21 research outputs found
Evolutionary dynamics on strongly correlated fitness landscapes
We study the evolutionary dynamics of a maladapted population of
self-replicating sequences on strongly correlated fitness landscapes. Each
sequence is assumed to be composed of blocks of equal length and its fitness is
given by a linear combination of four independent block fitnesses. A mutation
affects the fitness contribution of a single block leaving the other blocks
unchanged and hence inducing correlations between the parent and mutant
fitness. On such strongly correlated fitness landscapes, we calculate the
dynamical properties like the number of jumps in the most populated sequence
and the temporal distribution of the last jump which is shown to exhibit a
inverse square dependence as in evolution on uncorrelated fitness landscapes.
We also obtain exact results for the distribution of records and extremes for
correlated random variables
Number of adaptive steps to a local fitness peak
We consider a population of genotype sequences evolving on a rugged fitness
landscape with many local fitness peaks. The population walks uphill until it
encounters a local fitness maximum. We find that the statistical properties of
the walk length depend on whether the underlying fitness distribution has a
finite mean. If the mean is finite, all the walk length cumulants grow with the
sequence length but approach a constant otherwise. Experimental implications of
our analytical results are also discussed
The diffusion of a new service: Combining service consideration and brand choice
We propose an individual-level model of a two-stage service diffusion process. In the first stage, customers decide whether to "consider" joining the service. This (Consideration) stage is modeled by a hazard model. Customers who decide to consider the service move on to the Choice stage, wherein they choose among the service alternatives and an outside No Choice option. This stage is modeled by a conditional Multinomial Logit model. The service provider does not observe the transition in the first stage of potential customers who have yet to choose a brand. Such potential customers may have started to consider joining the service, yet chose the outside alternative in each period thereafter. One of the main contributions of the model is its ability to distinguish between these two non-adopter types. We estimated the model using data on the adoption process of newly introduced service plans offered by a commercial bank. We employed the hierarchical Bayes Monte Carlo Markov Chain procedure to estimate individual as well as population parameters. The empirical results indicate that the model outperforms competing models in breadth of analysis, model fit, and prediction accuracy
Nonlinear deterministic equations in biological evolution
We review models of biological evolution in which the population frequency
changes deterministically with time. If the population is self-replicating,
although the equations for simple prototypes can be linearised, nonlinear
equations arise in many complex situations. For sexual populations, even in the
simplest setting, the equations are necessarily nonlinear due to the mixing of
the parental genetic material. The solutions of such nonlinear equations
display interesting features such as multiple equilibria and phase transitions.
We mainly discuss those models for which an analytical understanding of such
nonlinear equations is available.Comment: Invited review for J. Nonlin. Math. Phy