sometimes, this behavior emerges naturally. A person at
one end of the group might start moving in one direction, while
a person at the other end starts moving in the opposite direction.
As people in the group feel themselves "stretched,"
they recognize the possibilities of a new strategy.
Sometimes, people push the limits of the rules-for example, whispering
in unison so the the group can be heard at a greater distance.
In some cases, these strategies might be planned by a few members
of the group and "illegally" communicated to the others.
In other cases, the strategies emerge: if several whispers happen
to synchronize by chance, the group members might realize that
it would be useful to keep all of their whispers synchronized-and
they do so using strategies similar to those used in the synchronized
Summary. What's the point of these activities? The
activities are designed to engage participants in thinking about
different types of strategies. Their initial strategies (in Activity
1) are very centralized: one person takes the lead to organize
the others. Their later strategies are more decentralized: each
person follows simple rules, without any centralized control.
The appropriate choice of strategy often depends on what means
of communication are available. With global communication, participants
choose centralized strategies; as communications are restricted
to local interactions, participants are forced to develop more
These activities provide a context for thinking about important
biological ideas. As mentioned earlier, the activities have an
"organic feel" to many participants. The patterns and
dynamics that arise in the activities evoke images from the natural
world; in describing the activities, participants frequently use
analogies to biological creatures (such as ants in a colony).
Moreover, the activities provide a framework in which participants
can think about the rules and mechanisms underlying the creature-like
behaviors. In particular, the activities provoke participants
to consider how creature strategies might be constrained (and,
in an evolutionary sense, selected) based on the communications
capabilities available to the creatures.
The activities also engage participants in thinking about the
role of randomness and chance in natural phenomena. The biological
analogies provide a meaningful context for understanding the value
of a probabilistic approach for thinking about the behavior of
all types of systems. Rather than the traditional
classroom approach of learning probability through manipulating
formulae to calculate the outcome of flipping coins or rolling
dice, the StarPeople activities engage learners in experiencing
probabilistic behavior within a "messy" system of interactions.
By combining deterministic and probabilistic components in their
strategies, participants can assess the relative effectives of
these different approaches.
We have sometimes used these activities as an introduction to design projects6. In several cases, we gave participants the challenge to design behaviors for a colony of "robot termites." The goal: make the termites gather randomly-scattered wood chips into piles. Participants have proposed many types of strategies. Some of their strategies were deterministic and centralized. For example, one termite could direct all other termites to bring the wood chips to a particular location, or each termite could be responsible for gathering wood chips in a particular region. Other strategies were decentralized and probabilistic. One particularly elegant approach: each termite wanders randomly until it bumps into a wood chip, then it picks up the chip and wanders randomly again until it bumps into another wood chip, upon which it puts down the one it was carrying7 .
The role-playing activities clearly influenced the ways participants
thought about their design projects. In particular, the role-playing
highlighted the link between creature strategies and communications
capabilities. As participants developed strategies for the termites,
they considered what types of communication mechanisms were most
appropriate. For centralized and deterministic strategies, they
needed complex and "expensive" global communications
mechanisms (such as "global positioning systems" for
each termite); for decentralized and probabilistic strategies,
they could use simpler, local communications mechanisms (such
as dropping "bread crumbs" for others to follow).
Following the Rules
There are two fundamentally different ways of exploring the behaviors
of systems. In one approach, sometimes called "phenomena-based"
(or "backwards") modeling (Wilensky, 1995; 1996), you
design strategies for individual parts of a system in an effort
to achieve a particular goal for the overall system. In an alternative
approach, sometimes called "exploratory" (or "forwards")
modeling, you start with rules for the individual parts of a system,
and you observe the group-wide patterns that arise from the interactions.
The same two approaches can apply to role-playing activities.
In the previous section, we described a set of "backwards"
role-playing activities. In this section, we describe some "forwards"
role-playing activities, in which participants are told to follow
a pre-determined set of rules, and asked to think about the patterns
that arise from the interactions.
In one activity, we start by giving hats to half of the people
in the room. Then we ask everyone to randomly choose a number
between 1 and 10, and we arrange the people into ten groups of
like-numbered people. Since the distribution of people with hats
is random with respect to the numbering, each group typically
includes some people with hats and some people without hats.
