(Part 2) Reddit mentions: The best computer simulation books

I guess computational economics is many different things to many different people. Easiest way to combine your degrees would probably be to look into something like machine learning applied to the domain of economics. Related is the whole area of financial engineering. There are quite a lot of MOOCs that cover various aspects of that.

An area I find very interesting is Agent Based Modeling. It's still not a very respected field though it has grown quite a lot. A good introductory book is Agent-Based and Individual-Based Modeling: A Practical Introduction. It's a bit on the practical side but a very nice read. If you want to go further into that area, there are many areas of research that combine CS and economics.

It's up!

I hope it helps you. I still have a few minor formatting issues from the .mobi conversion, but the material looks good. Let me know if you have any questions or requests.

The assessment is interesting, and from a physical/dynamical perspective, it's very enticing. However I can't help but feel unsatisfied that still it's not clear what society should actually do in such a situation.

I also tend to agree with the wildfire analogy right at the very end and have used it myself a few times. I think the useful thing about a wildfire is its obvious ability to quickly deconstruct a massive amount of space at a molecular level, allowing new life to take its place. Nature, evolution, culture are all emergent properties of hysteresis; the past is encoded deeply into the future. When the environment/constraints of life change quicker than the hysteresis allows, societies (or avalanches) collapse. While catastrophic, these collapses can also open new space for new opportunities to blossom that otherwise would not get the chance to.

So I think the problem is that as humans, a controlled and quick deconstruction is not something we like or are good at doing. Tradition, while useful in it's wisdom, also has an interval of relevance. If the constraints of life change quicker than tradition can explain, one must change and explore the chaos and unknown. The age old dichotomy of left and right or yin and yang. Obviously it's a balance of the two, so that means we need to learn as a society when to be swift, and when to be calm.

In today's world where change seems inevitable and tradition longs for relevancy, we face the dilemma of what we keep and what we throw over board. If we don't figure it out fast enough, the probability of collapse or at least a catastrophe will continue to increase as the constraints of life overpower our ability to make the choices required to create a good future and prevent misery.

PS. The citations on the wiki article on Self-organized Criticality is an interesting place to explore the idea of criticality in nature, the human brain, and society. One of the original authors, Per Bak, wrote a whole book on this subject which I've heard is good though I have not had the chance to read yet.

Responding publicly to: "Any recommendations for stuff to read about agent based modeling?"

One of the best resources for agent based modeling is the modeling tool, NetLogo. It's developed by Northwestern:

https://ccl.northwestern.edu/netlogo/

It has TONS of sample models in quite a few different disciplines to see how things work.

Railsback and Grimm have a nice textbook style book on agent based modeling (http://www.amazon.com/Agent-Based-Individual-Based-Modeling-Practical-Introduction/dp/0691136742)

Mitchel and Resnick have a smaller book focused on the concepts of ABM called Turtles, Termites, and Traffic Jams. (http://www.amazon.com/Turtles-Termites-Traffic-Jams-Explorations/dp/0262680939)

Lastly Growing Artificial Societies by Epstien (http://www.amazon.com/Growing-Artificial-Societies-Science-Adaptive/dp/0262550253). He developed generative models of economics using an environment he called "Sugarscape".

Another popular modeling system is Repast (written by people at Argonne National Labs) but I think it's not as easy for the non-programmer to get started with. If you happen to be near University of Oregon, they are having a complexity conference later this month that features a day-long seminar on Repast taught by some guys from Argonne.
http://calendar.uoregon.edu/event/exploring_complexity

Those books are all quite good, and I would also recommend Jim Albert's Bayesian Computation with R as a supplement to Gelman's Bayesian Data Analysis text, as Albert provides R code for many of Gelman's examples.

Hacking: The Underground Guide to Computer Hacking, Including Wireless Networks, Security, Windows, Kali Linux and Penetration Testing https://www.amazon.com/dp/B077BRS413/ref=cm_sw_r_cp_api_T3jaCb7YWNE3T

I say anytime you can learn about the issues with systems, the better off you’ll be implementing them.

