In a mathematical model of tagmosis, lineages that reach optimal fitness via indirect paths spend longer under selection and evolve higher syntactic complexity than those taking the direct path.
Strong approximation of particular one-dimensional diffusions
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abstract
This paper develops a new technique for the path approximation of one-dimensional stochastic processes, more precisely the Brownian motion and families of stochastic differential equations sharply linked to the Brownian motion (usually known as L and G-classes). We are interested here in the $\epsilon$-strong approximation. We propose an explicit and easy to implement procedure that constructs jointly, the sequences of exit times and corresponding exit positions of some well chosen domains. The main results control the number of steps to cover a fixed time interval and the convergence theorems for our scheme. We combine results on Brownian exit times from time-depending domains (one-dimensional heat balls) and classical renewal theory. Numerical examples and issues are also described in order to complete the theoretical results.
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Evolutionary path dependence of semantic complexity
In a mathematical model of tagmosis, lineages that reach optimal fitness via indirect paths spend longer under selection and evolve higher syntactic complexity than those taking the direct path.