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Just finished "Model Thinking"  taught by Scott E. Page.
I really enjoyed this class. It provided me with several very useful mathematical tools to think more clearly about important and frequent problems. Scott is a great teacher.
⬐ dzongaeasily one of the best courses around. teaches you a new way to look at problems. having learned about automata in cs, and see it applied to social sciences was mind blowing.⬐ frankie_tI've seen this one in 'the old days', but never actually took it, just skimmed through the introduction. It was hard for me to grasp what I would take from it, even though it seemed interesting.
Can you please summarize what you took from the course? How would you use outside of the course the information you've learned? I would imagine something like "I've learned a model A that can be applied at real life situations such as a, b to get better insight", or maybe even "I can write a simulation to predict how some system will evolve in the future"?⬐ ghaffThat was one of the few MOOCs I completed back in the day. I don't really remember everything in the course--but not everything needs to be practical. One interesting thing I specifically do remember is Schelling's model which basically demonstrated that fairly subtle preferences can lead to very different outcomes.⬐ toumhiI'm currently taking it and loving it. What I took from it so far is a toolbox for better thinking.
- why you only have to convince small % of people for your idea to be adopted by most people (schelling models) - how self-organization in cities and other domains works (lyapunov functions) - multiple models for decision making (decision trees, spatial choice models) - how economic growth works and how innovation is the only way to avoid economic stagnation (whether it's a good thing is another story) - how linear models are super useful to understand complex situations - how diseases spread and why R0 is so important (quite timely with covid!) - how to try different perspectives and heuristics to solve problems - how culture evolves and how innovation works to change the culture - how networks work - and more things!
Loved Model Thinking, taught by Scott Page at UM. Good for the current model dependent times as well.
We live in a complex world with diverse people, firms, and governments whose behaviors aggregate to produce novel, unexpected phenomena. We see political uprisings, market crashes, and a never ending array of social trends. How do we make sense of it? Models. Evidence shows that people who think with models consistently outperform those who don't. And, moreover people who think with lots of models outperform people who use only one. Why do models make us better thinkers? Models help us to better organize information - to make sense of that fire hose or hairball of data (choose your metaphor) available on the Internet. Models improve our abilities to make accurate forecasts. They help us make better decisions and adopt more effective strategies. They even can improve our ability to design institutions and procedures. In this class, I present a starter kit of models: I start with models of tipping points. I move on to cover models explain the wisdom of crowds, models that show why some countries are rich and some are poor, and models that help unpack the strategic decisions of firm and politicians.
The models covered in this class provide a foundation for future social science classes, whether they be in economics, political science, business, or sociology. Mastering this material will give you a huge leg up in advanced courses. They also help you in life. Here's how the course will work. For each model, I present a short, easily digestible overview lecture. Then, I'll dig deeper. I'll go into the technical details of the model. Those technical lectures won't require calculus but be prepared for some algebra. For all the lectures, I'll offer some questions and we'll have quizzes and even a final exam. If you decide to do the deep dive, and take all the quizzes and the exam, you'll receive a Course Certificate. If you just decide to follow along for the introductory lectures to gain some exposure that's fine too. It's all free. And it's all here to help make you a better thinker!
I know that you didn't ask for this but I think this is the best course on Coursera hands down:
I've larned much more from this than from anything else on this site.
⬐ techsin101Any examples of what makes it good
I enjoyed taking Model Thinking: https://www.coursera.org/learn/model-thinking
It's designed to be a foundation course for subsequent social science classes, but I personally found the exposure to models from different fields of study to be quite insightful.
If you're interested, there's also a book by the professor on the same topic: https://www.goodreads.com/en/book/show/39088592-the-model-th...
⬐ sulsCame here to say the same. Especially now that the world is struggling with SARS-CoV-2/COVID-19, understanding these different models helps a lot!
