It seems that randomization can serve as a general substitute for memory. It’s not a perfect substitute, but whenever you don’t want to, or can’t, remember something for some reason, randomization might help.
Besides the example of The Absent-Minded Driver, I’ve realized there are other examples in cryptography:
nonces—Many encryption schemes require a unique nonce to be generated for each message to be encrypted. You can either pick random nonces, or keep a counter. But keeping a counter might be too troublesome, and you might be running in a virtual machine that can be rolled back from time to time, so it’s usually better to use a random nonce, even though you’ll need a longer nonce than if you used a counter (to keep the probability of collision sufficiently small).
distributed key search—You can either have a central server that hands out regions of the key space to search, or each participant can search a random region. The latter is less efficient in computing time, but more efficient in communications cost.
I might do a post on this, if I could figure out a way to think about why randomization substitutes for memory.
if I could figure out a way to think about why randomization substitutes for memory.
Let A and B be actions leading to deterministic outcomes, and let C be some lottery between A and B. A rational agent will never prefer both C>A and C>B.
When you repeat the scenario without memory, the lottery is no longer exactly over choices the agent could deterministically make: the randomness is re-rolled in places where the agent doesn’t get another decision. Despite what the options are labeled, you’re really choosing between 2xA, 2xB, and a lottery over {2xA, 2xB, A+B}. Since the lottery contains an outcome that isn’t available to the deterministic decision, it may be preferred.
I think this is equivalent to the role played by observational evidence in UDT1: Observations allow a constant strategy to take different actions in different places, whereas without any observations to distinguish agent instances you have to pick one action to optimize both situations. Of course good evidence is reliably correlated with the environment whereas randomness doesn’t tell you which is which, but it’s better than nothing.
It seems that randomization can serve as a general substitute for memory. It’s not a perfect substitute, but whenever you don’t want to, or can’t, remember something for some reason, randomization might help.
Besides the example of The Absent-Minded Driver, I’ve realized there are other examples in cryptography:
nonces—Many encryption schemes require a unique nonce to be generated for each message to be encrypted. You can either pick random nonces, or keep a counter. But keeping a counter might be too troublesome, and you might be running in a virtual machine that can be rolled back from time to time, so it’s usually better to use a random nonce, even though you’ll need a longer nonce than if you used a counter (to keep the probability of collision sufficiently small).
distributed key search—You can either have a central server that hands out regions of the key space to search, or each participant can search a random region. The latter is less efficient in computing time, but more efficient in communications cost.
I might do a post on this, if I could figure out a way to think about why randomization substitutes for memory.
Let A and B be actions leading to deterministic outcomes, and let C be some lottery between A and B. A rational agent will never prefer both C>A and C>B.
When you repeat the scenario without memory, the lottery is no longer exactly over choices the agent could deterministically make: the randomness is re-rolled in places where the agent doesn’t get another decision. Despite what the options are labeled, you’re really choosing between 2xA, 2xB, and a lottery over {2xA, 2xB, A+B}. Since the lottery contains an outcome that isn’t available to the deterministic decision, it may be preferred.
I think this is equivalent to the role played by observational evidence in UDT1: Observations allow a constant strategy to take different actions in different places, whereas without any observations to distinguish agent instances you have to pick one action to optimize both situations. Of course good evidence is reliably correlated with the environment whereas randomness doesn’t tell you which is which, but it’s better than nothing.
I had some comments in this thread that outline the way that I think about this.