David Chavalarias

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We introduce an automated method for the bottom-up reconstruction of the cognitive evolution of science, based on big-data issued from digital libraries, and modeled as lineage relationships between scientific fields. We refer to these dynamic structures as phylomemetic networks or phylomemies, by analogy with biological evolution; and we show that they(More)
Imitation is fundamental in the understanding of social systems' dynamics. But the diversity of imitation rules employed by modelers proves that the modeling of mimetic processes cannot avoid the traditional problem of endogenization of all the choices, including the one of the mimetic rules. Starting from the remark that metacognition and human reflexive(More)
We develop analytical and computational models to study the conditions for the stability of a population consisting of agents with heterogeneous preferences. The analytical models that utilize an indirect evolutionary approach show that the ability to detect others' types is critical for the evolution of reciprocal preferences. The computational models of(More)
We propose a series of methods to represent the evolution of a field of science at different levels: namely micro, meso and macro levels. We use a previously introduced asymmetric measure of paradigmatic proximity between terms that enable us to extract structure from a large publications database. We apply our set of methods on a case study from the(More)
Accurate and up-to-date knowledge of keywords entered by users who search or provide paedophile content is a key resource for filtering purposes and for monitoring by law enforcement institutions. However, such keywords are often hidden and may change frequently, and our current knowledge about them relies on manual inspection and field expertise. We(More)