We tell people that each of them will play the role of a physics
particle. The ones without hats are
particles; the ones with hats are particles
(pronounced alpha-hat: get it?).
We tell them that the particles follow these rules: If a group
is dominated by one type of particle (composing at least 2/3 of
the group), those particles "expel" the less-common
particles. For example, if a group has five
particles and only two particles, then the
particles will expel the particles, and the
particles will go
to a neighboring group (see figure 1). But if a group has roughly
even numbers of and particles, none
of the particles are expelled-the group is
We tell the participants to follow these rules repeatedly, until
no more particles are expelled. Usually, there are several rounds
of activity before the system stabilizes.
Many participants are surprised by the distribution of
particles and particles after the system stabilizes. (You might
want to think about the pattern of particles before reading on.)
It turns out that most of the groups end up with only one type
of particle, either particles or
particles. That is surprising since most of the groups start out
stable, with roughly even numbers of
The figure shows three snapshots, each with three groups. Reading
down shows the evolution of the three groups over time. Arrows
indicate the movement of "particles" at a particular
After acting out the simulation, participants try to make sense
of the surprising results. It turns out that most participants
haven't considered the possibility of a "ripple effect"-the
fact that particles expelled from one group can destabilize the
next group. Imagine a group with five
particles and two particles,
the two particles will be expelled
to the next group. That group might have been stable, with five
particles and three particles. But
with the two new particles, the balance
changes to seven particles and three particles-so the three
particles are expelled (figure 2).
But those three particles might destabilize
the next group, and so on.
The failure to take into account the ripple
effect is one example of a general tendency people have in trying
to make sense of complex systems. They tend to focus on short-term
effects at the expense of understanding the larger-scale systemic
effects. Similar effects have been studied in detail by systems-dynamics
researchers (e.g., Forrester, 1968). In a simulation game set
up at MIT (Senge, 1990), participants took on one of three roles:
beer consumer, small retail store owner, and beer manufacturer.
In the game, many retailers got into deep trouble by not taking
into account the ripple effect. They would order many cases of
beer in response to pent up consumer demand, forgetting about
the lag time it would take the manufacturers to gear up for the
new orders. By the time all the orders came in, they would swamp
the retail stores creating huge surpluses that would often bankrupt
the stores. These ripples would in turn affect the manufacturers
who had ramped up for greater production only to find that demand
had suddenly vanished. Failing to take into account ripple effects
is one important "bug" in people's thinking about complex
After people have acted out and discussed the simulation with
and particles, we suggest a shift
in interpretation. Imagine that the participants represent not
particles in a physics problem but people at a cocktail party.
The particles represent men and the
particles represent women. Men and women like to be together in
a group. But if a group has an abundance of one gender, people
of the other gender feel somewhat uneasy and move to a neighboring
group. People aren't "expelled," they leave voluntarily,
but the effect is the same. In this case, most of the groups at
the "cocktail party" end up single-gendered-that is,
most of the groups are either all men or all women
8. In fact, some
researchers have proposed that this very mechanism can explain
the distribution of genders in groups at real cocktail parties
Some participants find this explanation enlightening, providing
a mechanism to explain previously mysterious phenomena. But others
find it disturbing. They argue that human behavior can not be
described by a few simple rules. Indeed, many people seem to take
it as a personal affront that someone might try to describe human
behavior-or, more pointedly, their behavior-in terms of
a few simple rules. As one person put it: "People are not
Why do people have this strong reaction? We see several possible
explanations. First, the idea that behavior is based on "following
rules" conflicts with people's sense of their own free will.
They feel as if they are in control of their actions, not following
a set of fixed rules. Second, people are bothered by the omission
of any emotional or spiritual aspects in the description of the
behavior. They reject the idea of a "purely rational"
set of behavior rules. Third, the notion of a collection of rules
running independently conflicts with people's sense of a whole
and integrated self. Fourth, some participants argue that people
are able to modify their behavior over time, especially if they
are unhappy with the results of previous behaviors. So they react
against an activity which (as they see it) is based on fixed and
immutable behaviors. They assert that people must assume responsibility
for their behaviors; if all behaviors were fixed and pre-determined,
there would be no sense of moral culpability. Finally, some people
who accept that behaviors might be explained by simple rules react
against the idea (implicit in the cocktail-party example) that
everyone must follow the same rules.
