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The sheer number of interacting variables that you'd need to track makes it impossible to make any accurate predictions.

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Edward J. Alessi, P. D., Courtney Hutchison, L. M., & Sarilee Kahn, P. D. (2020). Understanding COVID-19 through a complex trauma lens: Implications for effective psychosocial responses. PsyArXiv. https://doi.org/10.31234/osf.io/8kmqb

This is the part of the enzyme where the substrate binds. The substrate enters the active site to form an enzyme-substrate complex.

This is the process of breaking down complex substances within a living organism into smaller ones. This process also releases energy unlike anabolism.

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Scaffolding teaching? Providing those hints that are mentioned earlier in the article?

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Complex systems thinking is being used for policymaking. Is it the future? (2018, October 25). https://apolitical.co/en/solution_article/complex-systems-thinking-policymaking

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Starting to get a little bit more into linear algebra / complex numbers. I'd like to see this happen more gradually as I haven't used any of this since college.

How Complex Systems Fail

Lowering costs for learning materials via OER: A complicated problem vs what Dave calls complex problems, like open pedagogies.

question interrogates problem of... wow, hard for me to encapsulate in a sentence. How about: Us teachers and other stakeholders need to engage in dialogue/action around equity and NGSS in our own local connections yet be aware of and engage with other local dialogues and furthermore transform/be-transformed-by the emerging, higher level discourse. np

When I view this diagram, I am reminded of Robert Rosen's Modeling Relation, an image of which is here The Modeling Relation grew out of research in Relational Biology which was the first mathematical biology to recognize that relations among organism components and between those components and the environment are key to understanding complex adaptive systems.

As I begin to read this (as I did back in 2000 when I was introduced to Doug) I begin to think in terms of reductionism as a practice in the face of problems which are highly complex, nonlinear, and which do not submit to chunking.

this stands out to me that a complex idea in the mind of one person will be fluid and different from day to day, my gut reaction was that an individual's "complex ideas" would remain rather unaltered for extended periods of time, even a lifetime. Maybe as one develops throughout life (adolescence to adulthood) one's thoughts would change, but not day to day

Very nice review. Please note the reference [94] (Pantazatos et al.) is misplaced because they did not argue that spatial biases cannot entirely explain the putative links between CGE and functional segregation. Instead, they argued there was insufficient evidence in the original Richiardi et al. study linking elevated CGE with resting state functional networks, and that spatial biases may in fact entirely account for their findings. To describe the debate/exchange more accurately, I would suggest replacing the below sentence

“While spatial biases may contribute to these findings, as nodes belonging to the same module tend to be anatomically colocalized [7,8], they cannot explain these effects entirely [94,95].”

with the below paragraph:

“Spatial biases may contribute to these findings, as nodes belonging to the same module tend to be anatomically colocalized [7,8]. Pantazatos et al. argued that these findings are entirely explained by spatial biases [94]. They showed that elevated CGE, as defined in the original Richiardi et al. study, falls monotonically as longer distance edges are removed. Moreover, they showed that 1,000 sets of randomly spaced modules all have significantly high CGE when using the same null distribution defined in the original Richiardi et al. analyses. Therefore, elevated CGE is not specifically related to functional segregation as defined by resting state functional networks, which is in direct contradiction to the main conclusion of the original Richiardi et al. study. Since randomly placed modules do not align (spatially) with any distributed pattern of functional segregation, the finding of elevated CGE may instead be attributed entirely to anatomical colocalization of the nodes within each module. In their rebuttal to [94], Richiardi et al. argue spatial biases cannot explain their findings entirely [95]. However, the authors do not offer an explanation for significantly high CGE observed for randomly spaced sets of modules, other than to note that nodes tend to be closer on average compared to when modules are defined by resting state fMRI. Future work is required to dissociate the effects of spatially proximity on relationships between CGE and spatially distributed functional networks.”

Military Industrial Complex:

1. Eisenhower has seen the consequences of this intersection of military power and his own "new look" policy

Presidential speeches can be measured by how long we talk about them. Still one of the most referenced presidential speeches ever given.

