Predictability of imitative learning trajectories
Authored by Jose F Fontanari, Paulo R A Campos
Date Published: 2019
DOI: 10.1088/1742-5468/aaf634
Sponsors:
Brazilian National Council for Scientific and Technological Development (CNPq)
São Paulo Research Foundation (FAPESP)
Platforms:
No platforms listed
Model Documentation:
Other Narrative
Mathematical description
Model Code URLs:
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Abstract
The fitness landscape metaphor plays a central role in the modeling of
optimizing principles in many research fields, ranging from evolutionary
biology, where it was first introduced, to management research. Here we
consider the ensemble of trajectories of an imitative learning search,
in which agents exchange information on their fitness and imitate the
fittest agent in the population, with the aim of reaching the global
maximum of the fitness landscape. We assess the degree to which the
start and end points determine the learning trajectories using two
measures, namely the predictability, which yields the probability that
two randomly chosen trajectories are the same, and the mean path
divergence, which gauges the dissimilarity between two learning
trajectories. We find that the predictability is greater in rugged
landscapes than in smooth ones. The mean path divergence, however, is
strongly affected by the search parameters-population size and imitation
propensity-that obliterate the influence of the underlying landscape.
The learning trajectories become more deterministic, in the sense that
there are fewer distinct trajectories and those trajectories are more
similar to each other, with increasing population size and imitation
propensity. In addition, we find that the roughness of the learning
trajectories, which measures the deviation from additivity of the
fitness function, is always greater than the roughness estimated over
the entire fitness landscape.
Tags
Agent-based models
Evolution
Adaptive walks
Evolutionary
processes
Evolution models
Stochastic search
Fitness landscapes