Best system theory books according to redditors
We found 40 Reddit comments discussing the best system theory books. We ranked the 13 resulting products by number of redditors who mentioned them. Here are the top 20.
We found 40 Reddit comments discussing the best system theory books. We ranked the 13 resulting products by number of redditors who mentioned them. Here are the top 20.
It's funny to me (not ha-ha funny) that the founding fathers were prepared for the eventuality that the people would elect a bad President, but they didn't predict that both of the mechanisms they put in place to prevent this - the electoral college and Congress - might also be bad at the same time.
There a great book called Systemantics about how systems fail, and in it the author talks about how catastrophic failures happen when one safety mechanism fails and only then do we discover that all of the backup safety mechanisms have been failing for years and no one knew it because that last one was holding. In this case, that last one is our Presidents being basically good people and holding to the traditions of the office, and for that matter to basic human decency.
All of which is a long way of agreeing with you: We need to codify in law a lot of the things that Presidents (and candidates) have traditionally done voluntarily. For example, releasing tax returns.
The book Systemantics (1st edition) is a fun read on how systems become their own problem, and provides many of these insights in a lighthearted way. It is interesting to me how much of this was observed 40 years ago...
I joined the USAF in 1981, became a contractor in 1986, joined the Air Force C-17 program office at Wright-Patterson in 1988, and I've been there as a Unix sysadmin ever since. Everything you've seen here about gov't IT is true.
I wrote a how-to on something many years ago and mentioned that I'd been an admin for around 20 years. The best comment I got was someone saying "If I'm still an admin after 20 years, would someone please write a program to kill me?"
How to avoid strangling the person in the next cubicle? These two links made a big difference for me:
Once I understood that the DoD (and any other large system I have to deal with) acts the way it does mainly because of size rather than malice, my job got a lot better.
It isn't a book about programming, but The Recursive Universe by William Poundstone is a fascinating book about Conway's game of Life and it's implications of complexity from simple rules in the universe in general.
Alas, it's long out of print, and even the used copies on Amazon are going for high (>$20) prices.
It's about time I re-read it...
Take a look at Nexus. I'm reading a library copy right now. It's about how and why you would build a graph that has the 'small world' property. 'Small world' is defined as a large graph that has a small number of links between the two points most remote from each other. Think six degrees of separation. It's a good layman's book, I'm not sure how well it stands up to someone with a lot of graph theory background. He does cite sources, though, so if you have the desire, you can dig into the theory underpinning the book.
The social branch of network science studies this kind of thing and would have some good uses for the data set, I'm sure.
http://scholar.google.com/scholar?hl=en&q=social+contact+network&btnG=&as_sdt=1%2C5&as_sdtp=
http://barabasilab.com/pubs-socialnets.php
http://barabasilab.com/pubs-humandynamics.php
http://www-personal.umich.edu/~mejn/pubs.html
With respect to looking for happiness, you might look for studies on sentiment analysis and the spreading of emotion in social networks. I know people have looked at how positive and negative emotion traverses the graph of twitter followers and retweets.
There's a small lifetime's worth of reading in those links. If you want a fairly comprehensive introduction that balances well between theory and examples, check out Mark Newman's book.
For the type of graph (network) theory that is currently hot in neuroscience contexts, [Newman's book](http://www.amazon.com/Networks-An-Introduction-Mark-Newman/dp/0199206651
) is a great compendium (quite readable, but fairly comprehensive).
For bedside reading about mammalian cortical networks in particular, Networks of the Brain and Discovering the Human Connectome, both by Olaf Sporns, are well worth a look.
From there... it's already becoming a pretty big literature. If you have some specific areas of interest, I can do my best to point you to resources. Take my suggestions with a grain of salt, though... I'm a pure mathematician who kinda got seduced into applied maths... which means I probably don't know as much about either discipline as I should.
This book is a pretty good start:
The Perfect Swarm: The Science of Complexity in Everyday Life - by Len Fisher
Thanks! Just bought a used copy of the original hardback. What’s changed in the second edition?
Other books about symmetry:
Symmetry (review, Princeton Univ. Press, archive.org, amazon)
The Symmetries of Things (review 1, review 2, CRC Press, errata, lecture, amazon)
Structure in Nature is a Strategy for Design (amazon)
Patterns in Nature (amazon)
Handbook of Regular Patterns (amazon)
Shape, Space, and Symmetry (review, amazon)
Symmetry in Science and Art (amazon)
Crystal Structures I: Patterns and Symmetry (amazon)
Space Groups for Solid State Scientists (amazon)
International Tables for Crystallography (site)
Bibliographies:
http://www.york.ac.uk/depts/maths/histstat/symmetry/symmetrybib.pdf
http://www.georgehart.com/virtual-polyhedra/references.html
You can easily write scaling blocks that will filter noise. Also most AI cards come with features for this.. this is explained in Hans Bergers recent book Automating with SIMATIC: Hardware and Software, Configuration and Programming, Data Communication, Operator Control and Monitoring https://www.amazon.com/dp/3895784591?ref=yo_pop_ma_swf
He has written a lot of stuff and it's the best. Consise, accurate, thorough. German. Haha
A six-foot tall single women told me today that fat people are simply using food to replace love...she is 20 pounds overweight.