Last but not least, Scott E. Page is a great educator. Glad to hear there is a book from him now -- his papers were a good read as well during/after the course.⬐ radiowaveAgreed. I took Model Thinking years back, and it's probably the course that I most enjoyed (as in: just for it's own sake). I had no idea there was now also a book.⬐ mportela+1. One of the best courses I took in Coursera, especially because my background is in Engineering, not Social Sciences.⬐ xkgtI took this course few years back as well. Quite enjoyed it at that time. Recommended!⬐ pmohunModel Thinking is the best Coursera course that I've taken. The lessons have practically applied to many areas of my personal and professional life in a way that far exceeded my expectations.
Scott is one of the best teachers on the topic, and makes complex models simple and intuitive to understand.
It is a long course, but well worth it. Cannot recommend it enough.
For those interested, there's a great course on models on Coursera where they (amongst other models) talk about tipping points
Not a book, but a course. This one was insanely useful for me and I've applied the models countless times: [Model Thinking]. I heartily recommend this to anyone who wants to learn about models and has some spare time:
That's one of the main takeaways from the cellular automata models lecture in Coursera's Model Thinking course (https://www.coursera.org/learn/model-thinking)
"It from bit"
what are your thoughts on this course: https://www.coursera.org/learn/model-thinking
⬐ OldHand2018That course looks great. Keep in mind that you are unlikely to "learn" anything "new". It's a process; do this, do that. Put some effort here, think about that stuff there. And then you start to see interactions and cause/effect patterns that you didn't see before as well as behaviors that work completely differently than you previously believed.
Why is it linked to an affiliate website and not coursera itself?
EDIT: here the original coursera link: https://www.coursera.org/learn/model-thinking
⬐ thinkingkongI was on my phone and couldnt copy the target link without the app opening the page instead. :/
A related course on the topic: https://www.coursera.org/learn/model-thinking
I found it very interesting.
Model thinking (https://www.coursera.org/learn/model-thinking)
Taught by Prof. Scott E. Page, teaches about models in several fields and how they're used to aid thinking about complex issues by careful design and usage.
A couple of insights: all models are wrong but some are useful. Having many models about a situation to help your thinking is better than having only one, and much better than none. Complex models are not necessarily better than simple ones.
⬐ thelittlenagAbsolutely, this was one of the better courses I've taken. My only wish would be to have the course expand and include models from says physics or chemistry, just so that students could get a feel for how both quantitative and qualitative models can both be useful.⬐ SohakesI love this one. Saw it when it launched and it frequently helps me see through some situations. Some models explains why, despite best intentions, things go awry sometimes. Sometimes things are terrible not because people are terrible, but because everything interact in ways that are difficult to understand and predict.
This is a bit of a non answer, but there was an early MOOC delivered on this topic. I remember this was back in 2010/2011 and the topic was System Thinking.
The course was delivered by a political scientist, but focused entirely on systems thinking.
I will look for it and return; But for now I don't have the name of the course, or the university. I can remember is the year :).
Edit: Found it; https://www.coursera.org/learn/model-thinking
I found this very useful. https://www.coursera.org/learn/model-thinking
This is not book, but it has 'content' and an 'author'. A Coursera course: "Model Thinking" by Scott E. Page
Reminds me of the Coursera course on model thinking: https://www.coursera.org/learn/model-thinking
The segregation model was one of the models (which I though eas pretty neat), the course is basically an introduction to a bunch of different models to think about the world. Highly recommended.
I would like to recommend one more MOOC that covers different models (economic models, modelling people behavior, randomness, collective actions -Prisoner's dilemma, segregation models) and make you start thinking about the world in term of models.
⬐ kornishIf anyone is interested in this course but would prefer to read a book instead, most of this class draws from "Micromotives and Macrobehaviors" by Nobel economist Thomas Schelling. The book basically serves as a compendium of different classes of models, and explores how counterintuitive behavior of the collective can arrive from perfectly reasonable and rational individual strategies of the actors.
