I’m still confused. My biology knowledge is probably lacking, so maybe that’s why, but I had a similar thought to dkirmani after reading this: “Why are children born young?” Given that sperm cells are active cells (which should give transposons opportunity to divide), why do they not produce children with larger transposon counts? I would expect whatever sperm divide from to have the same accumulation of transposons that causes problems in the divisions off stem cells.
Unless piRNA and siRNA are 100% at their jobs, and nothing is explicitly removing transposons in sperm/eggs better than in the rest of the body, then surely there should be at least a small amount of accumulation of transposons across generations. Is this something we see?
I vaguely remember that women are born with all the egg cells they’ll have, so, if that’s true, then maybe that offers a partial explanation (only half the child genome should be as infected with transposons?). I’m not sure it holds water, because since egg cells are still alive, even if they aren’t dividing more, they should present opportunities for transposons to multiply.
Another possible explanation I thought of was that, in order to be as close to 100% as possible, piRNA and siRNA work more than normal in the gonads, which does hurt the efficacy of sperm, but because you only need 1 to work, that’s ok. Still, unless it is actually 100%, there should be that generational accumulation.
This isn’t even just about transposons. It feels like any theory of aging would have to contend with why sperm and eggs aren’t old when they make a child, so I’m not sure what I’m missing.
A little late to the party, but
I’m confused about the minimax strategy.
The first thing I was confused about was what sorts of rules could constrain Murphy, based on my actions. For example, in a bit-string environment, the rule “every other bit is a 0” constrains Murphy (he can’t reply with “111...”), but not based on my actions. It doesn’t matter what bits I flip, Murphy can always just reply with the environment that is maximally bad, as long as it has 0s in every other bit. Another example would be if you have the rule “environment must be a valid chess board,” then you can make whatever moves you want, and Murphy can just return the environment with the rule “if you make that move, then the next board state is you in checkmate”, after all, you being in checkmate is a valid chessboard, and therefore meets the only rule you know. And you can’t know what other rules Murphy plays by. You can’t really run minimax on that, then, because all of Murphy’s moves look like “set the state to the worst allowable state.”
So, what kind of rules actually constrain Murphy based on my actions? My first take was “rules involving time,” for instance if you have the rule “only one bit can be flipped per timestep” then you can constrain Murphy. If you flip a bit, then within the next timestep, you’ve eliminated some possibilities (they would require flipping that bit back and doing something else), so you can have a meaningful minimax on which action to take.
This didn’t feel like the whole story though, so I had a talk with my friend about it, and eventually, we generalized it to “rules that consume resources.” An example would be, if you have the rule “for every bit you flip, you must also flip one of the first 4 bits from a 1 to a 0″, then we can constrain Murphy. If I flip any bit, that leaves 1 less bit for Murphy to use to mess with me.
But then the minimax strategy started looking worrying to me. If the only rules that you can use to constrain Murphy are ones that use resources, then wouldn’t a minimax strategy have some positive preference for destroying resources in order to prevent Murphy from using them? It seems like a good way to minimize Murphy’s best outcomes.