Glen, Yes, that is the kind of weighted ensemble I am thinking about. I see the algorithm as weighing the novel trajectories more heavily in the early stages of a search, but ultimately giving the *correct* stationary distribution in the limit. My list was a combination of references, dreams, and starting points for a conversation, so thank you for biting.
>* 2. An alternative (bin-based sampling) to globally defined "fitness" >measures in evolutionary modeling. * In one paper by Aristoff and Zuckerman, "Optimizing Weighted Ensemble Sampling of Steady States"[1], the authors mention in passing: *We emphasize that weighted ensemble is different from most sequential Monte Carlo methods, as it relies on a bin-based resampling mechanism rather than a globally defined fitness function like a Gibbs-Boltzmann potential.* This caught my attention for a number of reasons. First, it got me thinking about a trade off between classifying (whether apriori or adaptive bining) and the globally defined potentials made popular by physics and saught after by the *fairer* sciences. To the extent that bining is an alternative implementation of the same underlying algebra provided by potentials, I got to wondering how I can better see this. Second, I got to thinking about bining as a kind of niche and how this approach might correspond nicely to *Natural Design* a'la Thompson and friends. >* 3. An application of diffusion-limited aggregation to general search >(especially in the face of limited resources)* So far, here in my early investigations, it appears that questions of WE optimization and the proving of its low-hanging theorems seem to rely on the Hill relation[2] and interpreting the process as diffusion. Here I found visualization of the algorithm at work is illustrative, the process appears reminescent of watching goatheads[3] perform a search across my yard (perhaps a kind of diffusion limited aggreation, DLA, with damping?) Interpreted as a DLA, I can see *selection* at the bining step as a kind of "collision" mechanism, causing the DLA to branch. This seems interesting to me because it helps me to see how one can use a mixuture of maximally-stateful computations (DLA) and fairly straightforward probabilistic thinking to perform novel searches. Here, rather than trying to always find the shorted path or better approximate the mean, WE manages to find novelty and rare events. idk, it seems promising. Not mentioned in my previous post, though also of interest, is that typically weighted ensemble assumes explicit underlying dynamics (Langevin). But it seems like it should be applicable to networks of hyperlinks as well. I am thinking of the xkcd comic[4] about philosophy... Cheers, JZ [1] https://arxiv.org/pdf/1806.00860.pdf [2] http://statisticalbiophysicsblog.org/?p=8 [3] https://www.google.com/search?q=goathead+plant&oq=goathead+plant&aqs=chrome..69i57.2206j0j7&sourceid=chrome&ie=UTF-8 [4] https://xkcd.com/903/ ps. Since leaving nabble, I haven't figured out how to properly sub-thread on redfish.com. Any advice/resources are welcome. Also, if someone out there who can do something is listening, I would like to be added back onto the emails.
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