Bryan A. Knowles | Searcharxiv

arXiv2015

Proposed Approximate Dynamic Programming for Pathfinding under Visible Uncertainty

Continuing our preleminary work \cite{knowles14}, we define the safest-with-sight pathfinding problems and explore its solution using techniques borrowed from measure-theoretic probability theory. We find a simple recursive definition for the probability that an ideal pathfinder will select an edge in a given scenario of an uncertain network where edges have probabilities of failure and vertices provide "vision" of edges via lines-of-sight. We propose an approximate solution based on our theoretical findings that would borrow techniques from approximate dynamic programming.

cs.DS↗

arXiv2015

Initial Analysis of a Simple Numerical Model that Exhibits Antifragile Behavior

I present a simple numerical model based on iteratively updating subgroups of a population, individually modeled by nonnegative real numbers, by a constant decay factor; however, at each iteration, one group is selected to instead be updated by a constant growth factor. I discover a relationship between these variables and their respective probabilities for a given subgroup, summarized as the variable $c$. When $c>1$, the subgroup is found to tend towards behaviors reminiscent of antifragility; when at least one subgroup of the population has $c\ge1$, the population as a whole tends towards significantly higher probabilities of "living forever," although it may first suffer a drop in population size as less robust, fragile subgroups "die off." In concluding, I discuss the limitations and ethics of such a model, notably the implications of when an upper limit is placed on the growth constant, requiring a population to facilitate an increase in the decay factor to lessen the impact of periods of failure.

q-bio.PE↗