I held my tongue, but really, isn't it easier for her to lose 20 pounds than an obese person to lose 100 pounds?
Fat people are fat because their ancestors were better at conserving calories, if we ever face a famine, the fat people will be "normal weight"--everyone else will die. And the next generation, given ample resources, will be even fatter.
If you "believe" fat people are pigs and "normal" people have great "Self-Control", I invite you to read a new book, The Watchman's Rattle.
The end of societies begins with replacing "fact" with "belief".
Senior Level Software Engineer Reading List
Read This First
Fundamentals
Development Theory
Philosophy of Programming
Mentality
Software Engineering Skill Sets
Design
History
Specialist Skills
DevOps Reading List
Added to wish list.
I would recommend Frequency Identification, which is explained quite well in Schoukens et al. (http://www.amazon.com/System-Identification-Frequency-Domain-Approach/dp/0470640375) Their accompanying Matlab examples are free to download and actually explain the procedure quite nicely.
Basically, one puts noise on the input, and measures noise on the output. This will give you the transfer function. More interesting things happen if you want to do this while the quad rotor is flying.
Basic Economics - Thomas Sowell
Six Degrees: The Science of a Connected Age - Duncan Watts
Linked: How Everything Is Connected to Everything Else and What It Means - Albert-Laszlo Barabasi
Nexus: Small Worlds and the Groundbreaking Theory of Networks - Mark Buchanan
The Selfish Gene - Richard Dawkins
Sperm Wars: Infidelity, Sexual Conflict, and Other Bedroom Battles - Robin Baker
Motley Crue: The Dirt - Confessions of the World's Most Notorious Rock Band - Neil Strauss
The Black Swan: The Impact of the Highly Improbable - Nassim Nicholas Taleb
The World is Flat - Thomas Friedman
The Tipping Point: How Little Things Can Make a Big Difference - Malcolm Gladwell
The Wisdom of Crowds - James Surowiecki
Into Thin Air: A Personal Account of the Mt. Everest Disaster - Jon Krakauer
The Climb - Anatoli Boukreev
Ultramarathon Man: Confessions of an All-Night Runner - Dean Karnazes
Not really; this is a new area for games (in terms of approaching it with any degree of theory at all). That's one of the reasons why we tackled it at Horseshoe.
In addition to a few sources listed in the Constructing Emergence paper, John Holland's books Hidden Order and Emergence are theoretically useful, but not so much directly (and they're pretty dense). I hit some of this in my game design text, but I'd like to go back and add more to it now!
Well, as an IR scholar that applies inferential network methods to substantive IR questions, I think the previous findings show promise for a thriving research agenda. Send me a PM if you'd like to talk about anything in particular. If you're looking for IR-substantive references, here are some favorites:
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1287857
https://www.dropbox.com/s/fwzlmae58fx1fax/Cranmer-CV.pdf?dl=0
http://ps.ucdavis.edu/people/maoz/MaozCV.pdf
For networks specific texts, it depends on what level you're at. I'd recommend Wasserman and Faust as a foundation:
https://www.amazon.com/Social-Network-Analysis-Applications-Structural/dp/0521387078
But this is also a favorite:
https://www.amazon.com/Networks-Introduction-Mark-Newman/dp/0199206651/ref=sr_1_1?s=books&ie=UTF8&qid=1475112947&sr=1-1&keywords=newman+networks
From that, I'd recommend reading this:
http://onlinelibrary.wiley.com/doi/10.1111/ajps.12263/abstract
Here are all the local Amazon links I could find:
amazon.co.uk
amazon.ca
amazon.com.au
amazon.in
amazon.com.mx
amazon.de
amazon.it
amazon.es
amazon.com.br
amazon.nl
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It seems like he got it from this book.
Try this one http://www.amazon.com/Ubiquity-Science-History-World-Simpler/dp/060960810X
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MacKay: Information theory, inference and learning algorithms and Newman: Networks for the latter.
Consciousness is an information processing algorithm. Like any algorithm, it's substrate neutral and can be implemented in any number of substrates.
There is no distinction between "physical property" and "emergent property." The entire notion of an "emergent property" has been so distorted by philosophers that it's lost all meaning. It doesn't mean that something magically becomes non-physical. Read some Complexity Theory (John Holland is a decent place to start) if you'd like to understand what an "emergent property" actually is.
Again, look into reductionism. Sociology is psychology is biology is chemistry is physics. We just don't have a complete set of bridge laws yet.