• for: example - systems thinking, quote -: many people are complex systems thinkers

• quote:

  • many people are complex systems thinkers even though they don't know it
• Thomas Homer-Dixon

• date: 2023

• examples: complex systems clichés

  • the whole of greater than the sum of its parts
    • nonlinearities
  • the straw that broke the camels back
    • emergence

• comment

  • the first one could be that systems are not the same as just all the parts
  • the second one could also represent Tipping points and nonlinearities of complex systems

08:00 his sword technique was adaptable, mendable, consistent with the complex nature of reality, that changes constantly, not resisting change but adapting self to it

• see zk on how a more dynamic approach to productivity and systems can help us reflect reality more closely, ever changing

A term recommended by Eve regarding an interdisciplinary approach that accounts for multiple feedback loops within complex systems. Need to confer complex systems science to see if ADHD is already addressed in that domain.

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

Jazayeri, A., & Yang, C. C. (2020). Motif Discovery Algorithms in Static and Temporal Networks: A Survey. ArXiv:2005.09721 [Physics]. http://arxiv.org/abs/2005.09721

Della Rossa, F., & DeLellis, P. (2020). Stochastic master stability function for noisy complex networks. Physical Review E, 101(5), 052211. https://doi.org/10.1103/PhysRevE.101.052211

How Complex Systems Fail

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.

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).

Should be credited to Stanley Fish.

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. :-)

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/