It kind of reminds me of the explanation of how the crowd at the Hajj crush (on the front page earlier today) behaved more like a fluid than crowd of communicating agents because the human density was so high.⬐ nicolapedeThanks a lot for sharing it. I have just looked it up on Amazon and found that they say something like 'before Freaknomics ...'. What is the link between the two?⬐ oli5679Freakonomics is a write up of several economics papers that used a statistical method called Instrumental Variables for causal analysis.
Schelling's book is a write-up of the theoretical predictions of several different game-theory models. I didn't notice much overlap between the two.
You can create emergent behavior even with very crude, seemingly simplistic rule-based models. Forget about AI. Think about modeling processes. You can model a process using a very crude set of hand-crafted rules and still get useful simulations that give you valuable insight into the original process.
There is a wonderful free course about this kinds of stuff:
Highly recommend it.
One of the authors, Scott Page, majored in math at the University of Michigan. He also teaches the course "Model Thinking" on Coursera: https://www.coursera.org/course/modelthinking. Perhaps one to avoid...
⬐ dxbydtHey that's a great course. Pls do not avoid. Highly recommend signing up. The reason he wrote this famous paper & the book that evolved from that paper, in his own words - http://vserver1.cscs.lsa.umich.edu/~spage/thedifference_inte...
He says - "progress and innovation may depend less on lone thinkers with enormous IQs than on diverse people working together"
Not too sure about that. At all.Especially not in math, the subject he majored in.⬐ eropple> He says - "progress and innovation may depend less on lone thinkers with enormous IQs than on diverse people working together"
> Not too sure about that.
Why would that not be a thing?
I mean, I am not the smartest person on my team. The list of things I know very little about is long. But I occasionally ask questions that are really obvious to me that reorient the discussion because people didn't think of them--problems that were Too Obvious To See up close, you know? And, similarly, when working in stuff that I do know a lot about, I find myself ignoring things that are to me so obvious and basic that my brain just goes right by them.
Innovation isn't like Civilization, you aren't just generating lightbulbs. Diversity of thought process leads to some inefficiencies, but it helps you reach new global maxima.⬐ jrapdx3I'm not so sure diverse (divergent?) approaches to a problem are always better than one leader's effort.
For one thing, stand-out genius or talent shows up randomly in populations, it's not really predictable when or where it will occur. But on rare occasion when a natural leader does emerge, suppressing diversity (of problem-solving approaches) is likely the better strategy.
The true maxima of human effort have generally followed this pattern, born of singularity and not diversity. Once an innovation is widely enough known, it attracts a diverse range of followers who improve the idea and nurture its maturation. Diversity is useful for the "aftercare" of innovation, but not its creation.
The other major point is that value of diversity (however defined) vs. uniformity depends on context. In the context of one's home, there may be many ways to arrange furniture in a room, and with few exceptions, one way is as valid as another. By contrast, in a shared household over time diverse opinions are likely to converge to an arrangement using the space optimally. Diversity probably leads to a better workflow solution vs. one person's choices.
OTOH there is not a great diversity of "valid" ways to remove an inflamed appendix, that is, regardless of other considerations, a qualified surgeon is required and procedural diversity is constrained by the anatomical realities. This situation demands uniformity not diversity.
Finally, "diversity" has an indeterminate number of definitions, and applicability of the term is entirely dependent on context. On second thought, that would seem to drain it of specificity or meaning, on that ground, I should have a policy of using "diversity" carefully and sparingly.
If you have the time, there is a good Coursera course on the domain: https://www.coursera.org/course/modelthinking
Ok but that's not really what he's talking about.
I totally agree that being able to have everyone walk to work may be a better goal than self driving cars and sadly because of past choices our best option is probably demolishing cities (not terribly feasible). However the status quo doesn't have to be maintained, rezoning and incremental new development can fix much of this (look outside American city development) but now we are also dangerously off topic.
Thiel says "the world has willingly retreated from a culture of risk and exploration towards one of safety and regulation. We have discarded a century of can-do ambition built on rapid advances in technology and replaced it with a cautiousness far too satisfied with incremental improvements."