Anany Levitin's Introduction to the Design and Analysis of Algorithms. The MIT videos are also very useful for self study.

I like Anany Levitin's: https://www.amazon.com/Design-Analysis-Algorithms-Anany-Levitin/dp/8120334213

You can also get Lindenmayer's book used on amazon for 20 bucks.

https://www.amazon.com/Algorithmic-Beauty-Plants-Virtual-Laboratory/dp/0387972978/ref=sr_1_fkmrnull_1?keywords=the+algorithmic+beauty+of+plants&qid=1549593472&sr=8-1-fkmrnull

Books? WHAT YEAR IS IT?

Gotta know your PDE

Oh hey here’s one

Here’s another

http://www.amazon.com/Oculus-Rift-Virtual-Reality-ebook/dp/B00DDXLM94/ref=sr_1_1?s=digital-text&ie=UTF8&qid=1371299310&sr=1-1&keywords=oculus+rift

You need this and pick on of these. Now you just need a shitload of freetime.

My suggestion would be the JCB 3CX or the NH B115.

At the risk of self-promotion, I just published a book in this series: A User's Guide to Network Analysis in R.

I assume googling already got you to the RasterTek tutorials.

Frank D. Luna's book is good, Introduction to 3d Game Programming with DirectX 11. While introductory it's fairly comprehensive of the pipeline and why (sometimes brief, but the main DX docs fill that in).

Beginning DirectX11 Game Programming, is a bit more newcomer friendly but a lot less comprehensive.

HLSL Development Cookbook is a reasonable fill-in for HLSL and shaders that you would actually use. It doesn't cover the nitty gritty like DX api calls, but does explain what you need to have your render states setup as each step of the way.

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If you aren't already familiar with 11 then 12 is likely out of your league for the time being. DX10 began the move towards a lower-level API in comparison to DX9 and OpenGL.

Here is a run down on the subject. I don't have experience with it besides looking at some articles and a couple talks on the application of agent based modeling in ecology. Talked with some REU mentors a lot about it, too.

Say you are interested in modelling the rate of disease in a population. We will assume that once you become infected, you stay infected. Typically, we would use one differential equation to model the rate of being infected and the rate of being susceptible to a disease over time.

You'd add in birth and death processes and eventually make your model more realistic for the disease spread scenario you are looking at. An issue that arises is: the spread of the disease could be stochastic, and individuals become infected without some deterministic formula (e.g. spread of disease is hard to measure even if you had alot of resources and money). You can turn your simple system of ODE's into a stochastic approximation using the Gillespie algorithm. The change from being susceptible to infected, as well as other parts of the system of ODE's, is now governed by a pseudorandom number generation process that is used to account for uncertainty of how the two groups work together.

The benefits of the stochastic approximation is you can have a carrying capacity (some asymptote) that is a positive integer, where as with a continuous system of ODE's you could get a result of, say, 12.3 infected individuals at t=500. The other added benefit is the acknowledgment and formulation of uncertainty in the real-life scenario you are trying to model.

Since computation is rarely an issue anymore, people started to wonder if instead of modeling groups of a population, we could model each individual of a population instead. Instead of having a system of two ODE's, or two stochastic ODE's, we have a system of 1000 individuals, each with their own formula. This has a lot of favor in ecology and biomathematics modeling.

Doing an agent based model on your own would be tough - there are programs that do the hard work for you. I don't know if SAS has anything; I know base MATLAB doesn't. R might.

This book looks good:
http://www.amazon.com/Agent-Based-Individual-Based-Modeling-Practical-Introduction/dp/0691136742/ref=sr_1_1?s=books&ie=UTF8&qid=1393906325&sr=1-1&keywords=agent+based+and+individual+based+modeling