Nersessian’s articles reiterated the theme of The Mangle of
Practice, describing how modelers’ intentions changed as they interacted with
non-human agents (models they built and tested). C9 particularly represents the
dance of agency, as she made significant contributions to her field that were
tangential to her initial research question.
Nersessian’s research illustrates several examples of the
co-evolution of human intentions and non-human agents, describing the data,
computational, and collaborative constraints that modelers face and how
modelers manage these challenges. In “Coupling simulation and experiment,”
MacLeod and Nersessian show how one researcher manages these challenges using a
bimodal strategy, coupling modeling with experimentation to reduce
collaborative and data constraints. In “Building simulations” they follow an
alternative strategy: modeling while collaborating with experimentalists. They
explain that ISB draws on computation, engineering, and biology, and that
different individuals will be successful combining these practices in different
ways based on their experiences and ability to learn new practices.
This idea is important to apply to modeling complex systems
in K-12 instruction. Based on students’ age, experiences, and the teacher’s
background, modeling might be best implemented through mathematical or
agent-based models. Students may gain more from using reading, observation, or
experimentation to inform their models.
Recognizing that researchers have material constraints makes
me consider the constraints of time and materials in a classroom. While it
might be ideal to model invasive species from data collected by tracking their
growth, students might not have time to monitor these plants, or might not have
access to them. Teachers will need to plan deliberately to accommodate such
constraints.
Models working from mesoscopic views recognize that they do
not need to understand every interaction within a system, just the inputs and
outputs and important pathways within the systems. To implement modeling,
teachers and students will also need to be able to recognize what portions of
their system should be modeled and what can be simplified.
I think the cost of managing complexity is worthwhile based
on the “Building Cognition” article. Many of theories presented about
Distributed Cognition could be applied to a classroom setting. Chandrasekharan
and Nersessian describe how models serve as an “external imagination.”
Especially for younger students, having a tool that can help them visualize the
interaction of agents in complex systems could help them gain a deeper
understanding of these systems, rather than only focusing on agent behaviors or
aggregate outcomes. This “external imagination” could help them avoid “slippage”
between levels that Wilensky describes by providing them with space to organize
their ideas and could help them breakdown systems in ways that experimentation
could not (page 35).
Models allow researchers to collaborate effectively and
encourage collaboration since researchers depend on others’ data. I wonder if model
would help support ELL students, low readers, or young children who do not have
the vocabulary to describe complex systems, but do have the sensorimotor skills
to manipulate and develop models.
In a classroom, would teachers or students manage complexity?
How? Can children benefit from distributed cognition as researchers do?
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