This is his standard libertarian line. More risk, less big government and safety nets. Putting a gun to more peoples heads (metaphorically) will somehow instantly make the world better, create more innovation, and etc etc etc. (There's actually some pretty good data backed evidence instead of gut feeling backed evidence that good social safety nets promote a healthier society that’s more innovative that came up in coursera's Michigan University Model Thinking class [https://www.coursera.org/course/modelthinking])
And he also buys into that false thinking of "yesterday was better" when he talks about "However, we bounded forward in the 1950s and 1960s thanks to a generation of scientists who did not just believe in a better future but invented it" and "The genuine progress in IT from the 1970s up to the 2000s masked the relative stagnation of energy, transportation, space, materials, agriculture and medicine."
I don't buy this narrative at all for the reasons stated above. As I pointed out above, I don't buy his perceived stagnation and lack of innovation and I strongly don't buy into his current fears and solutions. Also, this is pretty much general libertarian/Peter Theil philosophy very loosely redressed to try and seem new and relevant in light of the recent election, but it's the same old view.
In that light, some people agree with it, and some don't but there's nothing new here in this article either.
⬐ yummyfajitasThis is his standard libertarian line.
I don't think you are fairly representing Thiel. I don't think he is making libertarian points at all, since he seems to advocate in favor of big government projects (at least in principle). Indeed, it's tough to argue this is a libertarian point, since he criticizes the private sector for the exact same thing.
I think his view is that this is just a cultural shift.
There used to be all sorts of circulating ideas about large projects ...A 20th century example is Robert Moses, who in the 1920s simultaneously held twelve fairy high-level government posts...bulldoze neighborhoods, build roads and highways, and do a lot of planning and construction...From today’s perspective, this is crazy.⬐ guylhemIt's indeed an ideological reply, because for someone with a libertarian approach (like - me), the slowdown seems to match quite well the rise of the welfare state.
"the world has willingly retreated from a culture of risk and exploration towards one of safety and regulation" - that says it all.
The competing ideology (let's say "progressive") cares about stuff like equality (ex: affirmative action) or safety (look at what you did as a kid, and what is allowed today) while most libertarians and conservative care about efficiency and freedom.
Some equality and safety might be a god thing - but I worry much more about efficiency and freedom - a part of which is scientific progress.
There's some basic differences here and there, but that is IMHO the core difference.⬐ tlbWelfare may discourage ambition among the low- and middle-classes, but I don't believe it affects scientists or inventors.
No one is debating the choice between living off welfare or solving the world's problem, and thinking "if welfare was just a bit cushier I could stop inventing".⬐ guylhem⬐ mindstabLet's talk about scientists and inventors - especially the world changing ones that are so rare.
What if it had some effect? I mean, something small - but non zero?
What if it did reduce their drive just enough that it made their rarity almost identical to an absence?
Answer- we don't know. So far, the welfare state seems to have increased scientific output, but maybe there's a limit and too much welfare will damage it.
(Think about Einstein on welfare with enough money not to care- would he have spent time as a patent office clerk? How important was this stint in becoming himself? What effect does it has on others?)
We just don't know - maybe because the welfare state is too recent. With 50 more years, maybe we will notice differences?
I'm only speculating - I'm not sure there is any answer. It's just something that bothering me a lot.Part of me wants to debate this, point our correlation does not equal causation and ask for better evidence. (yeah, I know, cheap attempt at last word)
But ultimately we probably won't be persuading each other and at this point the debate is useless. I've pointed out my problems with the article, and together we've at least distilled the problem people like we will have with the article (well stated by guylhem). I don't think much more progress can be made :)
My critical reasoning and logic prof from Uni always said that logic is a fine thing for enhancing discourse but if you start with different world views, different axioms, no amount of logic can bridge that, it's just a tool for fixing smaller disputes.
Thank's at least for more concisely summing up :)⬐ guylhemThanks for your understanding.
I don't want to push any ideology or debate on this - I just see a weird trend that other people seems to notice too, and I'd like to understand what is happening, regardless of whose ideology is used to explain that.