Structural Drift: The Population Dynamics of Sequential Learning

Details Structural Drift: The Population Dynamics of Sequential Learning Structural Drift: The Population Dynamics of Sequential Learning

2010-05-15

arxiv.org/abs/1005.2714v2

Biology

Quantitative Biology

Populations and Evolution

15 pages, 9 figures; http://csc.ucdavis.edu/~cmg/compmech/pubs/sdrift.htm

Scientific paper

We introduce a theory of sequential causal inference in which learners in a chain estimate a structural model from their upstream teacher and then pass samples from the model to their downstream student. It extends the population dynamics of genetic drift, recasting Kimura's selectively neutral theory as a special case of a generalized drift process using structured populations with memory. We examine the diffusion and fixation properties of several drift processes and propose applications to learning, inference, and evolution. We also demonstrate how the organization of drift process space controls fidelity, facilitates innovations, and leads to information loss in sequential learning with and without memory.

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