Models, Simulations, and Experiments
Francesco Guala - Models, Simulations, and Experiments
My thoughts:
* In mathematics, simulations and experiments are the same thing.
* As an AI/Cogsci person, I would like to see a simulation in which resource-bounded agents each make a simulation of their world. Looking at their performance might shed some light on how *we* should make our simulations in the real world (this may be especially true, if you believe that we are living in a simulation). Even if the lesson is too hard for us to understand, e.g. imagine that one of the simulated agents came up with crazy feature selection algorithm, maybe using neural networks (or some other algorithm that is a blackbox to us). We might still benefit from copying their algorithm and using it in the real world... especially if we try to make sure that the reason it works is not because it exploits artifacts of the simulation (one way of doing this is to make sure that the algorithm is robust across different simulations, written by different people).
I'm reminded of this idea:
* Debugging is like the scientific method: you combine theory (reasoning about programs) and experiment (testing). The difference is that debugging is easier:
** computer programs are known to be deterministic, and we can control initial conditions.
** closed world: when debugging, there is a bounded number of things that could be causing the undesired behavior. The evil genie of worst-case can only be so evil.
My thoughts:
* In mathematics, simulations and experiments are the same thing.
* As an AI/Cogsci person, I would like to see a simulation in which resource-bounded agents each make a simulation of their world. Looking at their performance might shed some light on how *we* should make our simulations in the real world (this may be especially true, if you believe that we are living in a simulation). Even if the lesson is too hard for us to understand, e.g. imagine that one of the simulated agents came up with crazy feature selection algorithm, maybe using neural networks (or some other algorithm that is a blackbox to us). We might still benefit from copying their algorithm and using it in the real world... especially if we try to make sure that the reason it works is not because it exploits artifacts of the simulation (one way of doing this is to make sure that the algorithm is robust across different simulations, written by different people).
I'm reminded of this idea:
* Debugging is like the scientific method: you combine theory (reasoning about programs) and experiment (testing). The difference is that debugging is easier:
** computer programs are known to be deterministic, and we can control initial conditions.
** closed world: when debugging, there is a bounded number of things that could be causing the undesired behavior. The evil genie of worst-case can only be so evil.