2012/5/21 Owen Densmore <[email protected]> > Either of you finish the paper? Comments? > > -- Owen > > No, I can't seem to read anything these days.
But the paper on the neural networks evolving strategies to play the prisoners' dilemma with each other was very much a comment. The fitness of an inherited strategy is defined entirely by the population of strategies it is born into, so you cannot evaluate the fitness of a neural network independent of the environment in which it plays. The environment in which it plays is determined by the fitness of the strategies in play in the last generation of the game and random number generators. The entire system is deterministic, you can integrate from any initial state by running the simulation. You can generate an ensemble of outcomes by varying random number seeds and running simulations. Now, having run as many simulations as your budget allows, what do you know about the laws governing the system? You know that the "organisms" evolve larger neural networks even though size is penalized, and that you don't have sufficient budget to enumerate the possible neural networks, or the possible populations of neural networks, or the possible random number streams mutating the outcomes of generations, or the possible encounter schedules between members of a population (which will matter when the neural nets learn to implement reinforcement learning). Although you know everything about the bits and the deterministic rules that make a particular simulation, you don't know squat about laws that allow you to predict the outcome of the next simulation. Each generation of simulation is a law unto itself. -- rec --
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