These reactions are understandable. It is certainly true that
a few simple rules are not sufficient to fully explain human behaviors-even
in a "simple" situation like a cocktail party. But is
there something to be gained by modeling human situations in this
way? We think so. The point of "modeling" is not necessarily
to fully characterize phenomena in the real-world, but to highlight
key aspects of those phenomena.
In our view, some aspects of human behavior can be modeled
with simple rules9.
These aspects of behavior are often ignored
or repressed, since they lead to conflicts with our usual ways
of viewing ourselves (and our selves). Our role-playing activities
are designed to provoke people to re-examine the ways in which
rules underlie behaviors. Such re-examination can help people
avoid or remedy situations in which underlying rules lead to unexpected
and undesired effects. In the cocktail-party case, everyone would
be happier if the groups had both men and women. But the underlying
rules lead to a dynamic that causes segregated groups. Once the
groups segregate, a participant might assume that the others actually
prefer single-gender groups-and thus would be unlikely to take
action to change the situation. By recognizing the possibility
that all participants might prefer mixed-gender groups (even though
their actions lead to a different result), an individual might
feel empowered to suggest changes that would make everyone more
satisfied. Paradoxically, it is by ignoring or resisting these
types of rules that we become slaves to the rules; it is only
by recognizing (and thus becoming able to challenge) the rules
that we can take control of the rules for our own purposes.
The case of the cocktail party might seem trivial. But some researchers
have argued that similar dynamics are at work in urban housing
patterns. Even if people of all races and ethnic backgrounds were
content living in integrated neighborhoods, communities might
end up segregated by the same mechanism as in the cocktail party.
Of course, that does not mean that segregated neighborhoods
always arise from such benign factors; in many cases, blatant
racism is undoubtedly at work. But by understanding the multiple
factors that might give rise to urban segregation, we are in a
better position to rethink and try to remedy the situation.
The StarPeople activities emerged out of our previous work on
helping students explore complex systems through computer modeling
activities. A common critique of computer modeling activities
in educational settings is that the activities are "disconnected
from the real world." Just because it is possible to model
a particular phenomenon on the computer doesn't mean that it has
any relationship to real-world phenomena-for example, it might
be based on a totally different underlying mechanism. Computer
models that are not "grounded" in some real-world connections
can feel like Nintendo games: lots of interactions, but no connections
to deep ideas.
In the activities discussed in this paper, we focus on a different
type of "connection" for grounding
computer modeling. Our emphasis is not so much on connections
to real-world phenomena, but connections to personal experience.
By participating in role-playing activities, learners can "dive"
into mathematical and scientific phenomena, developing new relationships
with the knowledge underlying the phenomena. Learners can build
upon their sensory and social experiences to help understand (and
become engaged with) a variety of scientific phenomena. The importance
of forming personal connections to scientific knowledge is all
too often ignored in today's classrooms.
The fields of complexity and systems sciences are particularly
well-suited for role-playing explorations. Complex systems typically
involve a large number of objects interacting with one another
in parallel-so there is a natural match for a group of learners
interacting with one another in a role-play. More importantly,
role playing provides a natural path for helping learners develop
an understanding of the causal mechanisms at work in complex systems.
By acting out the role of an individual within a system (for example,
an ant within a colony or a molecule within a gas), participants
can gain an appreciation for the "perspective" of the
individual while also gaining insights into how interactions among
individuals give rise to larger patterns of behavior.
These role-playing activities can serve as a good entry point
for computer modeling activities (in particular, object-based
parallel modeling as in StarLogo), in which learners must explicitly
articulate rules for the individual objects to follow. But we
do not see role-playing as merely a prelude to the computer; role-playing
can provide very different sorts of experiences. For example,
our StarPeople activities can provide a middle-ground between
the strict rules that govern most computer-modeling activities
and the very loose structures in most social-studies role-playing
activities. In the "making the rules" activities, for
example, participants have freedom within specific constraints.
By choosing strategies within the constraints of limited communications
capabilities (local whispering, blindfolds, etc.), StarPeople
participants have more freedom than is typical in a computer-modeling
activity, but are still able to get reliable feedback from the
system, enabling them to try and test out their theories.