IRAN — Mohammed Mossafegh (1951–1954)

• First military Coup during CIA golden age
• US tells Shah Mohammad Reza Pahlavi (1941–1979) that they will take over the country unless he overthrows Mossafegh.
• For 20+ years we supported a dictator who murdered his own people
• Any nation state has the option to buy out foreign companies

Guatemala — Jacobo Arbenz Guzman (1951–1954)

• Democratically elected leader, called for Progressive Reform (second President to do so)
• Nationalizing land (US decided it looked like Communism)
• Guzman runs into problems with the United Fruit Company, who had been cheating on their taxes, undervaluing their land prices. Government seeks to purchase land to nationalize it, and wants to buy it for the price that the UFC valued their land for.
• UFC and US Government set up a military Coup. Using radio broadcast propaganda, pretending that an army is ravaging the countryside. Guzman believes the propaganda and flees. We set up a dictator.

This is an interesting move in terms of form. Hayles herself is the one who introduced the term "dance" and then immediately amends it parenthetically to comment that she prefers the term "complex ecology." I'm not sure why she chose to leave both of those thoughts in, but I like it.

That does not really explain why there are very talented musicians that have limb defects, but I suppose that similar to a blind person being able to hear better, their brains adjust (like complex-adaptive systems do) and reassign a new input-element (e.g. the feet) to a left-over motoric system(e.g. the hands).

Sounds like one big happy family

Should be credited to Stanley Fish.

We can make the argument here that the University of North Texas, the Oklahoma Historical Society, and the Ethics in Journalism Foundation support de facto slave labor. Let's be honest here: "workers" = "prisoners"

I can relate. I have deep respect for AK but often find myself flipping back and forth between strong agreement and feeling somewhat off-put by what he writes. That seems like a healthier relationship to have with any public intellectual though than 100% agreement 100% of the time. I'm so glad his writing exists in the world and I'm glad yours does too. Seems like this kind of layered relationship is just part of dealing with complex systems. :-)

"the combination of multiple strange loops"

Formal models are explicit in the assumptions they make about how the parts of a system work and interact, and moreover are explicit in the aspects of reality they omit.

-- Paul Smaldino

You already have a generating model for your data – it’s your model. Statistical analyses on model data often involve modeling your model with a stupider model. Don’t do this. Instead, run enough simulations to obtain limiting distributions.

I have come across too many modeling papers in which the model – that is, the parts, all their components, the relationships between them, and mechanisms for change – is not clearly expressed. This is most common with computational models (such as agent-based models), which can be quite complicated, but also exists in cases of purely mathematical models.

This is important for at least two reasons, the first and foremost of which is that science absolutely requires empirical data. Those data are often painstaking to collect, requiring clever, meticulous, and occasionally tedious labor. There is a certain kind of laziness inherent in the professional modeler, who builds entire worlds from his or her desk using only pen, paper, and computer. Relatedly, many scientists are truly fantastic communicators, and present extremely clear theories that advance scientific understanding without a formal model in sight. Charles Darwin, to give an extreme example, laid almost all the foundations of modern evolutionary biology without writing down a single equation.

General Relativity may also turn out to be a "dumb model". https://twitter.com/worrydream/status/672957979545571329

Twelve good uses for dumb models, William Wimsatt (1987).

By making our assumptions explicit, we can clearly assess their implied conclusions. These conclusions will inevitably be flawed, because the assumptions are ultimately incorrect or at least incomplete. By examining how they differ from reality, we can refine our models, and thereby refine our theories and so gradually we might become less wrong.

A model that approximates some aspect of reality can be very useful, even if the model itself is flat-out wrong.

But on the other hand, we can't accept approximation of reality as hard proof that a model is correct.

Many people, including some with training in advanced mathematics, contradicted her smugly. But a simple computer program that models the situation can demonstrate her point.

2/3 times, your first pick will be wrong. Every time that happens, the door Monty didn't open is the winner. So switching wins 2/3 times.

http://marilynvossavant.com/game-show-problem/

Probably a good thing to add to the Complex Data integration workshop write up

German "military-industrial complex"--does m-i c have the same meaning in every country?