> You have people playing these games with voice commands[1] and DDR mats[2] and whatever. If that isn't evidence this isn't a test of dexterity, I don't know what will.
I thought it's precisely the opposite: people find these kind of challenges meaningful because it is a test of dexterity that is particularly difficult to perform with such controllers.
I think you misread the parent comment. They aren't saying that DDR mats and voice commands aren't a test of dexterity. They are saying that people beating the game using DDR mats and voice commands illustrate that the base game isn't a test of dexterity, because people use those weird difficult input methods as a way to add the actual dexterity challenge.
The statement about rising IQ is the Flynn Effect[1]. The fact that IQ has risen over the past 80 years or so is well accepted. Exactly why this is happening is open to debate.
The fourmilab link you mentioned seem to be based on the book "IQ and the Wealth of Nations"[2]. They explicitly normalized all IQ data to have Briton as 100. The author then assumes that IQ hasn't changed any in the past 50 years. The author mentions the Flynn effect and then ignores it. Once you do that, you really can't say anything about how IQ is changing over time. Instead he basically ends up measuring the relative growth rates of countries.
The Daily Mail link has a whole bunch of things, the graph they have of lowering world IQ looks pretty much like the one in the fourmilab article, so I suspect it's based on the same data.
"My major gripe with haskell was that I could never tell the space/time complexity of the my code without serious analysis (that among other things involves second-guessing the compiler's ability to optimize). This makes writing good quality code harder than it needs to be."
This problem is significantly worse in Haskell than in most other languages. Usually you lose at most a constant factor in performance if the compiler doesn't optimize as much as you expected, but the lazy evaluation can actually change the asymptotic memory complexity. I have been bitten by lazy evaluation several times by writing code that I expect to run in constant space and then finding it require linear amount of memory because of the evaluation order.
> It's true, however, that this leads to a very interesting consequence which is not discussed in this context, something quite convoluted: "I will commit to a first-order Markov strategy in order to prevent my opponent from using a higher-order Markov strategy, so that my own analysis of their strategy simplifies." It is a curious statement that your own ignorance forces someone else to be ignorant, which you can then exploit.
This is only true if you know the strategy of the enemy beforehand, though. For instance if you play rock-paper-scissors and decide your move only based on the previous move your enemy can easily exploit that after playing for a while. It is true that the enemy doesn't have to remember more than one move after he learns your strategy but he needs to remember many moves to learn it.
1. No, that statement is still true even if you don't know the higher-order strategy of your opponent: no matter what it is, it has the same payoffs as some lower-order strategy.
2. You would have to define "exploit that," especially with the understanding that this is game theory and probabilistic strategies are certainly encouraged. So for example, you might imagine a genius who can consistently outthink you, knows your entire history and how you like to play Rock-Paper-Scissors and immediately as you throw down Rock, simply is able to guess that this is what you're likely to do, and throws Paper.
You can beat this guy. Or, more precisely, you can equal him. It's very simple: before the day has begun, roll a six-sided die and memorize the sequence. As long as they are not exploiting certain "tells" (as a Japanese robot did in the news a week or two ago) -- as long as they are just making a deduction based upon the sort of person you are, they cannot produce a net win against you and you're safe. Indeed, the Nash equilibrium for RPS is not terribly interesting, it's to choose each of the options with probability 1/3rd -- I don't really have much reason to believe that this changes dramatically in iterated RPS.
1. I agree with what you say. There exists a low-order strategy that has the same payoff against the low-order strategy your opponent uses, and you can use that if you know your opponent's strategy. However, in many games there aren't low-order strategies that would work well against any low-order strategy so you need to know the opponent's strategy to choose a proper low-order strategy. Alternatively you could use a higher order strategy that learns the opponent's strategy and adapts to it.
2. Well, I was thinking that a strategy would be a mapping from game history to a probability distribution over possible moves. Should this be considered in some other way?
Sure, you can just play according to the Nash equilibrium and you can't be exploited, but probably a more interesting case is when both players try to outsmart the other player and not just aim for a draw.
I thought it's precisely the opposite: people find these kind of challenges meaningful because it is a test of dexterity that is particularly difficult to perform with such controllers.