Role-playing is already used in some science classroom activities-for
example, some students learn about the solar system by acting
out the motions of the planets. But this is a very different sort
of role playing than in our StarPeople activities. In playing
the planets in the solar system (or, in another example, the electrons
and protons in an atom), students are creating a type of visualization
of a scientific phenomenon. This activity might be helpful in
understanding the relative motions or positions of the planets,
but it provides students with little insight into the causal mechanisms
underlying the planets' behavior. It focuses more on the results,
not the processes and interactions that give rise to the results.
In StarPeople activities, our goal was to engage participants
in thinking about the dynamics and process of pattern formation
in complex systems, not just the patterns themselves.
"Complexity" does not refer to a restricted set of phenomena
but instead provides a new and powerful lens for seeing many scientific
and mathematical concepts. Perhaps most important from an educational
perspective, complexity and systems sciences have the potential
to make certain scientific and mathematical ideas more accessible
to a broader audience. Models in complexity and systems sciences
are often based on interactions among concrete objects-so that
learners can make analogies by recruiting intuitions based on
their own experience as "objects" interacting with other
objects in the world.
Some people might argue that role-playing activities are not appropriate
for exploring scientific phenomena. Classic role-playing activities
involve people impersonating other people-trying to mimic the
goals, intentions, and actions of historical characters or economic
actors. But in most scientific investigations, the objects under
consideration do not have human-like goals or intentions. Thus,
it might seem that human "impersonations" would not
provide any leverage for understanding these types of objects
or their interactions. Some might see a danger that scientific
role playing would confuse social and scientific ways of thinking
This view, however, represents a deep (though widespread) misunderstanding
of the true nature of scientific learning. It assumes that the
deep differences between humans and the objects of scientific
investigation (be they ants or molecules) make human experience
irrelevant to understanding scientific phenomena. But in fact,
practicing scientists often make use of their own personal experiences
in making sense of the natural world. Some scientists go a step
further and actually adopt the perspective of the objects they
are studying. Barbara McClintock, a Nobel-winning biologist who
studied the genetics of corn, attributed her greatest discoveries
to the fact that she had a "feeling for the organism"
and was able to imagine herself as one of the genes within the
corn (Keller, 1983).
The challenge, then, is not to root out all personal experiences
when trying to understand a scientific phenomenon, but rather
to figure out which aspects of one's personal experience are useful-and
which are not-in understanding a particular
situation. When trying
to imagine themselves as ants in a colony, for example, learners
can make use of some of their own perceptual experiences and intuitions,
but they need to be careful not to assume that ants have the same
types of "goals" that they as humans do. We have found
that our role-playing activities provide a framework in which
learners can start to make precisely these types of distinctions-learning
to project only the specific parts of their own experiences that
are useful for understanding other creatures and objects.
When learners try to understand the workings of human systems,
the problem is, in some ways, the reverse. In the cocktail-party
example discussed earlier, some participants strongly resisted
the idea that human behavior at a cocktail party can be understood
in terms of a few simple rules. But just as some human-like behaviors
can be very useful in understanding creatures and inanimate objects,
so too can simple creature-like behaviors be useful in understanding
some human behaviors.
We have barely scratched the surface with our StarPeople activities.
We imagine extending this research along several different dimensions-new
types of technological support, new domains for role-playing,
new empirical studies.
Our current StarPeople activities use very simple "technologies"-Post-it
notes, pens, blindfolds. We believe that new technologies could
help overcome some of the limitations of the current StarPeople
activities. For example, participants in StarPeople activities
could wear "Thinking Tags" (Borovoy et al., 1996)-wearable
electronic tags that communicate with one another via infrared
signals. These tags (which include a tiny micro-controller and
memory) could allow new forms of enhanced (or restricted) communication
among StarPeople participants-and, as a result, further explorations
of how modes of communication influence strategies for organization.
The tags would also allow longer-term activities, in which the
tags gather and store data about interactions over extended periods
of time. For example, we imagine a group of students participating
in an extended simulation of the spread of an infectious disease,
in which "viruses" jump from one person's tag to another.
Another limitation with current StarPeople activities is the difficulty
of gathering sufficient numbers of
participants. One solution
is to allow people to participate over the Internet. Each person
could control the behavior of a proxy object in a shared virtual
space, and then observe the group-wide patterns that arise from
the interactions. This type of activity could build on existing
MUD environments (or architectures), but would have a greater
emphasis on interactions of collections of computational objects (including representations of self) in structured
activities. Of course, this type of activity would remove the
physical dimension of StarPeople interactions, but it would allow
many more people to participate in StarPeople-like activities
while also allowing explorations of new modalities of interaction.
