We present a method for calculating the ``noise sensitivity signature'' of a learning algorithm which is based on scrambling the output of classes of various fractions of the training data. This signature can be used to indicate a good (or bad) match between the complexity of the classifier and...

This paper analyzes the evolutionary dynamics of a modified version of Rosenthal's ``centipede,'' whereby the players' strategies are represented by finite-state automata. In the framework considered, the automata required to implement different strategies may differ among them both with respect...

Learning is frequently proposed as a mechanism that enables an individual to adapt to changes in the environment during its lifetime (Levins, 1968; Stephens, 1991). These studies generally take as their point of departure the Òabsolute fixity argument.Ó That is, in an absolutely fixed environment,...

Swarm is a simulation environment which facilitates development and experimentation with simulations involving a large number of agents behaving and interacting within a dynamic environment. Many of the computer simulations performed by researchers in various disciplines rest upon a very similar...

We propose an extension to multivariate time series of the phase-randomized Fourier-transform algorithm for generating surrogate data. Such surrogate data sets must mimic not only the autoncorrelations of each of the variables in the original data set, they must minic the cross-correlations...

We exhibit a quantum circuit that performs the Quantum Fourier Transform on $n$ qubits in $ (n)$ depth. Thus, a parallel quantum computer can carry out the QFT in linear time. We conjecture that this can, in fact, be reduced to $ ({\rm log} n)$ depth, which would place the QFT in the class {\bf...

Success and efficiency of Darwinian evolution is based on the dichotomy of genotype and phenotype: The former is the object under variation whereas the latter constitutes the target of selection. Genotype-phenotype relations are highly complex and hence variation and selection appear...

An agent-based computational model of Long House Valley, in northern Arizona near Monument Valley, is described and demontrated. The model, that runs from about AD 400 to 1400, consists of artificial adaptive agents (households) who inhabit a digitized version of the Long House Valley landscape....