2/16 Steve: Managing the mangle

1.       Article

a.       Science is not scientists observing the world from a place of isolation.  Science is about the interaction between people and the environment.  Science happens when humans have goals and they use machines and tools to try to achieve those goals. Humans make their moves by playing certain tools and setting up certain scenarios, and nature makes its move by reacting to the scenario according to the rules that govern its behavior. Science classrooms should reflect this messy interplay between humans and nature both by providing examples of past interactions and by teaching students how to manage their own interactions.

b.      The practical implications of this idea are many. I think the most important is to impress on students that the real world doesn’t function by equations.  It functions by tons of individuals with agency acting out their roles, and the job of science is to figure out what roles different individuals play and why.  Science shouldn’t just be what we know it should include how we got that information and what is still left unexplained. 

c.       The idea of mangle that Pickering discusses is closely related to what we have discussed about the ever-changing nature of our models.  When we model we must model and then test, model and then test, never stopping but always evaluating our model against the truth.  In science, Pickering claims, the same thing happens.  We design an experiment, then nature shows us what it does in response.  Then we use that information to redesign the experiment until we have pulled the information we want out of nature.  But it is a constantly evolving process where the path is not always direct and we are often surprised by what nature does.

d.      Pickering’s article seems to relate mostly to Nercessian’s.  Pickering mentions how his second through 5^th chapters will explore examples of the interplay between human agency and machine/nature agency, which is basically the story of Nercessian’s lab group.  They constantly made models, watched what happened, and remade their models, and then poked the neurons in new ways to see if their model (which represents human agency) accurately predicted what the neurons did (nature’s agency).

e.       The Pickering chapter related to my own modeling experience by elucidating the messiness of science and how difficult it really is to know what nature will do.  Even though our wolf-sheep models are not exactly nature, they are nature in a way in that there is some randomness involved and they are in the end the result of physical processes like electrons traveling through circuits.  I found that changing the variables of the model resulted in outcomes that I would never have predicted.  It was very difficult to get what I wanted out of the model just by thinking about where I should put the sliders.  This is the mangle of science incarnate; I had to just set up a situation, watch what happened, and adjust accordingly.  It was not a textbook science situation where I added two chemicals together and they had some predictable reaction.  It was complex and richly relational.

2.      Questions

a.       Can the agency model be used to predict other less sciency things like how many people decide to vote, how many Lyft/Uber drivers there will be in a city etc?

b.      What are some of the coolest remaining mysteries of science that are accessible to high school students that we can use to show how messy and mangled science research is?

3.      Modeling ideas: how people decide whether or not to vote based on which way people around them are going to vote, the acceleration of a bungee jumper

4.      How Pickering Chapter 1 relates to what you might do with Netlogo in your classes. How does it not? Netlogo relates to Pickering because it shows how messy science is. Because there is some randomness built into most of the models, the results are not always going to be the same.  This unpredictability is welcomed by Pickering, who would contrast it with experiments designed for the classroom where the results will be the same for everyone and there is little chance of any interesting results.  In the Netlogo system, students feel more ownership because they are controlling the sliders and settings.  They also feel more like real scientists because the result is not known beforehand, just like in real science when it is pushing the edge of knowledge.  It doesn’t relate in that Netlogo is still just a program, and not a real-world experiment, so it is slightly less legitimate than a real messy experiment in nature.  But like all models, though not right it is useful to show students how messy science can be.  Qs: How can we convince students that NetLogo represents a useful model of the situation?  What other examples of Netlogo models advancing science are there other than the monkey head turning angle example?
5.   I would consider using the N-Bodies model in a physics class.  I remember writing a similar program in Java in a computer science class in college and thinking it was very cool.  This model would help students with the content objective Use mathematical or computational representations to predict the motion of orbiting objects in the solar system.  This model would fit so well because it allows students to play around with the way that gravity works in a variety of systems.  Students can tweak initial velocities, number of planets, etc to really see how gravity works not just see that the planets go around the sun in circular orbits.