In particular, this NetStarPeople approach would allow human-controlled
objects to interact alongside programmed objects, thus facilitating
greater maneuverability in the middle ground between rigid rules
At the same time, there is a need for more in-depth empirical
study of how and what people learn as they participate in StarPeople
activities-in particular, a finer-grained micro-analysis of participants'
learning paths as they participate in-and engage in thinking about-complex
systems. There has been very little research in the developmental
and cognitive psychology communities on how people make sense
of complexity. We believe that StarPeople-based studies could
make important contributions to this emerging field of research,
helping both in the development of new educational approach for
learning about complexity and richer theoretical accounts of how
people build new understandings of the workings of complex systems.
The preparation of this paper was supported by the National Science Foundation (Grants RED-9552950, RED-9358519, REC-9632612), The ideas expressed here do not necessarily reflect the positions of the supporting agency. We would like to thank Seymour Papert for his overall support and inspiration and for his constructive criticism of this research in its early stages. We'd also like to thank members of the Epistemology and Learning group of the MIT Media Lab for helpful feedback on this research. Finally, we thank David Williamson Shaffer and the reviewers(Jeremy Roschelle and Roland Hubscher) for their suggestions and comments.
1 Models of these phenomena (e.g., predation, gaslab, and traffic-basic) can be viewed and downloaded at /cm/models.
2 Several versions of StarLogo are now available. An MIT version can be downloaded from http://www.media.mit.edu/starlogo/ An enhanced version, based on the MIT version, is called StarLogoT, and can be downloaded from the CCL site.
3 For a StarLogoT model of this, see /cm/models/ants.
4 One source of difficulties people have with mathematics is that mathematics is often taught by giving the global rules, but no mention is made of the local interactions that give rise to the global patterns. Statistics, a notoriously difficult subject for many, is usually taught so that the central concept of probability distribution is introduced through the global parameters (mean, standard deviation, skew, moments, ...) of various distributions, but little connection is made between these parameters and the local rules that lead to these effects (see Wilensky, 1995; 1997).
5 This effect of order emerging from randomness has a paradoxical feel to many participants. Randomness is conceived of as the antithesis of order. Through these activities they come to see that algorithms that incorporate random components can often be a much more efficient and robust path to order than deterministic algorithms (see Martin, 1996; Papert, 1996; Resnick & Wilensky, 1993; Wilensky, 1993).
6 Often we have embedded these design challenges in stories with local color. In a workshop we gave at the Artifical Life III conference in Santa Fe, New Mexico, we gave the following version of the challenge:
A terrible windstorm swept through the Painted Desert. Blue, green, yellow, and white grains of sand were all mixed together. The ants in the desert don�t like their new high-entropy environment. They decide to move the sand to form a more interesting pattern.
Each ant can perform four simple actions: it can take a step forward, it can turn left by an arbitrary angle, it can pick up a grain of sand (if there is a grain directly underneath it and it isn�t already carrying a grain), and it can put down a grain of sand if it is carrying one). Each ant also has several senses: it can detect whether there is a grain of sand directly under it (and if so what color) and whether there is a grain directly ahead of it (and if so what color). An ant also knows whether it is carrying a grain at the current time.
What rules should the ants follow to move the sand into a more interesting pattern? Try to create some of the following patterns:
- Move grains of the same color near one another. (one solution can be found at /cm/painteddesert/.
- Move the grains into concentric circles, according to color.
- Move the grains into four horizontal strips, according to color.
- Create your own abstract art.
- Draw a picture of a famous person.
7 A StarLogoT model of this approach can be found at: ccl.northwestern.edu/cm/models/termites/.
8 A StarLogoT model of this party can be downloaded from: /cm/models/party.
9 We have also found that many aspects of human behavior can be productively modeled with "homogeneous" systems (in which all individuals follow the same rules). Of course, people are very heterogeneous (with many different approaches, preferences, and strategies). But, surprisingly, this heterogeneity of behavior can sometimes be modeled through simpler, homogeneous rules. Smith, diSessa & Roschelle (1994) describe a number of interesting examples of how learners adapt their "incorrect" methods derived from early experience to more complex situations.
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