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Structural drift: the population dynamics of sequential learning.

Crutchfield JP, Whalen S - PLoS Comput. Biol. (2012)

Bottom Line: 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 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.

View Article: PubMed Central - PubMed

Affiliation: Complexity Sciences Center, Physics Department, University of California Davis, Davis, California, United States of America. chaos@ucdavis.edu

ABSTRACT
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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Time to stasis as a function of initial Pr[Heads] for structural drift (SD) of the Biased Coin Process versus Monte Carlo (MC) simulation of Kimura's model.Kimura's predicted times to fixation and deletion are shown for reference. Each estimated time is averaged over  realizations with sample size .
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pcbi-1002510-g007: Time to stasis as a function of initial Pr[Heads] for structural drift (SD) of the Biased Coin Process versus Monte Carlo (MC) simulation of Kimura's model.Kimura's predicted times to fixation and deletion are shown for reference. Each estimated time is averaged over realizations with sample size .

Mentions: The time to stasis of the Biased Coin Process as a function of initial [Heads] was shown in Figure 7. Also shown there was the previous Monte Carlo Kimura drift simulation modified to terminate when either fixation or deletion occurs. This experiment illustrates the definition of structural stasis and allows direct comparison of structural drift with genetic drift in the memoryless case.


Structural drift: the population dynamics of sequential learning.

Crutchfield JP, Whalen S - PLoS Comput. Biol. (2012)

Time to stasis as a function of initial Pr[Heads] for structural drift (SD) of the Biased Coin Process versus Monte Carlo (MC) simulation of Kimura's model.Kimura's predicted times to fixation and deletion are shown for reference. Each estimated time is averaged over  realizations with sample size .
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3369870&req=5

pcbi-1002510-g007: Time to stasis as a function of initial Pr[Heads] for structural drift (SD) of the Biased Coin Process versus Monte Carlo (MC) simulation of Kimura's model.Kimura's predicted times to fixation and deletion are shown for reference. Each estimated time is averaged over realizations with sample size .
Mentions: The time to stasis of the Biased Coin Process as a function of initial [Heads] was shown in Figure 7. Also shown there was the previous Monte Carlo Kimura drift simulation modified to terminate when either fixation or deletion occurs. This experiment illustrates the definition of structural stasis and allows direct comparison of structural drift with genetic drift in the memoryless case.

Bottom Line: 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 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.

View Article: PubMed Central - PubMed

Affiliation: Complexity Sciences Center, Physics Department, University of California Davis, Davis, California, United States of America. chaos@ucdavis.edu

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus