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Q&A site about game development: http://gamedev.stackexchange.c...
Open source game: http://www.wesnoth.org/

It may still be too advanced for 11, but Wesnoth allows people to build custom campaigns.

This may be advanced, but worth a shot: Game Theory

Game Theory doesn't have anything to do with games that we play. This is going to be things like the prisoners' dilemma and decision theory. I.e. a lot about mathematics-based economics and very little about actual games. You might as well suggest that the kid bone up on history. It's more accessible and study can inform how games should work.

You might look for books that link existing games to history or science. They might help awaken interest in those subjects in him. This can help with game development, as it can make the games more realistic. Also, if game development is more like the desire to be a cowboy or a firefighter than a genuine career choice (always possible at 11), history and science have more general benefits.

Hide nothing from him for game development.
He can't have total focus on any particular aspect and be able to ship anything.
Buy this kid a new gaming-class computer every 2 years.
This investment could save you $100,000 in college tuition fees.

This may be advanced, but worth a shot: Game Theory

As far as gameplay goes, follow the mantra of Dwarf Fortress and FTL, "Losing is Fun!".
Occasionally place players in impossible situations so they can experience failure while trying their best.
A game that's too easy or too linear gets dropped quickly.

Maybe he'll pick up the art side as well and make a game before he's out of high school.
8 years is long enough for a really good game, even designed/developed solo - Uplink had 3 devs, Banished is a 1-man shop.
I started programming at age 15 - within a year of getting my 1st computer.
I could have done it earlier, but didn't have the $2000+ required in those days.

More resources
Unity 3d
Playmaker - an AI design tool using zero code.
Blender
Blender Guru
character rigging

Quick assets (some free):
CG Textures
Open Game Art
Turbo Squid

Easy scenery:
Terrain - Terragen
Plants - Speedtree

Google uses aptitude tests, which it has even placed in technical magazines, hoping some really big brains would tackle the hardest problems

Almost all hightech companies look for big brains. Typical questions would look like this:

five pirates have 100 gold coins. they have to divide up the loot. in order of seniority (suppose pirate 5 is most senior, pirate 1 is least senior), the most senior pirate proposes a distribution of the loot. they vote and if at least 50% accept the proposal, the loot is divided as proposed. otherwise the most senior pirate is executed, and they start over again with the next senior pirate. what solution does the most senior pirate propose? assume they are very intelligent and extremely greedy (and that they would prefer not to die).

The answer is in the no. 63 of techInterview. Don't feel depress when you couldn't come up with the right answer, and don't bother memorizing all those answers before going to interview. They probably wouldn't reuse any of them anyway. If you don't have extremely high IQ, you probably want to learn techniques to solve those problems.

As a matter of fact, questions as such are mostly problems in Game Theory(Yes, Game Theory as in the movie A Beautiful Mind). Pirates problem above is a typical game that can be solved by backward induction on an extended subgame. I've actually seen this question in a final examination of Game Theory in my prograduate Economics studies.

All games are a puzzle of some sort,

Pedantically, that is exactly backwards. To be technical about it, what we often call "games" like Doom and Half-Life are not games at all, but puzzles. By definition, a "game" requires competitiveness, so nothing single-player need apply.

There's enough math and computational expertise required in advanced economics to keep any math geek satisfied. It's not a coincidence that large numbers of Physics PhD's are working on Wall Street these days. The cookbook economics you hear on the tube is not the economics being done in research today; it's the economics that politicians and TV newshosts can understand, and communicate in soundbites.

As you alluded, much of basic Econ can be described as a bunch of rules-of-thumb and ad hoc arguments, of the sort, "If we ignore all these things here, and assume that they are constant, we can pretend that this here happens." The problem is that economic systems are complex systems (analogous to the brain's neural network), and can't be modeled well using "billiard ball" physics models. Until recently the only alternative has been to use statistical, "gas law" models and other simplifications of the systems.

Example: a small town may have 1000 citizens, 200 businesses, and perhaps 500 formal and informal groups/organizations. Each of those individuals and organizations has over 1000 'inputs' and 1000 'outputs' - relations with each other and outside entities, that may be considered as economic factors. (Relations may be financial or other.) You have a social network with something like 10^13 relations/interconnections. And that's just a small town or neighborhood.

I'm embarking on a PhD in Econ shortly, after many years in computing, and my math skills are being stretched like they haven't in a long time. Differential equations is a prerequisite for several of the introductory graduate level courses, along with linear algebra and a bunch of statistics and game theory. Thomas Bayes' much appreciated Bayesian Theorem probability is a tool of economists. Vilfredo Pareto (Pareto-optimal" game outcomes) was an economist. Many elements of modern statistics, probability and game theory were developed by economists.

The problem faced by economists has been not that it was too simple, but that the systems under study have been too complex to delve into very deeply until both the mathematical tools and the computational power became available. It was necessary to drastically simplify the models in order to get any sense at all. And, of course, there is a strong philosophical and social-studies thread throughout economics.

Nowadays there is a strong thrust into new approaches to Economics, including complex adaptive systems, agent based systems, Neuroeconomics, Experimental Economics (vis. Vernon Smith, 2002 Bank of Sweden "Nobel" and social network economics.

Often in addition to training and/or experience in biology, physics, systems theory and other disciplines, these approaches require a good understanding of differential equations, comfort in manipulating long chains of partial derivatives, and working with multi-layered irregular networks. Interestingly, even fluid dynamics equations are applicable in some cases.

There's enough math and computational expertise required in advanced economics to keep any math geek satisfied. It's not a coincidence that large numbers of Physics PhD's are working on Wall Street these days. The cookbook economics you hear on the tube is not the economics being done in research today; it's the economics that politicians and TV newshosts can understand, and communicate in soundbites.

As you alluded, much of basic Econ can be described as a bunch of rules-of-thumb and ad hoc arguments, of the sort, "If we ignore all these things here, and assume that they are constant, we can pretend that this here happens." The problem is that economic systems are complex systems (analogous to the brain's neural network), and can't be modeled well using "billiard ball" physics models. Until recently the only alternative has been to use statistical, "gas law" models and other simplifications of the systems.

Example: a small town may have 1000 citizens, 200 businesses, and perhaps 500 formal and informal groups/organizations. Each of those individuals and organizations has over 1000 'inputs' and 1000 'outputs' - relations with each other and outside entities, that may be considered as economic factors. (Relations may be financial or other.) You have a social network with something like 10^13 relations/interconnections. And that's just a small town or neighborhood.

I'm embarking on a PhD in Econ shortly, after many years in computing, and my math skills are being stretched like they haven't in a long time. Differential equations is a prerequisite for several of the introductory graduate level courses, along with linear algebra and a bunch of statistics and game theory. Thomas Bayes' much appreciated Bayesian Theorem probability is a tool of economists. Vilfredo Pareto (Pareto-optimal" game outcomes) was an economist. Many elements of modern statistics, probability and game theory were developed by economists.

The problem faced by economists has been not that it was too simple, but that the systems under study have been too complex to delve into very deeply until both the mathematical tools and the computational power became available. It was necessary to drastically simplify the models in order to get any sense at all. And, of course, there is a strong philosophical and social-studies thread throughout economics.

Nowadays there is a strong thrust into new approaches to Economics, including complex adaptive systems, agent based systems, Neuroeconomics, Experimental Economics (vis. Vernon Smith, 2002 Bank of Sweden "Nobel" and social network economics.

Often in addition to training and/or experience in biology, physics, systems theory and other disciplines, these approaches require a good understanding of differential equations, comfort in manipulating long chains of partial derivatives, and working with multi-layered irregular networks. Interestingly, even fluid dynamics equations are applicable in some cases.

There's enough math and computational expertise required in advanced economics to keep any math geek satisfied. It's not a coincidence that large numbers of Physics PhD's are working on Wall Street these days. The cookbook economics you hear on the tube is not the economics being done in research today; it's the economics that politicians and TV newshosts can understand, and communicate in soundbites.

As you alluded, much of basic Econ can be described as a bunch of rules-of-thumb and ad hoc arguments, of the sort, "If we ignore all these things here, and assume that they are constant, we can pretend that this here happens." The problem is that economic systems are complex systems (analogous to the brain's neural network), and can't be modeled well using "billiard ball" physics models. Until recently the only alternative has been to use statistical, "gas law" models and other simplifications of the systems.

Example: a small town may have 1000 citizens, 200 businesses, and perhaps 500 formal and informal groups/organizations. Each of those individuals and organizations has over 1000 'inputs' and 1000 'outputs' - relations with each other and outside entities, that may be considered as economic factors. (Relations may be financial or other.) You have a social network with something like 10^13 relations/interconnections. And that's just a small town or neighborhood.

I'm embarking on a PhD in Econ shortly, after many years in computing, and my math skills are being stretched like they haven't in a long time. Differential equations is a prerequisite for several of the introductory graduate level courses, along with linear algebra and a bunch of statistics and game theory. Thomas Bayes' much appreciated Bayesian Theorem probability is a tool of economists. Vilfredo Pareto (Pareto-optimal" game outcomes) was an economist. Many elements of modern statistics, probability and game theory were developed by economists.

The problem faced by economists has been not that it was too simple, but that the systems under study have been too complex to delve into very deeply until both the mathematical tools and the computational power became available. It was necessary to drastically simplify the models in order to get any sense at all. And, of course, there is a strong philosophical and social-studies thread throughout economics.

Nowadays there is a strong thrust into new approaches to Economics, including complex adaptive systems, agent based systems, Neuroeconomics, Experimental Economics (vis. Vernon Smith, 2002 Bank of Sweden "Nobel" and social network economics.

Often in addition to training and/or experience in biology, physics, systems theory and other disciplines, these approaches require a good understanding of differential equations, comfort in manipulating long chains of partial derivatives, and working with multi-layered irregular networks. Interestingly, even fluid dynamics equations are applicable in some cases.

There's enough math and computational expertise required in advanced economics to keep any math geek satisfied. It's not a coincidence that large numbers of Physics PhD's are working on Wall Street these days. The cookbook economics you hear on the tube is not the economics being done in research today; it's the economics that politicians and TV newshosts can understand, and communicate in soundbites.

As you alluded, much of basic Econ can be described as a bunch of rules-of-thumb and ad hoc arguments, of the sort, "If we ignore all these things here, and assume that they are constant, we can pretend that this here happens." The problem is that economic systems are complex systems (analogous to the brain's neural network), and can't be modeled well using "billiard ball" physics models. Until recently the only alternative has been to use statistical, "gas law" models and other simplifications of the systems.

Example: a small town may have 1000 citizens, 200 businesses, and perhaps 500 formal and informal groups/organizations. Each of those individuals and organizations has over 1000 'inputs' and 1000 'outputs' - relations with each other and outside entities, that may be considered as economic factors. (Relations may be financial or other.) You have a social network with something like 10^13 relations/interconnections. And that's just a small town or neighborhood.

I'm embarking on a PhD in Econ shortly, after many years in computing, and my math skills are being stretched like they haven't in a long time. Differential equations is a prerequisite for several of the introductory graduate level courses, along with linear algebra and a bunch of statistics and game theory. Thomas Bayes' much appreciated Bayesian Theorem probability is a tool of economists. Vilfredo Pareto (Pareto-optimal" game outcomes) was an economist. Many elements of modern statistics, probability and game theory were developed by economists.

The problem faced by economists has been not that it was too simple, but that the systems under study have been too complex to delve into very deeply until both the mathematical tools and the computational power became available. It was necessary to drastically simplify the models in order to get any sense at all. And, of course, there is a strong philosophical and social-studies thread throughout economics.

Nowadays there is a strong thrust into new approaches to Economics, including complex adaptive systems, agent based systems, Neuroeconomics, Experimental Economics (vis. Vernon Smith, 2002 Bank of Sweden "Nobel" and social network economics.

Often in addition to training and/or experience in biology, physics, systems theory and other disciplines, these approaches require a good understanding of differential equations, comfort in manipulating long chains of partial derivatives, and working with multi-layered irregular networks. Interestingly, even fluid dynamics equations are applicable in some cases.

Although I can see how someone might think the parent post was a troll, it does present a somewhat reasonable strategy, from a game theory point of view, for Cisco ... basically a Grim Trigger strategy. Cisco threatens the Canadian government that they'll pull out of their market entirely if they don't cooperate with them. Cisco doesn't have much to lose, but Canada has a LOT to lose.

I'm quite happy with a tit for tat retaliation.

TFT has been proven a successful strategy too. I use it all the time and it's a very effective natural enforcement of the golden rule (original version, do unto others* etc), good training for the naive. The 'just take it' strategy only encourages the offensive behavior.

[ * that is to say, I DO 'do unto others', but if THEY 'cheat', then I cheat right back untill they 'get it', that cooperation is more profitable overall ]

It isn't just the secular ("post-Christian") world that is abandoning all sense of decency. It's religion too, and in particular Christianity. The religious right is constantly complaining about sex, drugs and violent movies, yet condones corporations that do everything in the name of profit. (Yes, I know that fundamentalists don't represent all Christians, but they do have a very powerful influence on U.S. policy.)

At the core of most religions is some variant of the "Do unto others" Golden Rule. There's a reason for this: it's the best strategy for long-term survival in a society composed of intelligent agents. It emerges naturally through evolution, and is probably hard-coded into our DNA. Only in the corporate world is it routinely ignored.

Reciprocity with no downsides? Hardly. It's simply Pareto optimal, like I...<sigh/> said...

I, frankly, don't have the patience any more for free trade arguments. Free trade is to economists as evolution is to biologists.

By the way, didn't you just restate the same reservations I expressed about free markets?

• the orange grove picker jobs that have been exported to Florida
• the snowmobile rental jobs farmed out to Colorado
• the Chicago tourism jobs exported to Chicago...

Game Theory.

It's a matter of which snitch rolls over first.